Databricks Databricks-Generative-AI-Engineer-Associate Dumps

The scenario describes a common "retrieval gap" where the initial bi-encoder (embedding model) used for vector search identifies relevant documents but does not rank them perfectly. This happens because embedding models represent entire documents as a single vector, which can lose nuance. The standard engineering solution is to implement aReranker(Cross-Encoder). Unlike embedding models, a reranker processes the query and a candidate document simultaneously, allowing it to capture deep semantic interactions between the two. In a Mosaic AI workflow, after the vector search retrieves the top $k$ documents, the reranker evaluates those specific $k$ documents to produce a more accurate relevance score. This ensures that the most contextually relevant documents are placed at the top of the list (and thus the top of the LLM prompt), which is crucial because LLMs are sensitive to document order and often prioritize information found at the beginning of the context.

The task is to benchmark LLMs for text translation using an evaluation set with known high-quality examples, requiring a performant metric. Let’s evaluate the options.

Option A: ROUGE metric

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures overlap between generated and reference texts, primarily for summarization. It’s less suited for translation, where precision and word order matter more.

Databricks Reference:"ROUGE is commonly used for summarization, not translation evaluation"("Generative AI Cookbook," 2023).

Option B: BLEU metric

BLEU (Bilingual Evaluation Understudy) evaluates translation quality by comparing n-gram overlap with reference translations, accounting for precision and brevity. It’s widely used, performant, and appropriate for this task.

Databricks Reference:"BLEU is a standard metric for evaluating machine translation, balancing accuracy and efficiency"("Building LLM Applications with Databricks").

Option C: NDCG metric

NDCG (Normalized Discounted Cumulative Gain) assesses ranking quality, not text generation. It’s irrelevant for translation evaluation.

Databricks Reference:"NDCG is suited for ranking tasks, not generative output scoring"("Databricks Generative AI Engineer Guide").

Option D: RECALL metric

Recall measures retrieved relevant items but doesn’t evaluate translation quality (e.g., fluency, correctness). It’s incomplete for this use case.

Databricks Reference: No specific extract, but recall alone lacks the granularity of BLEU for text generation tasks.

Conclusion: Option B (BLEU) is the best metric for translation evaluation, offering a performant and standard approach, as endorsed by Databricks’ guidance on generative tasks.

In modern agentic frameworks like LangGraph or LangChain, the standard workflow for creating an autonomous tool-calling agent follows a specific sequence. First, tools must be defined (often as Python functions with clear docstrings, which the LLM uses to understand the tool's purpose). Second, the agent logic is defined, which specifies how the LLM should think. Third, the agent is initialized using a logic pattern likeReAct(Reason + Act). The ReAct framework is essential here because it enables the "orchestrator" loop: the LLM receives a prompt, generates a "Thought" about which tool to use, generates an "Action" to call that tool, receives an "Observation" (the tool's output), and repeats until it can provide a final answer. Loading tools into "separate agents" (C) or defining tools "inside" agents (D) are non-standard patterns that add unnecessary complexity and do not align with the centralized orchestration model required for LangGraph.

Problem Context: The task is to design responses for a healthcare-focused chatbot that appropriately addresses the urgency of a patient's symptoms.

Explanation of Options:

Option A: Suggesting articles might be suitable for less urgent inquiries but is inappropriate for symptoms that could indicate a serious condition.

Option B: Given the description of severe symptoms like headaches and dizziness, directing the patient to emergency services is prudent. This aligns with medical guidelines that recommend immediate professional attention for such severe symptoms.

Option C: Offering well-wishes does not address the potential seriousness of the symptoms and lacks appropriate action.

Option D: While gathering more information is part of a detailed assessment, the immediate need here suggests a more urgent response.

Given the potential severity of the described symptoms,Option Bis the most appropriate, ensuring the chatbot directs patients to seek urgent care when needed, potentially saving lives.

The task is to prevent toxic or unsafe responses in an LLM system using the Foundation Model API with minimal effort. Let’s assess the options.

Option A: Host Llama Guard on Foundation Model API and use it to detect unsafe responses

Llama Guard is a safety-focused model designed to detect toxic or unsafe content. Hosting it via the Foundation Model API (a Databricks service) integrates seamlessly with the existing system, requiring minimal setup (just deployment and a check step), and leverages provisioned throughput for performance.

