Oracle 1z0-1110-25 Dumps

Detailed Answer in Step-by-Step Solution:

Objective: Identify three required params for an OCI Data Flow app config.

Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.

Evaluate Options:

A: Archive path—Optional if script is in Object Storage—incorrect.

B: Local script path—Not needed; script is uploaded—incorrect.

C: Compartment—Required for resource scope—correct.

D: Bucket—Required for script storage/access—correct.

E: Display name—Required for app identification—correct.

Reasoning: C, D, E are mandatory metadata for Data Flow creation—script location is specified via bucket.

Conclusion: C, D, E are correct.

OCI documentation states: “To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object.” Local paths (B) or archives (A) are optional or handled separately—only C, D, E are required per OCI’s Data Flow API spec.

Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".

Detailed Answer in Step-by-Step Solution:

Objective: Optimize compute shape for cost and time.

Evaluate Options:

A: Tuning params—Focuses on model, not shape.

B: Strongest shape—Costly, unbalanced.

C: Scale up when utilized—Balances cost/time—correct.

D: Random start—Unsystematic.

Reasoning: C iteratively optimizes based on utilization.

Conclusion: C is correct.

OCI documentation advises: “Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes—optimizes cost and speed.” A misfocuses, B overspends, D lacks method—only C aligns.

Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".

Detailed Answer in Step-by-Step Solution:

Objective: Identify interfaces to save a 2 GB model to the Model Catalog.

Evaluate Options:

A: OCI CLI—Supports Data Science tasks—possible but not primary.

B: ADS SDK—Designed for model catalog ops—correct.

C: ODSC CLI—Not standard; likely typo for OCI CLI.

D: Console—GUI for catalog uploads—correct.

E: OCI Python SDK—Programmatic catalog access—correct.

F: Git CLI—Version control, not catalog-related.

Reasoning: B, D, E are OCI’s primary interfaces; A is valid but less emphasized.

Conclusion: B, D, E are correct (A plausible but not top-tier).

OCI documentation lists “ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog.” OCI CLI (A) works but isn’t highlighted, C isn’t real, and F is unrelated—B, D, E are the standard trio.

Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".

Detailed Answer in Step-by-Step Solution:

Objective: Identify IAM components in OCI.

Understand IAM: Manages access via policies, groups, etc.

Evaluate Options:

A: Policies—Core IAM component—correct.

B: Subnets—Networking, not IAM.

C: Instances—Compute, not IAM.

D: VCN—Networking, not IAM.

Reasoning: Only A is an IAM element—others are resources.

Conclusion: A is correct.

OCI documentation states: “IAM includes components like policies (A), groups, and compartments to control resource access.” B, C, and D are infrastructure, not IAM—only A fits per OCI’s IAM framework.

Oracle Cloud Infrastructure IAM Documentation, "IAM Components".

Detailed Answer in Step-by-Step Solution:

Objective: Assess required skills for data scientists.

Analyze Skills:

Technical: Coding, stats, ML.

Nontechnical: Communication, business acumen.

Traits: Curiosity, problem-solving.

Reasoning: Success requires this mix—e.g., explaining models to stakeholders.

Conclusion: A (True) is correct.

OCI documentation states: “Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking.” This holistic requirement is true (A), not false (B).

Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".

Detailed Answer in Step-by-Step Solution:

Understand OCI Data Labeling Capabilities: OCI Data Labeling is designed to annotate data for machine learning, supporting specific file types like images, text documents, and videos.

Evaluate Options:

A. Video footage: Supported for tasks like object detection or action recognition.

B. Images: Supported for image classification, object detection, etc.

C. Typewritten document: Supported as text data for tasks like entity extraction or classification.

D. Purchase orders: While potentially text-based, this is ambiguous without format clarification (e.g., PDF, image). OCI supports text annotation, but “purchase orders” isn’t a specific file type—it’s assumed as text here.

E. Audio recording: Not supported, as OCI Data Labeling focuses on visual and textual data, not audio.

Select Three: A (video), B (images), and C (text documents) are explicitly supported file types.

OCI Data Labeling supports annotating datasets of images, text, and videos, as per the official documentation. Video footage (A) can be annotated for tasks like object tracking, images (B) for classification or detection, and typewritten documents (C) for text-based annotations (e.g., named entity recognition). Audio files (E) are not supported, and while purchase orders (D) could be text, the question specifies “typewritten document” as a clearer match. (Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Supported Data Types").

Detailed Answer in Step-by-Step Solution:

Objective: Classify a PagerDuty notification in OCI Monitoring.

Understand OCI Monitoring: Involves events, rules, and actions for notifications.

Evaluate Options:

A: Action—executes a response (e.g., notify PagerDuty) when triggered—correct.

B: Rule—defines conditions for triggering actions—precedes the action.

C: Function—serverless code, not directly tied to notifications.

D: Event—state change triggering a rule, not the notification itself.

Reasoning: The notification is the action taken after an event/rule—fits A.

Conclusion: A is correct.

OCI documentation states: “Actions in the Monitoring service execute responses, such as sending notifications to PagerDuty, when a rule’s condition is met based on an event.” Rules (B) set conditions, Functions (C) are unrelated, and Events (D) are triggers—only Action (A) describes the notification step.

Oracle Cloud Infrastructure Monitoring Documentation, "Actions Overview".

Detailed Answer in Step-by-Step Solution:

Define Git: Git is a version control system—centralized vs. distributed is key.

Evaluate Options:

A: Incorrect—Git is distributed, not centralized (e.g., SVN is centralized).

B: Correct—Distributed, tracks file changes across local and remote repos.

C: Incorrect—Git allows simultaneous contributions; it manages, not prevents, merges.

D: Incorrect—Centralized is wrong, and “copious data” is vague.

Reasoning: Git’s distributed nature (each user has a full repo copy) and change-tracking are core traits.

Conclusion: B is accurate.

OCI documentation aligns with Git’s official definition: “Git is a distributed version control system that tracks changes to files, enabling collaboration and version history management.” A and D misclassify it as centralized, while C misrepresents merge handling—B captures Git’s essence as used in OCI Data Science.

Oracle Cloud Infrastructure Code Repository Documentation, "Git Overview".

Detailed Answer in Step-by-Step Solution:

Analyze Git Operations: Identify which depend on internet speed.

Evaluate Options:

A. Staging (git add): Local operation—adds files to the index; no network involved.

B. Updating local repo (git pull): Downloads remote changes—requires internet, slowed by poor connectivity.

C. Pushing changes (git push): Uploads local commits to remote—network-dependent, delayed by slow speed.

D. Committing (git commit): Local snapshot—no network needed.

E. Converting to Git repo (git init): Local initialization—no internet required.

Reasoning: Only B and C involve network transfers, directly impacted by slow internet.

Conclusion: B and C are the correct choices.

Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as “bandwidth-sensitive” in OCI’s guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user’s machine, unaffected by internet speed. This matches standard Git behavior and OCI’s implementation.

Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Identify export formats for OCI Data Labeling.

Understand Export: Annotated data is exported for ML use.

Evaluate Options:

A: CONLL V2003—Text-specific, not OCI-supported.

B: COCO—Standard for image annotations—correct.

C: Proprietary JSON—OCI’s default format—correct.

D: Spacy—NLP format, not supported.

Reasoning: B and C are image-compatible per OCI docs.

Conclusion: B and C are correct.

OCI documentation states: “Data Labeling exports annotations in COCO format (B) for image tasks and a proprietary JSON format (C) specific to the service.” CONLL (A) and Spacy (D) are text/NLP-focused—not supported for OCI’s image annotations.

Oracle Cloud Infrastructure Data Labeling Documentation, "Export Formats".

Detailed Answer in Step-by-Step Solution:

Define OCI Secrets: Secrets are sensitive data (e.g., API keys, passwords) managed securely in OCI.

Evaluate Options:

A: Object Storage is for general data, not secure secret management.

B: Vault is OCI’s service for storing and managing secrets securely.

C: Autonomous Data Warehouse is for analytics, not secret storage.

D: Oracle Databases store data, not OCI-specific secrets.

Reasoning: Vault is purpose-built for secrets with encryption and access control.

Conclusion: B is correct.

OCI Vault “provides a secure, centralized service to store and manage secrets, such as passwords and keys, with encryption at rest and fine-grained access policies.” Object Storage (A), Autonomous Data Warehouse (C), and Oracle Databases (D) serve other purposes—only Vault (B) is designed for secrets per OCI’s security architecture.

Oracle Cloud Infrastructure Vault Documentation, "Secrets Management".

Detailed Answer in Step-by-Step Solution:

Objective: Run a complex Python project as an OCI Job.

Evaluate Options:

A: Auto-identification—False; entrypoint must be set.

B: Rewrite—Unnecessary, inefficient.

C: Auto-executable—False; needs explicit entrypoint.

D: ZIP with entrypoint—Correct, flexible approach.

Reasoning: D preserves structure, specifies execution.

Conclusion: D is correct.

OCI documentation states: “For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py).” Auto-detection (A, C) isn’t supported, and B discards structure—D is best.

Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".

Detailed Answer in Step-by-Step Solution:

Objective: Transcribe Spanish video audio and identify profanity.

Evaluate Options:

A: Anomaly Detection—Not for transcription or text analysis.

B: Speech—Converts audio to text (e.g., Spanish), base for further analysis—correct.

C: Translation—Translates text, not transcription.

D: Language—Analyzes text (e.g., profanity), but needs transcribed input.

Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.

Conclusion: B is correct for transcription.

OCI Speech “transcribes audio from video or audio files into text, supporting languages like Spanish.” Post-transcription, OCI Language could detect profanity, but B is the starting point—Anomaly (A) and Translation (C) don’t fit.

Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".

Detailed Answer in Step-by-Step Solution:

Objective: Define data science’s main goal.

Evaluate Options:

A: Archiving—Not the focus; too narrow.

B: Analyze for insights/business value—Core purpose—correct.

C: Prep for analytics—Means, not the end goal.

D: Output-focused—Vague, incomplete.

Reasoning: B captures the actionable insight generation central to data science.

Conclusion: B is correct.

OCI documentation defines data science as “mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value.” A is storage-focused, C is preparatory, and D is unclear—only B reflects the principal goal per OCI’s mission.

Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".

Detailed Answer in Step-by-Step Solution:

Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform’s storage system.

Data Exploration: Once data is accessed, it’s explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.

Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.

Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.

Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.

Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.

The correct order reflects the machine learning lifecycle as outlined in Oracle’s OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK’s correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle’s recommended practices for building ML models in OCI Data Science (Reference: Oracle Cloud Infrastructure Data Science Service Documentation, "Machine Learning Lifecycle").

Detailed Answer in Step-by-Step Solution:

Objective: Locate sources for ADS SDK in OCI.

Understand ADS SDK: A Python library for Data Science tasks (e.g., EDA).

Evaluate Options:

A: Big Data Service—Spark-focused, not ADS source.

B: Machine Learning—Separate service, not ADS-related.

C: Conda in OCI Data Science—Preinstalled ADS in notebook sessions.

D: PyPI—Public source to install ADS (pip install oracle-ads).

E: ADW—Database, not an SDK source.

Reasoning: C (preinstalled) and D (installable) are practical access points.

Conclusion: C and D are correct.

OCI documentation states: “The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle-ads.” Big Data (A), Machine Learning (B), and ADW (E) don’t host ADS—only C and D apply.

Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".

Detailed Answer in Step-by-Step Solution:

Understand OCI Data Science Jobs: This service automates ML tasks (e.g., training, evaluation) with configurable, repeatable executions.

Key Characteristics: Jobs run on OCI’s infrastructure, managed by Oracle, not the customer or third parties, and are specific to Data Science, not general DevOps.

Evaluate Options:

A: Correct—Jobs are defined by users (e.g., via scripts) and executed on OCI’s fully managed compute resources.

B: Incorrect—Infrastructure is managed by OCI, not the customer.

C: Incorrect—No third-party cloud integration; it’s OCI-specific.

D: Incorrect—Jobs are for Data Science tasks (e.g., ML training), not all DevOps workloads (e.g., CI/CD pipelines).

Reasoning: “Fully managed” means OCI handles provisioning and scaling, aligning with A.

Conclusion: A accurately reflects the service’s purpose and operation.

OCI Data Science Jobs “allow users to define and execute repeatable machine learning tasks, such as model training or batch processing, on fully managed OCI infrastructure.” This eliminates customer management (B), third-party clouds (C), or broad DevOps scope (D). The documentation emphasizes automation and management by OCI, making A the precise description.

Oracle Cloud Infrastructure Data Science Documentation, "Overview of Jobs" section.

Detailed Answer in Step-by-Step Solution:

Objective: Identify the mechanism for sharing and reloading ML models in OCI Data Science.

Evaluate Options:

A. Model provenance: Tracks model origin—informative but not a sharing mechanism.

B. Model taxonomy: Categorizes models (e.g., regression)—not for sharing/loading.

C. Model deployment: Makes models accessible as endpoints, not for notebook reloading.

D. Model catalog: Stores models and artifacts, enabling sharing and loading into sessions.

Reasoning: The Model Catalog is OCI’s centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK).

Conclusion: D is the correct tool.

The OCI Model Catalog “enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation.” Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose.

Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the architecture with “never trust, always verify.”

Evaluate Options:

A: Federated identity—Shares auth, not verification-focused.

B: Zero trust—Explicitly “never trust, always verify”—correct.

C: Fluid perimeter—Adaptive, not the core principle.

D: Defense in depth—Layered, not verification-centric.

Reasoning: Zero trust matches the stated principle exactly.

Conclusion: B is correct.

OCI documentation states: “Zero trust (B) architecture operates on ‘never trust, always verify,’ requiring continuous authentication and authorization.” A, C, and D have different focuses—only B aligns with OCI’s security philosophy.

Oracle Cloud Infrastructure Security Documentation, "Zero Trust Architecture".

Detailed Answer in Step-by-Step Solution:

Objective: Locate artifacts for ADS model deployment.

Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI.

Evaluate Options:

A: Vault—Stores secrets, not models.

B: Depository—Not an OCI term.

C: Model Catalog—Stores models/artifacts for deployment—correct.

D: Artifactory—Not an OCI service.

Reasoning: Model Catalog is OCI’s model repository for ADS.

Conclusion: C is correct.

OCI documentation states: “ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored.” Vault (A) is for secrets, B and D aren’t real—only C supports ADS deployment.

Oracle Cloud Infrastructure Data Science Documentation, "ADS Model Deployment".

Detailed Answer in Step-by-Step Solution:

Objective: Select an OCI AI service for text and table extraction from documents.

Evaluate Options:

A: Language—Text analysis, not extraction—incorrect.

B: Digital Assistant—Chatbots, not document tasks—incorrect.

C: Speech—Audio transcription, not documents—incorrect.

D: Vision—OCR for text/tables—correct.

Reasoning: Vision’s OCR extracts text and tables from document images.

Conclusion: D is correct.

OCI documentation states: “OCI Vision (D) uses OCR to extract text and tables from documents, supporting document analysis tasks.” A analyzes text post-extraction, B and C are unrelated—only D fits per OCI’s AI services.

Oracle Cloud Infrastructure Vision Documentation, "Document Analysis Features".

Detailed Answer in Step-by-Step Solution:

Objective: Visualize correlation between continuous and categorical features.

Evaluate Options:

A: Pearson—Continuous vs. continuous—incorrect.

B: Cramer’s V—Categorical vs. categorical—incorrect.

C: Correlation ratio—Continuous vs. categorical—correct.

D: General correlation—Not specific to mixed types.

Reasoning: Correlation ratio handles mixed feature types for heatmaps.

Conclusion: C is correct.