Databricks Reference:"Foundation Model API supports hosting safety models like Llama Guard to filter outputs efficiently"("Foundation Model API Documentation," 2023).

Option B: Add some LLM calls to their chain to detect unsafe content before returning text

Using additional LLM calls (e.g., prompting an LLM to classify toxicity) increases latency, complexity, and effort (crafting prompts, chaining logic), and lacks the specificity of a dedicated safety model.

Databricks Reference:"Ad-hoc LLM checks are less efficient than purpose-built safety solutions"("Building LLM Applications with Databricks").

Option C: Add a regex expression on inputs and outputs to detect unsafe responses

Regex can catch simple patterns (e.g., profanity) but fails for nuanced toxicity (e.g., sarcasm, context-dependent harm), requiring significant manual effort to maintain and update rules.

Databricks Reference:"Regex-based filtering is limited for complex safety needs"("Generative AI Cookbook").

Option D: Ask users to report unsafe responses

User reporting is reactive, not preventive, and places burden on users rather than the system. It doesn’t limit unsafe outputs proactively and requires additional effort for feedback handling.

Databricks Reference:"Proactive guardrails are preferred over user-driven monitoring"("Databricks Generative AI Engineer Guide").

Conclusion: Option A (Llama Guard on Foundation Model API) is the least-effort, most effective approach, leveraging Databricks’ infrastructure for seamless safety integration.

When working within a Databricks notebook, several high-level SDKs are "Databricks-aware." TheMLflow Deployments SDK(C) and theDatabricks Python SDK(D) are designed to automatically look for the DATABRICKS_HOST and DATABRICKS_TOKEN environment variables provided by the notebook context. TheOpenAI client(A), when configured for Databricks via Mosaic AI Gateway, also typically handles authentication via workspace integration in recent versions. However, theREST API via the requests library(B) is a generic Python HTTP client. It has no intrinsic knowledge of the Databricks environment. To use it, an engineer must manually extract the token (e.g., via dbutils.notebook.entry_point...) and explicitly pass it in the Authorization: Bearer header of the request. Without this manual step, the requests library will fail with a 401 Unauthorized error.

To optimize forcode generation performanceandhosting costs, a Generative AI engineer must look at specific metrics.

Big Code Models Leaderboard (A):This is the industry-standard benchmark for code-specific LLMs (like StarCoder or CodeLlama). It measures performance on tasks like HumanEval and MBPP, providing a direct indicator of how well the model handles programming logic.

Number of model parameters (B):This is the primary driver of hosting costs. Larger models (e.g., 70B) require more GPU memory (VRAM) and more expensive compute instances (like A100s/H100s) than smaller models (e.g., 7B or 13B). To minimize costs, the team should look for the smallest model that achieves a high score on the Big Code Leaderboard.

Note: MTEB (C) is for embeddings, and Chatbot Arena (D) is for general-purpose chat, neither of which is the primary metric for specialized code generation fine-tuning.

Problem Context: When assessing the safety and effectiveness of LLM outputs in a translation use case, it is essential to ensure that the translations accurately and relevantly convey the intended message. The evaluation should focus on how well the LLM understands and processes different languages and contexts.

Explanation of Options:

Option A: The ability to generate responses in code– This is not relevant to translation quality or safety.

Option B: The similarity to the previous language– While ensuring that translations preserve the original's intent is important, this doesn't directly address the overall quality or safety of the translation.

Option C: The latency of the response and the length of text generated– These operational metrics are less critical in assessing the qualitative aspects of translation safety.

Option D: The accuracy and relevance of the responses– This is crucial in translation to ensure that the translated content is true to the original in meaning and appropriateness. Accuracy and relevance directly impact the effectiveness and safety of translations, especially in sensitive or nuanced contexts.

Thus,Option Dis the most important indicator when evaluating the safety of LLM outputs in translation, focusing on the core aspects that determine the utility and trustworthiness of translated content.

Problem Context: After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.

Explanation of Options:

Option A: Use a smaller embedding model to generate– This wouldn't necessarily address the issue of prompt size exceeding the model’s token limit.

Option B: Reduce the maximum output tokens of the new model– This option affects the output length, not the size of the input being too large.

Option C: Decrease the chunk size of embedded documents– This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model's context length limitations.

Option D: Reduce the number of records retrieved from the vector database– By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.