OCI documentation states: “correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps.” Pearson (A) and Cramer’s (B) are type-specific, corr() (D) is broad—only C fits per ADS capabilities.

Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".

Detailed Answer in Step-by-Step Solution:

Objective: Identify custom model types for OCI Language service.

Understand OCI Language: Focuses on text analysis, not images.

Evaluate Options:

A: Image classification—Incorrect; Language is text-based.

B: Text classification, NER—Both text tasks—correct.

C: Sentiment—Pretrained, not custom; NER ok—incorrect.

D: Object detection—Image-based, incorrect.

Reasoning: B aligns with OCI Language’s custom text capabilities.

Conclusion: B is correct.

OCI documentation states: “OCI Language supports training custom models for text classification and Named Entity Recognition (NER) (B) using user data.” A and D involve images, C includes pretrained sentiment—only B matches OCI Language’s custom model scope.

Oracle Cloud Infrastructure Language Documentation, "Custom Model Types".

Detailed Answer in Step-by-Step Solution:

Objective: Find a true statement about ML models.

Evaluate Options:

A: True—Data drift (changes in data distribution) degrades performance over time.

B: False—Static predictions don’t improve without retraining.

C: False—Models need updates as data changes, unlike static software.

D: False—Even high-quality models require retraining with new data.

Reasoning: A reflects the reality of data drift, a common ML challenge.

Conclusion: A is correct.

OCI documentation notes: “Model performance can degrade over time due to data drift, where the underlying data distribution changes, necessitating monitoring and retraining.” B, C, and D contradict this—static predictions don’t improve (B), models aren’t static (C), and retraining is needed (D). A is the accurate aspect.

Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Drift".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the required configuration for OCI Logging Analytics to collect logs from an instance.

Understand Logging Analytics: Collects and analyzes logs from OCI resources via Management Agents.

Key Concepts:

Entity: Represents the instance (e.g., Linux VM).

Source: Defines log locations (e.g., file paths).

Log Group: Organizes logs for analysis.

Evaluate Options:

A: Log-Log Group—Groups logs, not collection setup.

B: Entity-Log—Links instance to logs, but not source-specific.

C: Source-Entity—Maps log sources to the instance—correct.

D: Log Group-Source—Post-collection organization, not ingestion.

Reasoning: C establishes the link between the instance and its log sources—key for ingestion.

Conclusion: C is correct.

OCI documentation states: “To collect logs using Logging Analytics, configure a Source-Entity Association (C) to link the Management Agent on the instance (entity) to specific log sources (e.g., file paths).” A and D organize logs post-collection, B is less specific—only C is required for ingestion per OCI’s Logging Analytics setup.

Oracle Cloud Infrastructure Logging Analytics Documentation, "Configuring Log Collection".

Detailed Answer in Step-by-Step Solution:

Understand OCI IAM Policies: Policies control access using verbs like “inspect,” “read,” “use,” and “manage.”

Define Requirement: Consultants need view-only access without modification rights.

Evaluate Verbs:

A. Use: Allows interaction (e.g., running jobs)—too permissive.

B. Inspect: Grants view-only access to resource details—matches requirement.

C. Manage: Full control (create, update, delete)—too permissive.

D. Read: Includes viewing content (e.g., data), not just metadata—slightly broader than needed.

Conclusion: “Inspect” (B) is the precise verb for view-only access to resource details.

In OCI Identity and Access Management (IAM), the “inspect” verb allows listing and viewing resource metadata without granting modification or data access rights, ideal for this scenario. This is confirmed in the IAM policy reference. (Reference: Oracle Cloud Infrastructure Documentation, "IAM Policy Verbs").

Detailed Answer in Step-by-Step Solution:

Objective: Find a true statement about OCI Anomaly Detection.

Understand Service: Detects anomalies in multivariate data (e.g., time series).

Evaluate Options:

A: False—Accepted types are CSV/JSON, not SQL/Python.

B: Partially true—Focuses on numerical data (e.g., sensors), not text broadly.

C: True—Used for fraud, intrusions, and sensor anomalies (key use cases).

D: False—Trained on customer data only, not general datasets.

Reasoning: C aligns with documented applications; others misalign.

Conclusion: C is correct.

OCI Anomaly Detection documentation states: “The service is designed to detect anomalies in time series data, making it valuable for fraud detection, network intrusion analysis, and sensor discrepancies.” A is incorrect (file formats), B overgeneralizes (numerical focus), and D misstates training data—only C matches the service’s purpose.

Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".

Detailed Answer in Step-by-Step Solution:

Step 1: Install, configure, and authenticate Git: Git must be installed and configured (e.g., git config --global user.name) before any repository actions.

Step 2: Configure SSH keys: SSH keys are set up (e.g., ssh-keygen) and added to the remote service (e.g., GitHub, OCI Code Repository) for secure access.

Step 3: Create local and remote Git repository: Initialize a local repo (git init) and create/link a remote repo (e.g., git remote add origin).

Step 4: Commit files: Add files (git add .) and commit them locally (git commit -m "message").

Step 5: Push to remote: Push local commits to the remote repo (git push origin main).

Evaluate Options: Only D (1, 2, 3, 4, 5) follows this logical sequence; others (e.g., A starts with SSH before Git installation) are illogical.

The standard Git workflow in OCI Data Science or general practice begins with installing Git (1), configuring SSH for secure access (2), creating repositories (3), committing locally (4), and pushing remotely (5). The OCI Code Repository documentation aligns with this: “First, install Git and configure authentication (e.g., SSH), then set up repositories and manage code.” D is the only option reflecting this industry-standard process.

Oracle Cloud Infrastructure Code Repository Documentation, "Git Workflow Basics".

Detailed Answer in Step-by-Step Solution:

Objective: Understand the effect of False Alarm Probability (FAP) in OCI Anomaly Detection.

Understand FAP: Controls false positive rate—threshold for anomaly flagging.

Evaluate Options:

A: Disable reporting—Incorrect; FAP sets sensitivity, not on/off.

B: Changes sensitivity—Correct; lower FAP = fewer false positives—correct.

C: Count-based error—Incorrect; not a counter.

D: Score per signal—Incorrect; FAP is a global setting.

Reasoning: FAP adjusts detection threshold—direct impact on sensitivity.

Conclusion: B is correct.

OCI documentation states: “False Alarm Probability (FAP) (B) adjusts the model’s sensitivity in Anomaly Detection—lower values increase specificity, reducing false positives.” A, C, and D misinterpret FAP’s role—only B aligns with OCI’s anomaly detection tuning.

Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Settings".

Detailed Answer in Step-by-Step Solution:

Objective: Identify true statements about published conda environments in OCI.

Understand Published Conda: Custom envs shared via Object Storage.

Evaluate Options:

A: False—Users create them, not OCI-curated.

B: True—odsc conda init sets up access to published envs.

C: False—Object Storage, not notebook, is the source.

D: False—Can create from scratch, not just modifications.

E: True—Conda env vars extend job configs.

Reasoning: B configures access, E enhances flexibility—match OCI behavior.

Conclusion: B and E are correct.

OCI documentation states: “odsc conda init (B) configures the bucket for published conda environments, and Jobs can use conda environment variables (E) alongside service vars.” A misattributes curation, C misplaces source, and D limits creation—only B and E are true per OCI’s conda management.

Oracle Cloud Infrastructure Data Science Documentation, "Published Conda Environments".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the ADS SDK class for visualizing model performance comparison.

Understand ADS Classes: Each serves a specific ML purpose—visualization requires evaluation tools.

Evaluate Options:

A. EvaluationMetrics: Likely a typo—meant EvaluationsMetrics? Not a standalone class for visualization.

B. ADSEvaluator: Designed to evaluate and visualize model performance (e.g., ROC curves)—correct.

C. ADSExplainer: Explains model predictions (e.g., SHAP), not comparative visualization.

D. ADSTuner: Tunes hyperparameters, not for visualization.

Reasoning: ADSEvaluator provides comparative plots (e.g., precision-recall) for multiple models, including baselines.

Conclusion: B is correct.

OCI documentation states: “The ADSEvaluator class in ADS SDK (B) enables visualization of model performance metrics, such as ROC curves and confusion matrices, for multiple models on a test set, including baselines.” EvaluationMetrics (A) isn’t a class, ADSExplainer (C) focuses on interpretability, and ADSTuner (D) is for tuning—only B fits the visualization need per OCI’s ADS toolkit.

Oracle Cloud Infrastructure ADS SDK Documentation, "ADSEvaluator Class".

Detailed Answer in Step-by-Step Solution:

Objective: Convert fixed to flexible load balancer in OCI.

Understand Load Balancers: Fixed (e.g., 10 Mbps) vs. flexible (dynamic shapes).

Evaluate Options:

A: False—Conversion possible via recreation.

B: Update Shape—For flexible only, not conversion.

C: Delete and recreate—Standard method—correct.

D: Edit Listener—Configures rules, not type.

Reasoning: OCI requires new creation for type change.

Conclusion: C is correct.

OCI documentation states: “To change from a fixed to a flexible load balancer, delete the existing fixed load balancer and create a new flexible one (C)—direct conversion isn’t supported.” A is too absolute, B and D don’t apply—only C matches OCI’s process.

Oracle Cloud Infrastructure Load Balancing Documentation, "Changing Load Balancer Type".

Detailed Answer in Step-by-Step Solution:

Analyze OCI Open Data: OCI Open Data is a free service providing access to public datasets for AI/ML use cases.

Evaluate Statements:

A: True—Open Data includes text and image datasets (e.g., geospatial images).

B: False—Video and other formats may be available depending on the dataset; no strict exclusion exists.

C: False—Datasets may include metadata, but code/tooling examples aren’t guaranteed.

D: True—It’s designed for data scientists and analysts who work with datasets.

E: False—It’s not a user-contributed repository; it’s curated by Oracle.

F: False—Open Data is free and public, not subscription-based.

Select Two: A and D align with the service’s purpose and offerings.

OCI Open Data provides access to datasets like text and images (A) for AI/ML, aimed at data professionals (D). It’s a free, curated service, not user-contributed (E) or paid (F), and while it focuses on certain formats, it doesn’t explicitly exclude audio/video (B). (Reference: Oracle Cloud Infrastructure Open Data Documentation, "Overview of Open Data").

Detailed Answer in Step-by-Step Solution:

Objective: Identify a true statement about OCI Data Science Jobs.

Understand OCI Jobs: Jobs automate ML tasks (e.g., training) on managed infrastructure.

Evaluate Options:

A: True—Jobs provision OCI compute resources on-demand for task execution.

B: False—Users define custom tasks (e.g., Python scripts), not limited to standard ones.

C: False—Infrastructure is fully managed by OCI, not user-managed.

D: False—Multiple artifacts (e.g., ZIP with dependencies) can be used, not just one file.

Reasoning: A reflects OCI’s managed, on-demand provisioning model for Jobs.

Conclusion: A is correct.

The OCI Data Science documentation states: “Jobs provision compute infrastructure on-demand to execute user-defined tasks, such as model training or data processing, on fully managed OCI resources.” B is incorrect (customization is allowed), C contradicts the managed nature, and D misstates artifact flexibility—only A accurately describes Jobs.

Oracle Cloud Infrastructure Data Science Documentation, "Jobs Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Diagnose a network timeout during pip install in a notebook session.

Understand Notebook Networking: Sessions run in a VCN; internet access requires specific configs.

Analyze Timeout: Indicates failure to reach the public PyPI repository—likely no internet outbound route.

Evaluate Options:

A: NAT Gateway—Provides internet access for private subnets—correct fix.

B: Service Gateway—Accesses OCI services privately, not public internet.

C: FastConnect—Links to on-premises, not public internet.

D: VNIC—Essential but present by default; doesn’t solve internet access.

Reasoning: NAT Gateway enables outbound traffic to public repos like PyPI.

Conclusion: A is correct.

OCI documentation notes: “Notebook sessions in a private subnet require a NAT Gateway to access public internet resources, such as PyPI, for package installation via pip. Without it, network timeouts occur.” Service Gateway (B) is for OCI services, FastConnect (C) is irrelevant, and VNIC (D) is standard—only A resolves the issue.

Oracle Cloud Infrastructure Data Science Documentation, "Networking for Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Address declining model performance and improve churn prediction.

Analyze Issue: Poor predictions suggest data drift or model staleness—common ML challenges.

Evaluate Options:

A. Drift monitoring: Tracks changes in data distribution—identifies root cause of accuracy drop—correct.

B. Redeploy the model: Repeats deployment without fixing the issue—ineffective alone.

C. Operational monitoring: Tracks system health (e.g., latency), not prediction quality.

D. Retrain the model: Updates model with new data—directly improves accuracy—correct.

E. Validate with recent data: Checks performance but doesn’t fix—diagnostic, not corrective.

Reasoning: A diagnoses drift (cause), D retrains to adapt (solution)—best combo to verify and lower churn.

Conclusion: A and D are correct.

OCI documentation advises: “Drift monitoring (A) detects shifts in data distribution that degrade model performance, while retraining (D) with fresh data restores accuracy.” Redeployment (B) doesn’t address drift, operational monitoring (C) focuses on infra, and validation (E) is a check—not a fix. A and D align with OCI’s model maintenance strategy.

Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".

Detailed Answer in Step-by-Step Solution:

Understand Model Catalog: The OCI Model Catalog stores trained models and associated details.

Identify Shareable Artifacts: When sharing, all components—model file, metadata (e.g., name, description), hyperparameters (e.g., learning rate), and metrics (e.g., accuracy)—are accessible.

Evaluate Options:

A: Excludes the model itself—incorrect.

B: Excludes metrics—incorrect.

C: Excludes metadata and hyperparameters—incorrect.

D: Includes all components—correct.

Conclusion: D is comprehensive and accurate.

The OCI Model Catalog allows sharing of the model artifact (the trained model), metadata, hyperparameters, and performance metrics, enabling full examination by colleagues. This isdetailed in the official documentation. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Overview").

Detailed Answer in Step-by-Step Solution:

Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.

Evaluate Options:

A: False—Resource principals eliminate manual credential management.

B: False—They’re secure, leveraging IAM policies, not less secure than API keys.

C: False—Data Science supports resource principals for accessing resources (e.g., Object Storage).

D: True—Resource principals are an IAM feature authorizing resources as actors.

Reasoning: D captures the essence of resource principals as an IAM mechanism.

Conclusion: D is correct.

OCI documentation states: “A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies.” This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.

Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".

Detailed Answer in Step-by-Step Solution:

Objective: Identify common data science techniques.

Define Techniques:

Classification: Predicts categories (e.g., spam vs. not).

Regression: Predicts continuous values (e.g., sales).

Clustering: Groups data (e.g., customer segments).

Evaluate Options:

A, B, C: All are standard ML/statistical methods.

D: Encompasses all—correct as they’re widely used.

Reasoning: These are foundational in data science workflows.

Conclusion: D is correct.

OCI documentation lists “classification, regression, and clustering as core techniques in data science, supported by tools like ADS SDK and AutoML.” All (D) are common per OCI’s ML framework, not just subsets (A, B, C).

Oracle Cloud Infrastructure Data Science Documentation, "Analytical Techniques".

Detailed Answer in Step-by-Step Solution:

Objective: Choose the better model for cancer prediction based on metrics.

Understand Metrics:

Accuracy: Overall correct predictions.

Recall: True positives / (True positives + False negatives)—crucial for cancer (minimizing misses).

Context: Cancer prediction prioritizes recall—false negatives (missed cancers) are critical.

Evaluate Models:

Model 1: 80% accuracy, 70% recall—Misses more cancers.

Model 2: 75% accuracy, 85% recall—Misses fewer cancers.

Evaluate Options:

A: High recall—True, but lacks context.

B: High accuracy—Misses recall’s importance.