Option E: Retrain the response generating model using ALiBi– Retraining the model is contrary to the stipulation not to change the response generating model.

OptionsCandDare the most effective solutions to manage the model’s shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.

The task requires an open generative AI model for a transcription (speech-to-text) task where speed is essential. Let’s assess the options based on their suitability for transcription and performance characteristics, referencing Databricks’ approach to model selection.

Option A: Llama-2-70b-chat-hf

Llama-2 is a text-based LLM optimized for chat and text generation, not speech-to-text. It lacks transcription capabilities.

Databricks Reference:"Llama models are designed for natural language generation, not audio processing"("Databricks Model Catalog").

Option B: MPT-30B-Instruct

MPT-30B is another text-based LLM focused on instruction-following and text generation, not transcription. It’s irrelevant for speech-to-text tasks.

Databricks Reference: No specific mention, but MPT is categorized under text LLMs in Databricks’ ecosystem, not audio models.

Option C: DBRX

DBRX, developed by Databricks, is a powerful text-based LLM for general-purpose generation. It doesn’t natively support speech-to-text and isn’t optimized for transcription.

Databricks Reference:"DBRX excels at text generation and reasoning tasks"("Introducing DBRX," 2023)—no mention of audio capabilities.

Option D: whisper-large-v3 (1.6B)

Whisper, developed by OpenAI, is an open-source model specifically designed for speech-to-text transcription. The “large-v3” variant (1.6 billion parameters) balances accuracy and efficiency, with optimizations for speed via quantization or deployment on GPUs—key for the application’s requirements.

Databricks Reference:"For audio transcription, models like Whisper are recommended for their speed and accuracy"("Generative AI Cookbook," 2023). Databricks supports Whisper integration in its MLflow or Lakehouse workflows.

Conclusion: OnlyD. whisper-large-v3is a speech-to-text model, making it the sole suitable choice. Its design prioritizes transcription, and its efficiency (e.g., via optimized inference) meets the speed requirement, aligning with Databricks’ model deployment best practices.

To mitigate the issue of the LLM including explanations of how summaries are generated in its output, the best approach is to adjust the training or prompt structure. Here’s why Option D is effective:

Few-shot Learning: By providing specific examples of how the desired output should look (i.e., just the summary without explanation), the model learns the preferred format. This few-shot learning approach helps the model understand not only what content to generate but also how to format its responses.

Prompt Engineering: Adjusting the user prompt to specify the desired output format clearly can guide the LLM to produce summaries without additional explanatory text. Effective prompt design is crucial in controlling the behavior of generative models.

Why Other Options Are Less Suitable:

A: While technically feasible, splitting the output by newline and truncating could lead to loss of important content or create awkward breaks in the summary.

B: Tuning chunk sizes or changing embedding models does not directly address the issue of the model's tendency to generate explanations along with summaries.

C: Revisiting document ingestion logic ensures accurate source data but does not influence how the model formats its output.

By using few-shot examples and refining the prompt, the engineer directly influences the output format, making this approach the most targeted and effective solution.

Model inversion attacks occur when an attacker uses the model's outputs to reconstruct the sensitive training data used during the fine-tuning process. To mitigate this in a public-facing application, implementingAI Guardrailsis the most relevant strategy. Guardrails act as a programmable "filter" between the LLM and the end-user. They can be configured to detect if a model's response contains patterns that look like proprietary training data or PII (Personally Identifiable Information). While ACLs (B) and ABAC (D) protect the model'sinfrastructure(who can invoke the API), they do not inspect thecontentof the output, which is where the inversion attack actually manifests. Databricks provides integrated guardrail capabilities (via Mosaic AI Gateway) specifically to enforce compliance and prevent the leakage of sensitive internal knowledge that may have been baked into the model weights during fine-tuning.

The task at hand is to improve the LLM’s ability to generate poem-like article summaries with the desired tone and style. Using aneutralizerto normalize the tone and style of the underlying documents (option B) will not help improve the LLM’s ability to generate the desired poetic style. Here’s why:

Neutralizing Underlying Documents:A neutralizer aims to reduce or standardize the tone of input data. However, this contradicts the goal, which is to generate text with aspecific tone and style(like haikus). Neutralizing the source documents will strip away the richness of the content, making it harder for the LLM to generate creative, stylistic outputs like poems.