C: Recall’s impact—Correct for cancer use case—best.

D: Lesser recall impact—Incorrect for this priority.

Reasoning: C emphasizes recall’s critical role—aligns with medical needs.

Conclusion: C is correct.

OCI documentation advises: “For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at 85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C).” A is partial, B overlooks context, D reverses priority—only C fits OCI’s ML evaluation guidance for this scenario.

Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".

Detailed Answer in Step-by-Step Solution:

Objective: Sequence steps for a PySpark app in OCI Data Science.

Evaluate Steps:

Launch notebook: First—provides the environment.

Install PySpark conda: Second—sets up Spark libraries.

Configure core-site.xml: Third—connects to data (e.g., Object Storage).

Develop app: Fourth—writes the PySpark code.

Data Flow: Fifth—optional scaling, post-development.

Check Options: D (1, 2, 3, 4, 5) matches this logical flow.

Reasoning: Notebook first, then setup, coding, and scaling.

Conclusion: D is correct.

OCI documentation recommends: “1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale.” D follows this—others (A, B, C) misorder critical steps like launching the notebook.

Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the non-visualized AutoML stage with small data.

Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.

Evaluate Options:

A: Feature selection—Visualized (e.g., feature importance).

B: Algorithm selection—Visualized (e.g., algorithm scores).

C: Adaptive sampling—Skipped/visualization absent for <1000 rows.

D: Hyperparameter tuning—Visualized (e.g., trial plots).

Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.

Conclusion: C is correct.

OCI AutoML documentation notes: “Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it’s skipped, and no visualization is generated.” Other stages (A, B,D) produce visuals—only C is absent here.

Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".

Detailed Answer in Step-by-Step Solution:

Objective: Understand FAP’s effect in OCI Anomaly Detection.

Evaluate Options:

A: Disable reporting—Incorrect; FAP sets threshold.

B: Sensitivity—Correct; lower FAP reduces false positives.

C: Error message—Incorrect; not a count mechanism.

D: Score per signal—Incorrect; FAP is a global setting.

Reasoning: FAP adjusts detection threshold—key to sensitivity.

Conclusion: B is correct.

OCI documentation states: “False Alarm Probability (FAP) controls the model’s sensitivity—lower values reduce false positives, higher values increase detection.” B aligns—others misrepresent FAP’s role.

Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Configuration".

Detailed Answer in Step-by-Step Solution:

Objective: Get all AutoML trial results.

Understand AutoML: Trials include hyperparameter tuning outcomes.

Evaluate Options:

A: print_trials()—Displays all trial results—correct.

B: visualize_tuning_trials()—Visualizes tuning, not full list.

C: visualize_adaptive_sampling_trials()—Specific to sampling, not all trials.

D: visualize_algorithm_selection_trials()—Specific to algorithms, not all.

Reasoning: A provides comprehensive trial output.

Conclusion: A is correct.

OCI AutoML documentation states: “print_trials() outputs a table of all trials performed, including hyperparameters and scores.” Visualization methods (B, C, D) focus on specific aspects—only A gives the full list.

Oracle Cloud Infrastructure AutoML Documentation, "Trial Output Methods".

Detailed Answer in Step-by-Step Solution:

Understand OCI Data Science Pipelines: Pipelines automate ML workflows with configurable steps.

Check Override Mechanism: Defaults (e.g., compute shape, storage) can be modified before execution via the OCI Console, SDK, or CLI.

Evaluate Options:

A: False—Overrides can occur post-creation, before running.

B: False—Any authorized user, not just admins, can override defaults.

C: True—Settings can be adjusted before execution starts.

D: False—Defaults can be changed post-creation, pre-execution.

Conclusion: C is correct as it reflects the flexibility of pipeline configuration.

OCI Data Science Pipelines allow users to override default settings (e.g., compute resources, environment variables) before execution, as noted in the official documentation. This can be done via the UI or programmatically, offering flexibility beyond creation time (A) and without admin-only restrictions (B). (Reference: Oracle Cloud Infrastructure Data Science Pipelines Documentation, "Configuring Pipelines").

Detailed Answer in Step-by-Step Solution:

Objective: Identify a true statement about standards in an OCI context (likely governance/security).

Understand Standards: Rules or benchmarks, often compliance-related.

Evaluate Options:

A: Auditable—True; standards are checked for adherence.

B: Result of requirements—Partially true, but not always.

C: Methods/instructions—More procedural, not defining standards.

D: Foundation of governance—Broad, not specific to standards.

Reasoning: A is universally true—standards face audits (e.g., SOC, ISO).

Conclusion: A is correct.

OCI documentation notes: “Standards (e.g., security standards) may be audited (A) to ensure compliance with OCI policies or external regulations.” B is a source, C describes procedures, D is too vague—only A is consistently true per OCI’s compliance framework.

Oracle Cloud Infrastructure Security Documentation, "Compliance and Standards".

Detailed Answer in Step-by-Step Solution:

Objective: Sequence OCI AutoML pipeline stages.

Stages:

Adaptive sampling: Reduces data size if large.

Feature selection: Picks relevant features.

Algorithm selection: Chooses best model type.

Hyperparameter tuning: Optimizes model params.

Evaluate: C (sampling, features, algorithms, tuning) matches logical flow—data first, then model.

Reasoning: Sampling precedes feature work—standard in OCI.

Conclusion: C is correct.

OCI documentation states: “AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C).” Sampling reduces data first, then features and models are optimized—other orders (A, B, D) misalign with OCI’s sequence.

Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".

Detailed Answer in Step-by-Step Solution:

Objective: Spot the invalid OCI IAM policy.

Understand Policies: Use verbs (e.g., use, manage) and resource types.

Evaluate Options:

A: Valid—virtual-network-family is a resource type.

B: Invalid—No data-science-model-sessions resource type; likely meant data-science-models.

C: Valid—data-science-projects is correct.

D: Valid—data-science-family aggregates Data Science resources.

Reasoning: B uses a nonexistent resource type—error in syntax.

Conclusion: B is incorrect.

OCI IAM documentation lists valid resource types: “data-science-models, data-science-projects, data-science-family—but not data-science-model-sessions (B).” A, C, D are correct—B is invalid due to this typo/misunderstanding.

Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".

Detailed Answer in Step-by-Step Solution:

Objective: Find the OCI service for free geospatial images.

Evaluate Options:

A: Big Data Service—Spark processing, not datasets.

B: Analytics Cloud—Visualization, not data source.

C: Data Science—ML platform, not dataset provider.

D: Open Data—Free public datasets, including geospatial—correct.

Reasoning: Open Data is OCI’s public dataset hub.

Conclusion: D is correct.

OCI documentation states: “Oracle Open Data provides free access to curated datasets, including geospatial images, for public use.” A, B, and C serve other purposes—only D delivers free geospatial data.

Oracle Cloud Infrastructure Open Data Documentation, "Dataset Offerings".

Detailed Answer in Step-by-Step Solution:

Understand OCI IAM Policies: Policies control access using verbs like “inspect,” “read,” “use,” and “manage.”

Define Requirement: Consultants need view-only access without modification rights.

Evaluate Verbs:

A. Use: Allows interaction (e.g., running jobs)—too permissive.

B. Inspect: Grants view-only access to resource details—matches requirement.

C. Manage: Full control (create, update, delete)—too permissive.

D. Read: Includes viewing content (e.g., data), not just metadata—slightly broader than needed.

Conclusion: “Inspect” (B) is the precise verb for view-only access to resource details.

In OCI Identity and Access Management (IAM), the “inspect” verb allows listing and viewing resource metadata without granting modification or data access rights, ideal for this scenario. This is confirmed in the IAM policy reference. (Reference: Oracle Cloud Infrastructure Documentation, "IAM Policy Verbs").

Detailed Answer in Step-by-Step Solution:

Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.

Evaluate Options:

A: False—Resource principals eliminate manual credential management.

B: False—They’re secure, leveraging IAM policies, not less secure than API keys.