Why Other Options Improve Results:

A (Explicit Instructions in the Prompt): Directly instructing the LLM to generate text in a specific tone and style helps align the output with the desired format (e.g., haikus). This is a common and effective technique in prompt engineering.

C (Few-shot Examples): Providing examples of the desired output format helps the LLM understand the expected tone and structure, making it easier to generate similar outputs.

D (Fine-tuning the LLM): Fine-tuning the model on a dataset that contains examples of the desired tone and style is a powerful way to improve the model’s ability to generate outputs that match the target format.

Therefore, using a neutralizer (option B) isnotan effective method for achieving the goal of generating stylized poetic summaries.

The LLM classifies mushroom species accurately but includes unwanted reasoning text, and the engineer wants only the label. Let’s assess how to control output format effectively.

Option A: Use few shot prompting to instruct the model on expected output format

Few-shot prompting provides examples (e.g., input: description, output: label). It can work but requires crafting multiple examples, which is effort-intensive and less direct than a clear instruction.

Databricks Reference:"Few-shot prompting guides LLMs via examples, effective for format control but requires careful design"("Generative AI Cookbook").

Option B: Use zero shot prompting to instruct the model on expected output format

Zero-shot prompting relies on a single instruction (e.g., “Return only the label”) without examples. It’s simpler than few-shot but may not consistently enforce succinctness if the LLM’s default behavior is verbose.

Databricks Reference:"Zero-shot prompting can specify output but may lack precision without examples"("Building LLM Applications with Databricks").

Option C: Use zero shot chain-of-thought prompting to prevent a verbose output format

Chain-of-Thought (CoT) encourages step-by-step reasoning, which increases verbosity—opposite to the desired outcome. This contradicts the goal of label-only output.

Databricks Reference:"CoT prompting enhances reasoning but often results in detailed responses"("Databricks Generative AI Engineer Guide").

Option D: Use a system prompt to instruct the model to be succinct in its answer

A system prompt (e.g., “Respond with only the species label, no additional text”) sets a global instruction for the LLM’s behavior. It’s direct, reusable, and effective for controlling output style across queries.

Databricks Reference:"System prompts define LLM behavior consistently, ideal for enforcing concise outputs"("Generative AI Cookbook," 2023).

Conclusion: Option D is the most effective and straightforward action, using a system prompt to enforce succinct, label-only responses, aligning with Databricks’ best practices for output control.

Problem Context: The chatbot must handle various types of queries and intelligently route them to the appropriate responses or systems.

Explanation of Options:

Option A: Limiting the chatbot to only previous event information restricts its utility and does not meet the broader business requirements.

Option B: Having two separate chatbots could unnecessarily complicate user interaction and increase maintenance overhead.

Option C: Implementing a multi-step workflow where the chatbot first identifies the type of question and then routes it accordingly is the most efficient and scalable solution. This approach allows the chatbot to handle a variety of queries dynamically, improving user experience and operational efficiency.

Option D: Focusing solely on payments would not satisfy all the specified user interaction needs, such as inquiring about event details.

Option Coffers a comprehensive workflow that maximizes the chatbot’s utility and responsiveness to different user needs, aligning perfectly with the business requirements.

Problem Context: The Generative AI Engineer needs a prompt that will enable an LLM trained on customer call transcripts to classify and respond correctly regarding product availability. The desired response should clearly indicate whether a product is "In Stock" or "Out of Stock," and it should be formatted in a way that is structured and easy to parse programmatically, such as JSON.

Explanation of Options:

Option A: Respond with “In Stock” if the customer asks for a product. This prompt is too generic and does not specify how to handle the case when a product is not available, nor does it provide a structured output format.

Option B: This option is correctly formatted and explicit. It instructs the LLM to respond based on the availability mentioned in the customer call transcript and to format the response in JSON. This structure allows for easy integration into systems that may need to process this information automatically, such as customer service dashboards or databases.

Option C: Respond with “Out of Stock” if the customer asks for a product. Like option A, this prompt is also insufficient as it only covers the scenario where a product is unavailable and does not provide a structured output.

Option D: While this prompt correctly specifies how to respond based on product availability, it lacks the structured output format, making it less suitable for systems that require formatted data for further processing.