C: False—Data Science supports resource principals for accessing resources (e.g., Object Storage).

D: True—Resource principals are an IAM feature authorizing resources as actors.

Reasoning: D captures the essence of resource principals as an IAM mechanism.

Conclusion: D is correct.

OCI documentation states: “A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies.” This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.

Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".

Detailed Answer in Step-by-Step Solution:

Objective: Visualize correlation between continuous and categorical features.

Evaluate Options:

A: Pearson—Continuous vs. continuous—incorrect.

B: Cramer’s V—Categorical vs. categorical—incorrect.

C: Correlation ratio—Continuous vs. categorical—correct.

D: General correlation—Not specific to mixed types.

Reasoning: Correlation ratio handles mixed feature types for heatmaps.

Conclusion: C is correct.

OCI documentation states: “correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps.” Pearson (A) and Cramer’s (B) are type-specific, corr() (D) is broad—only C fits per ADS capabilities.

Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".

Detailed Answer in Step-by-Step Solution:

Objective: Identify two methods to enhance Object Storage durability.

Understand Durability: Ensures data isn’t lost—focus on redundancy and protection.

Evaluate Options:

A: RAID1—Block volume feature, not Object Storage.

B: Encryption—Secures data, not durability.

C: Versioning—Retains old versions, prevents loss—correct.

D: Limit delete—Prevents accidental deletion—correct.

E: Client encryption—Secures, not durability-focused.

Reasoning: C and D directly protect against data loss—durability-focused.

Conclusion: C and D are correct.

OCI documentation states: “Improve Object Storage durability with Versioning (C) to retain previous object versions and by limiting delete permissions (D) to prevent accidental loss.” A isn’t applicable, B and E focus on security—only C and D enhance durability per OCI’s storage features.

Oracle Cloud Infrastructure Object Storage Documentation, "Data Durability Options".

Detailed Answer in Step-by-Step Solution:

Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform’s storage system.

Data Exploration: Once data is accessed, it’s explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.

Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.

Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.

Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.

Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.

The correct order reflects the machine learning lifecycle as outlined in Oracle’s OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK’s correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle’s recommended practices for building ML models in OCI Data Science (Reference: Oracle Cloud Infrastructure Data Science Service Documentation, "Machine Learning Lifecycle").

Detailed Answer in Step-by-Step Solution:

Objective: Diagnose a network timeout during pip install in a notebook session.

Understand Notebook Networking: Sessions run in a VCN; internet access requires specific configs.

Analyze Timeout: Indicates failure to reach the public PyPI repository—likely no internet outbound route.

Evaluate Options:

A: NAT Gateway—Provides internet access for private subnets—correct fix.

B: Service Gateway—Accesses OCI services privately, not public internet.

C: FastConnect—Links to on-premises, not public internet.

D: VNIC—Essential but present by default; doesn’t solve internet access.

Reasoning: NAT Gateway enables outbound traffic to public repos like PyPI.

Conclusion: A is correct.

OCI documentation notes: “Notebook sessions in a private subnet require a NAT Gateway to access public internet resources, such as PyPI, for package installation via pip. Without it, network timeouts occur.” Service Gateway (B) is for OCI services, FastConnect (C) is irrelevant, and VNIC (D) is standard—only A resolves the issue.

Oracle Cloud Infrastructure Data Science Documentation, "Networking for Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the firewall type protecting against web app attacks like SQL injection and XSS.

Understand Firewall Types:

Stateful Inspection: Tracks connection states, not app-specific.

Web Application Firewall (WAF): Targets web app vulnerabilities.

Incident Firewall: Not a recognized term.

Packet Filtering: Basic packet rules, not app-aware.

Evaluate Options:

A: Stateful—General network, not web-specific—incorrect.

B: WAF—Designed for SQLi, XSS—correct.

C: Incident—Non-existent—incorrect.

D: Packet—Low-level, not app-focused—incorrect.

Reasoning: WAF specializes in web app security—matches requirement.

Conclusion: B is correct.

OCI documentation states: “Web Application Firewall (WAF) (B) protects against web application attacks like SQL injection and cross-site scripting by inspecting HTTP traffic.” A and D handle network-level threats, C isn’t real—only B aligns with OCI’s WAF purpose.

Oracle Cloud Infrastructure WAF Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Define OCI Secrets: Secrets are sensitive data (e.g., API keys, passwords) managed securely in OCI.

Evaluate Options:

A: Object Storage is for general data, not secure secret management.

B: Vault is OCI’s service for storing and managing secrets securely.

C: Autonomous Data Warehouse is for analytics, not secret storage.

D: Oracle Databases store data, not OCI-specific secrets.

Reasoning: Vault is purpose-built for secrets with encryption and access control.

Conclusion: B is correct.

OCI Vault “provides a secure, centralized service to store and manage secrets, such as passwords and keys, with encryption at rest and fine-grained access policies.” Object Storage (A), Autonomous Data Warehouse (C), and Oracle Databases (D) serve other purposes—only Vault (B) is designed for secrets per OCI’s security architecture.

Oracle Cloud Infrastructure Vault Documentation, "Secrets Management".

Detailed Answer in Step-by-Step Solution:

Objective: Identify correct statements about installing Python libraries in OCI Data Science.

Understand Notebook Sessions: Run in a managed environment with specific permissions.

Evaluate Options:

A: False—No root privileges; users operate as datascience with limited sudo.

B: True—pip install from PyPI works with internet access (e.g., NAT Gateway).

C: False—Yum isn’t available; pip is the primary tool as a normal user.

D: False—Misstated; youcaninstall non-preinstalled libraries—likely a typo (intended opposite).

E: True—Custom repos are supported with proper network config.

Correct Interpretation: Assuming D’s intent was “Youcaninstall…” (common exam error), B, D (corrected), E are true.

Conclusion: B, D (corrected), E are correct.

OCI documentation states: “In notebook sessions, you can install Python libraries from PyPI (B) or private repositories (E) using pip, but root privileges (A) are not granted—users operate asdatascience.” Yum (C) isn’t supported, and D’s phrasing contradicts capability—corrected, it’s true you can install beyond preinstalled. B, D (adjusted), E align with OCI’s flexibility.

Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Determine minimum log storage duration before archiving in Logging Analytics.

Understand Logging Analytics: Logs are active before archival.

Evaluate Options:

A: 60 days—Too long for minimum.

B: 10 days—Too short.

C: 30 days—Standard minimum—correct.

D: 15 days—Not OCI’s default.

Reasoning: 30 days is OCI’s documented minimum active period.

Conclusion: C is correct.

OCI documentation states: “Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis.” B and D are shorter, A is longer—only C matches OCI’s policy.

Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the WAF component that controls request actions based on criteria.

Understand WAF Components:

Protection Rules: Define conditions and actions (e.g., allow, block, log).

Bot Management: Handles bot traffic, not general request rules.

Origin: Backend server endpoint, not rule-based.

WAF Policy: Umbrella config, but rules specify actions.

Evaluate Options:

A: Protection rules—Set specific criteria and actions—correct.

B: Bot Management—Bot-specific, not general requests.

C: Origin—Defines source, not actions.

D: WAF policy—Broad config, not the granular rules.

Reasoning: Protection rules directly manage request behavior—fit the requirement.

Conclusion: A is correct.

OCI documentation states: “Protection rules (A) in WAF define conditions (e.g., IP, URL) and actions (allow, block, log) for incoming requests.” Bot Management (B) targets bots, Origin (C) is a target server, and WAF Policy (D) encompasses rules but isn’t the action specifier—only A aligns with OCI’s WAF configuration.

Oracle Cloud Infrastructure WAF Documentation, "Protection Rules".

Detailed Answer in Step-by-Step Solution:

Define Feature Engineering: It’s the process of creating or modifying features to improve model performance.

Evaluate Options:

A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.

B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.

C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering—correct.

D: Understanding features occurs during exploration, not engineering.

Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of-week), distinct from tuning or interpretation.

Conclusion: C is the precise definition.

OCI Data Science documentation defines feature engineering as “the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy.” Examples include scaling or creating interaction terms, aligning with C. Other options (A, B, D) relate to different ML stages.

Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify OCI’s managed Kubernetes service.

Evaluate Options:

A: Container Registry—Stores images, not Kubernetes.

B: Load Balancing—Network service, not Kubernetes.

C: Container Engine (OKE)—Managed Kubernetes—correct.

D: Streaming—Data streaming, not containers.

Reasoning: C is OCI’s Kubernetes offering—OKE.

Conclusion: C is correct.

OCI documentation states: “OCI Container Engine for Kubernetes (OKE) (C) provides a managed service to deploy, scale, and manage containerized applications using Kubernetes.” A, B, and D serve other purposes—only C matches per OCI’s container services.

Oracle Cloud Infrastructure OKE Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Understand OCI Data Labeling Export: After annotation, datasets are exported for ML use.

Check Supported Formats: OCI Data Labeling exports annotations in a structured, machine-readable format.

Evaluate Options:

A: Binary isn’t a standard export format for annotations.

B: XML isn’t used; JSON is preferred for flexibility.

C: Line-delimited JSON is the correct format, aligning with ML workflows.

D: CSV is common but not the default for OCI Data Labeling.

Conclusion: C matches the official export format.

OCI Data Labeling exports annotated datasets as line-delimited JSON files, which store each annotation as a separate JSON object per line, suitable for ML pipelines. This is explicitly stated in the documentation. (Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Exporting Datasets").

Detailed Answer in Step-by-Step Solution:

Objective: Determine when an OCI Data Science pipeline can start.

Understand Pipelines: They’re workflows with defined steps, executed on demand or scheduled.

Evaluate Options:

A: Once created, a pipeline can be run immediately—correct.

B: “During run state” implies it’s already running—illogical.

C: “After active state” is unclear; pipelines run when triggered, not post-state.

D: “Before active state” is vague—creation precedes running.

Reasoning: Pipelines are executable post-creation via UI/CLI—simplest interpretation is A.

Conclusion: A is correct.

OCI Data Science documentation states: “After a pipeline is created, you can initiate a pipeline run immediately or schedule it using the OCI Console, CLI, or SDK.” B, C, and D misalign with this—running starts post-creation (A), not during/after ambiguous states.

Oracle Cloud Infrastructure Data Science Documentation, "Pipelines - Running a Pipeline".

Detailed Answer in Step-by-Step Solution:

Objective: Transcribe Spanish video audio and identify profanity.

Evaluate Options:

A: Anomaly Detection—Not for transcription or text analysis.

B: Speech—Converts audio to text (e.g., Spanish), base for further analysis—correct.

C: Translation—Translates text, not transcription.

D: Language—Analyzes text (e.g., profanity), but needs transcribed input.

Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.

Conclusion: B is correct for transcription.

OCI Speech “transcribes audio from video or audio files into text, supporting languages like Spanish.” Post-transcription, OCI Language could detect profanity, but B is the starting point—Anomaly (A) and Translation (C) don’t fit.

Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the key difference between supervised and unsupervised learning.

Define Types:

Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.

Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).

Evaluate Options:

A: Labeled vs. unlabeled—Core distinction, correct.

B: Monitoring—Misleading, not the primary difference.

C: Image recognition—False, supervised applies broadly.

D: Data Engineer—Irrelevant to learning type.

Reasoning: A captures the foundational data difference.

Conclusion: A is correct.

OCI documentation states: “Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns.” B, C, and D misrepresent this—only A aligns with OCI’s ML definitions and industry standards.

Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".

Detailed Answer in Step-by-Step Solution:

Objective: Troubleshoot a 404 auth error when saving a model with resource principal.

Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies—no user credentials.

Analyze 404 Error: Indicates permission failure—likely IAM misconfiguration.

Evaluate Options:

A: Block volume storage—Irrelevant to auth; it’s about saving locally—incorrect.

B: Dynamic group matching—Ensures notebook is recognized—correct.

C: User group policy—Not used with resource principal—incorrect.

D: Dynamic group policy—Grants catalog access—correct.

E: Service Gateway—Network-related, not auth-specific—incorrect.

Reasoning: Resource principal needs B (group inclusion) and D (policy perms)—404 points to these.

Conclusion: B and D are correct.

OCI documentation states: “For ADS SDK to save to the Model Catalog using resource principal, ensure (1) a dynamic group includes notebook sessions with matching rules (e.g., resource.type ='datasciencenotebooksession') (B), and (2) a policy grants manage data-science-models to that dynamic group (D).” A is storage, C is user-based, E is network—only B and D fix the auth issue per OCI’s IAM setup.

Oracle Cloud Infrastructure Data Science Documentation, "Resource Principal with Model Catalog".

Detailed Answer in Step-by-Step Solution:

Objective: Enable credential-less API calls from an instance.

Understand Resource Principal: Allows instances to authenticate via IAM without user creds.

Evaluate Options:

A: Dynamic group + policy—Correct; groups instance, grants access.

B: Dynamic group only—Incomplete; needs policy.

C: User group—Irrelevant for instances.

D: No config—False; setup required.

Reasoning: A sets up resource principal fully—group and perms.

Conclusion: A is correct.

OCI documentation states: “To make API calls without credentials, create a dynamic group including the instance and add a policy (A) granting access to OCI services—enables resource principal.” B lacks policy, C is user-based, D is false—only A completes the process per OCI’s IAM setup.

Oracle Cloud Infrastructure IAM Documentation, "Resource Principal Configuration".

Detailed Answer in Step-by-Step Solution:

Objective: Securely authenticate a recurring OCI Job.

Evaluate Options:

A: PAR—Limited duration, insecure for recurring jobs.

B: Resource principal—Secure, managed auth for Jobs—correct.

C: Personal config—Unscalable, security risk.

D: Vault with personal keys—Complex, still uses user creds.

Reasoning: B uses OCI’s native, secure resource principal mechanism.

Conclusion: B is correct.

OCI documentation states: “For Jobs accessing sensitive data, use resource principals with a dynamic group (e.g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog—secure and scalable.” A is temporary, C and D risk credential exposure—B is best practice.

Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".

Detailed Answer in Step-by-Step Solution:

Objective: Assess if bias is a common issue in data science.

Define Bias: Systematic errors in data/models (e.g., skewed training data).

Evaluate Statement:

Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws—widely recognized in ML.

Examples: Gender bias in hiring models, racial bias in facial recognition.

Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.

Conclusion: A (True) is correct.

OCI documentation notes: “Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks.” This aligns with industry standards—bias is a well-documented issue, making A true.

Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".

Detailed Answer in Step-by-Step Solution:

Objective: Determine the primary benefit of data sampling.

Define Sampling: Selecting a subset of data to represent the whole—used in ML/statistics.

Evaluate Options:

A: Small batches reduce resources—True but not the main purpose.

B: Reduces storage—Incidental, not the goal.

C: Representative subset for faster, accurate models—Core purpose of sampling.

Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).

Conclusion: C is correct.

OCI documentation states: “Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical.” A focuses on resources (secondary), B on storage (not primary)—only C captures the analytical intent per OCI’s AutoML sampling approach.

Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the OCI service for ML model lifecycle.

Evaluate Options:

A: Data Catalog—Metadata management, not ML.

B: Data Integration—ETL, not ML.

C: Data Science—Full ML lifecycle—correct.

D: Data Flow—Spark processing, not full ML.

Reasoning: C supports building, training, deploying models.

Conclusion: C is correct.

OCI documentation states: “OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog.” A, B, and D serve other purposes—only C fits the ML lifecycle per OCI’s offerings.

Oracle Cloud Infrastructure Data Science Documentation, "Service Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Choose the better model for cancer prediction based on metrics.

Understand Metrics:

Accuracy: Overall correct predictions.

Recall: True positives / (True positives + False negatives)—crucial for cancer (minimizing misses).

Context: Cancer prediction prioritizes recall—false negatives (missed cancers) are critical.