Given the requirements for clear, programmatically usable outputs,Option Bis the optimal choice because it provides precise instructions on how to respond and includes a JSON format example for structuring the output, which is ideal for automated systems or further data handling.

Building a basic LLM-enabled chat application with conversational capabilities, knowledge retrieval, and contextual memory requires specific components that work together to process queries, maintain context, and retrieve relevant information. Databricks’ Generative AI Engineer documentation outlines key components for such systems, particularly in the context of frameworks like LangChain or Databricks’ MosaicML integrations. Let’s evaluate the required components:

Understanding the Requirements:

Conversational capabilities: The app must generate natural, coherent responses.

Knowledge retrieval: It must access external or domain-specific knowledge.

Contextual memory: It must remember prior interactions in the conversation.

Databricks Reference:"A typical LLM chat application includes a memory component to track conversation history and a retrieval mechanism to incorporate external knowledge"("Databricks Generative AI Cookbook," 2023).

Evaluating the Options:

A. (Q): This appears incomplete or unclear (possibly a typo). Without further context, it’s not a valid component.

B. Vector Stores: These store embeddings of documents or knowledge bases, enabling semantic search and retrieval of relevant information for the LLM. This is critical for knowledge retrieval in a chat application.

Databricks Reference:"Vector stores, such as those integrated with Databricks’ Lakehouse, enable efficient retrieval of contextual data for LLMs"("Building LLM Applications with Databricks").

C. Conversation Buffer Memory: This component stores the conversation history, allowing the LLM to maintain context across multiple turns. It’s essential for contextual memory.

Databricks Reference:"Conversation Buffer Memory tracks prior user inputs and LLM outputs, ensuring context-aware responses"("Generative AI Engineer Guide").

D. External tools: These (e.g., APIs or calculators) enhance functionality but aren’t required for abasicchat app with the specified capabilities.

E. Chat loaders: These might refer to data loaders for chat logs, but they’re not a core chain component for conversational functionality or memory.

F. React Components: These relate to front-end UI development, not the LLM chain’s backend functionality.

Selecting the Two Required Components:

Forknowledge retrieval, Vector Stores (B) are necessary to fetch relevant external data, a cornerstone of Databricks’ RAG-based chat systems.

Forcontextual memory, Conversation Buffer Memory (C) is required to maintain conversation history, ensuring coherent and context-aware responses.

While an LLM itself is implied as the core generator, the question asks for chain components beyond the model, making B and C the minimal yet sufficient pair for a basic application.

Conclusion: The two required chain components areB. Vector StoresandC. Conversation Buffer Memory, as they directly address knowledge retrieval and contextual memory, respectively, aligning with Databricks’ documented best practices for LLM-enabled chat applications.

The task involves improving a Retrieval-Augmented Generation (RAG) application’s performance by experimenting with embedding models. The choice of embedding model impacts retrieval accuracy, which is critical for RAG systems. Let’s evaluate the options based on Databricks Generative AI Engineer best practices.

Option A: Pick an embedding model trained on related domain knowledge

Embedding models trained on domain-specific data (e.g., industry-specific corpora) produce vectors that better capture the semantics of the application’s context, improving retrieval relevance. For RAG, this is a key strategy to enhance performance.

Databricks Reference:"For optimal retrieval in RAG systems, select embedding models aligned with the domain of your data"("Building LLM Applications with Databricks," 2023).

Option B: Pick the most recent and most performant open LLM released at the time

LLMs are not embedding models; they generate text, not embeddings for retrieval. While recent LLMs may be performant for generation, this doesn’t address the embedding step in RAG. This option misunderstands the component being selected.

Databricks Reference: Embedding models and LLMs are distinct in RAG workflows:"Embedding models convert text to vectors, while LLMs generate responses"("Generative AI Cookbook").

Option C: Pick the embedding model ranked highest on the Massive Text Embedding Benchmark (MTEB) leaderboard hosted by HuggingFace

The MTEB leaderboard ranks models across general tasks, but high overall performance doesn’t guarantee suitability for a specific domain. A top-ranked model might excel in generic contexts but underperform on the engineer’s unique data.

Databricks Reference: General performance is less critical than domain fit:"Benchmark rankings provide a starting point, but domain-specific evaluation is recommended"("Databricks Generative AI Engineer Guide").