Evaluate Models:

Model 1: 80% accuracy, 70% recall—Misses more cancers.

Model 2: 75% accuracy, 85% recall—Misses fewer cancers.

Evaluate Options:

A: High recall—True, but lacks context.

B: High accuracy—Misses recall’s importance.

C: Recall’s impact—Correct for cancer use case—best.

D: Lesser recall impact—Incorrect for this priority.

Reasoning: C emphasizes recall’s critical role—aligns with medical needs.

Conclusion: C is correct.

OCI documentation advises: “For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at 85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C).” A is partial, B overlooks context, D reverses priority—only C fits OCI’s ML evaluation guidance for this scenario.

Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".

Detailed Answer in Step-by-Step Solution:

Objective: Scale up notebook without losing work.

Evaluate Options:

A: Object Storage—Extra steps, inefficient.

B: Block volume—Persists data, seamless scale—correct.

C: Local machine—Risky, cumbersome.

D: Recreate—Loses work, impractical.

Reasoning: B uses OCI’s built-in persistence.

Conclusion: B is correct.

OCI documentation states: “Files in /home/datascience (B) persist on block volume; deactivate, then reactivate with a larger shape to scale up without data loss.” A, C, and D add complexity or risk—only B is optimal per OCI’s design.

Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Select an auth method for third-party APIs lacking OCI signature support.

Understand OCI Auth: Typically uses API keys, but alternatives exist for non-standard APIs.

Evaluate Options:

A: Username/password—Not API-friendly, insecure.

B: API Signing Key—Requires signature-based auth, unsupported here.

C: SSH Key—For instance access, not APIs.

D: Auth Token—Simple token for API calls—correct.

Reasoning: Auth Token provides a bearer token for APIs without signature complexity.

Conclusion: D is correct.

OCI documentation states: “For third-party APIs not supporting signature-based authentication, use an Auth Token (D), a secure, revocable token for accessing OCI resources via REST APIs.” A, B, and C don’t fit non-signature scenarios—only D ensures compatibility per OCI’s IAM options.

Oracle Cloud Infrastructure IAM Documentation, "Auth Tokens for API Access".

Detailed Answer in Step-by-Step Solution:

Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.

Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.

Evaluate Options:

A. Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn’t typically include readable source code; it’s a trained object, not the training script.

B. Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.

C. Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).

D. Check for provenance details: Provenance tracks the model’s creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.

Reasoning: Provenance details are designed to document the “how” and “what” of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.

Conclusion: D is the best approach for detailed investigation.

In OCI Data Science, the Model Catalog stores provenance information, which includes “details about the model’s origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references.” This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.

Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.

Detailed Answer in Step-by-Step Solution:

Objective: Identify interfaces to save a 2 GB model to the Model Catalog.

Evaluate Options:

A: OCI CLI—Supports Data Science tasks—possible but not primary.

B: ADS SDK—Designed for model catalog ops—correct.

C: ODSC CLI—Not standard; likely typo for OCI CLI.

D: Console—GUI for catalog uploads—correct.

E: OCI Python SDK—Programmatic catalog access—correct.

F: Git CLI—Version control, not catalog-related.

Reasoning: B, D, E are OCI’s primary interfaces; A is valid but less emphasized.

Conclusion: B, D, E are correct (A plausible but not top-tier).

OCI documentation lists “ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog.” OCI CLI (A) works but isn’t highlighted, C isn’t real, and F is unrelated—B, D, E are the standard trio.

Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".

Detailed Answer in Step-by-Step Solution:

Objective: Match a cache rule criterion for exact URL path and query.

Understand Cache Rules: Used in OCI (e.g., WAF, CDN) to cache content.

Evaluate Options:

A: Contains—Partial match, not exact.

B: Is—Exact match of full URL (path + query)—correct.

C: Ends with—Matches end, not full URL.

D: Starts with—Matches start, not full URL.

Reasoning: “URL_IS” checks exact equality—fits requirement.

Conclusion: B is correct.

OCI documentation states: “The URL_IS (B) criterion in cache rules matches when the full URL (path and query) exactly equals the specified value.” A, C, and D are partial matches—only B ensures identical concatenation per OCI’s caching config.

Oracle Cloud Infrastructure WAF Documentation, "Cache Rules Criteria".

Detailed Answer in Step-by-Step Solution:

Objective: Select an OCI AI service to sort images by language.

Evaluate Options:

A: Digital Assistant—Chatbots, not image/language processing.

B: Vision—Image analysis (e.g., object detection), not language sorting.

C: Speech—Audio-to-text, not image-based.

D: Language—Text analysis (e.g., language detection) after OCR—correct.

Reasoning: Images need OCR (Vision) then language detection (Language)—D fits the sorting task.

Conclusion: D is correct.

OCI Language “detects and classifies languages in text,” often paired with OCI Vision’s OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language—Digital Assistant (A) and Speech (C) don’t apply. Documentation supports this workflow.

Oracle Cloud Infrastructure Language Documentation, "Language Detection".

Detailed Answer in Step-by-Step Solution:

Objective: Identify a widely accepted maxim about data scientists’ time allocation.

Understand Data Science Workflow: Involves data collection, preparation, and analysis—time distribution is key.

Evaluate Options:

A: 80% on finding/preparing, 20% analyzing—Reflects the data wrangling challenge.

B: 80% analyzing, 20% finding/preparing—Inverts the common perception.

C: 80% on failed projects, 20% useful—Pessimistic, not a standard maxim.

Reasoning: Industry consensus (e.g., “80/20 rule”) emphasizes data prep as the bulk of effort due to messy real-world data.

Conclusion: A is correct.

OCI Data Science documentation aligns with industry norms: “Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data.” B reverses this, and C isn’t supported—only A reflects this widely cited maxim from sources like Forbes and OCI’s practical guidance.

Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify IAM components in OCI.

Understand IAM: Manages access via policies, groups, etc.

Evaluate Options:

A: Policies—Core IAM component—correct.

B: Subnets—Networking, not IAM.

C: Instances—Compute, not IAM.

D: VCN—Networking, not IAM.

Reasoning: Only A is an IAM element—others are resources.

Conclusion: A is correct.

OCI documentation states: “IAM includes components like policies (A), groups, and compartments to control resource access.” B, C, and D are infrastructure, not IAM—only A fits per OCI’s IAM framework.

Oracle Cloud Infrastructure IAM Documentation, "IAM Components".

Detailed Answer in Step-by-Step Solution:

Objective: Predict AutoML’s output when time runs out during tuning.

Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.

Evaluate Options:

A: Best-known config—Logical, reflects optimization goal—correct.

B: Last config—Ignores prior better results—incorrect.

C: Minimum learning rate—Arbitrary, not performance-based.

D: Random—Defeats tuning purpose.

Reasoning: AutoML prioritizes the best config found within the budget.

Conclusion: A is correct.

OCI AutoML documentation states: “If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics.” Last (B), minimum (C), or random (D) configs aren’t selected—only A aligns with OCI’s optimization strategy.

Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".

Detailed Answer in Step-by-Step Solution:

Objective: Optimize embarrassingly parallel workload in OCI Jobs.

Evaluate Options:

A: Sequential runs—Inefficient for parallel tasks.

B: Simultaneous runs—Maximizes parallelism—correct.

C: False—Jobs support parallelism.

D: One job per run—Misstates capability, wasteful.

Reasoning: B leverages OCI’s parallel run support.

Conclusion: B is correct.

OCI documentation states: “For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently.” A is slow, C is incorrect, and Dovercomplicates—B is the best approach.

Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".

Detailed Answer in Step-by-Step Solution:

Objective: Configure conda env for a pipeline step.

Evaluate Options:

A: Shape—Infra, not env config.

B: Volume—Storage, not env.

C: Command-line—Step args, not env.

D: Env variables—Sets conda path—correct.

Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).

Conclusion: D is correct.

OCI documentation states: “Configure a pipeline step’s conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition.” A, B, and C address other aspects—only D fits env config.

Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".