Option D: Pick an embedding model with multilingual support to support potential multilingual user questions

Multilingual support is useful only if the application explicitly requires it. Without evidence of multilingual needs, this adds complexity without guaranteed performance gains for the current use case.

Databricks Reference:"Choose features like multilingual support based on application requirements"("Building LLM-Powered Applications").

Conclusion: Option A is the best strategy because it prioritizes domain relevance, directly improving retrieval accuracy in a RAG system—aligning with Databricks’ emphasis on tailoring models to specific use cases.

Prompt chaining is a fundamental design pattern in LLM application development used to handle complexity. Instead of sending a single, massive, and highly complex prompt to an LLM—which often results in reasoning errors or hallucinations—chaining breaks the logic into a sequence of smaller, targeted steps. For example, a legal assistant might first chain a step to "identify the legal jurisdiction," followed by a step to "extract relevant statutes," and finally a step to "summarize the findings." This modularity improves reliability because each prompt has a narrower focus, making it easier for the model to follow instructions accurately. While it may actually increase latency (contradicting B) and cost (contradicting D) due to multiple API calls, the primary engineering benefit is the significant boost in the quality and robustness of the output. It also allows for intermediate validation and error handling between steps, which is impossible in a single-call architecture.

The task is to deploy an LLM application using Foundation Model APIs in a production environment while adhering to security best practices. Authentication is critical for securing access to Databricks resources, such as the Foundation Model API. Let’s evaluate the options based on Databricks’ security guidelines for production scenarios.

Option A: Use an access token belonging to service principals

Service principals are non-human identities designed for automated workflows and applications in Databricks. Using an access token tied to a service principal ensures that the authentication is scoped to the application, follows least-privilege principles (via role-based access control), and avoids reliance on individual user credentials. This is a security best practice for production deployments.

Databricks Reference:"For production applications, use service principals with access tokens to authenticate securely, avoiding user-specific credentials"("Databricks Security Best Practices," 2023). Additionally, the "Foundation Model API Documentation" states:"Service principal tokens are recommended for programmatic access to Foundation Model APIs."

Option B: Use a frequently rotated access token belonging to either a workspace user or a service principal

Frequent rotation enhances security by limiting token exposure, but tying the token to a workspace user introduces risks (e.g., user account changes, broader permissions). Including both user and service principal options dilutes the focus on application-specific security, making this less ideal than a service-principal-only approach. It also adds operational overhead without clear benefits over Option A.

Databricks Reference:"While token rotation is a good practice, service principals are preferred over user accounts for application authentication"("Managing Tokens in Databricks," 2023).

Option C: Use OAuth machine-to-machine authentication

OAuth M2M (e.g., client credentials flow) is a secure method for application-to-service communication, often using service principals under the hood. However, Databricks’ Foundation Model API primarily supports personal access tokens (PATs) or service principal tokens over full OAuth flows for simplicity in production setups. OAuth M2M adds complexity (e.g., managing refresh tokens) without a clear advantage in this context.

Databricks Reference:"OAuth is supported in Databricks, but service principal tokens are simpler and sufficient for most API-based workloads"("Databricks Authentication Guide," 2023).

Option D: Use an access token belonging to any workspace user

Using a user’s access token ties the application to an individual’s identity, violating security best practices. It risks exposure if the user leaves, changes roles, or has overly broad permissions, and it’s not scalable or auditable for production.

Databricks Reference:"Avoid using personal user tokens for production applications due to security and governance concerns"("Databricks Security Best Practices," 2023).

Conclusion: Option A is the best choice, as it uses a service principal’s access token, aligning with Databricks’ security best practices for production LLM applications. It ensures secure, application-specific authentication with minimal complexity, as explicitly recommended for Foundation Model API deployments.

The error in the original snippet usually stems from the improper instantiation of the LLMChain or the incorrect call to the .generate() method. In LangChain, an LLMChain requires two primary components: anLLM(the engine) and aPrompt(the template). Option C provides the correct syntax: first, the PromptTemplate is defined with the correct input_variables. Second, the OpenAI model is instantiated. Third, the LLMChain binds the model and the prompt together. Finally, the .generate() method expects a list of dictionaries, where each dictionary represents a set of inputs for the prompt variables. Options A, B, and D in the original image contain syntax errors such as passing the variable directly into the chain initialization or missing the dictionary list format required by the standard LangChain API for batch-like generation.