Amazon Web Services Data-Engineer-Associate Dumps

 Using an S3 bucket that is in the same AWS Region where the company runs Athena queries can improve query performance by reducing data transfer latency and costs. Preprocessing the .csv data to Apache Parquet format can also improve query performance by enabling columnar storage, compression, and partitioning, which can reduce the amount of data scanned and fetched by the query. These solutions can optimize the storage solution for the POC test without requiring much effort or changes to the existing data pipeline. The other solutions are not optimal or relevant for this requirement. Adding a randomized string to the beginning of the keys in Amazon S3 can improve the throughput across partitions, but it can also make the data harder to query and manage. Using an S3 bucket that is in the same account that uses Athena to query the data does not have any significant impact on query performance, as long as the proper permissions are granted. Preprocessing the .csv data to JSON format does not offer any benefits over the .csv format, as both are row-based and verbose formats that require more data scanning and fetching than columnar formats like Parquet. References:

Best Practices When Using Athena with AWS Glue

Optimizing Amazon S3 Performance

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

 Partitioning the data in the S3 bucket can improve the performance of AWS Glue jobs by reducing the amount of data that needs to be scanned and processed. By organizing the data by year, month, and day, the AWS Glue job can use partition pruning to filter out irrelevant data and only read the data that matches the query criteria. This can speed up the data processing and reduce the cost of running the AWS Glue job. Increasing the AWS Glue instance size by scaling up the worker type can also improve the performance of AWS Glue jobs by providing more memory and CPU resources for the Spark execution engine. This can help the AWS Glue job handle larger data sets and complex transformations more efficiently. The other options are either incorrect or irrelevant, as they do not affect the performance of the AWS Glue jobs. Converting the AWS Glue schema to the DynamicFrame schema class does not improve the performance, but rather provides additional functionality and flexibility for data manipulation. Adjusting the AWS Glue job scheduling frequency does not improve the performance, but rather reduces the frequency of data updates. Modifying the IAM role that grants access to AWS Glue does not improve the performance, but rather affects the security and permissions of the AWS Glue service. References:

Optimising Glue Scripts for Efficient Data Processing: Part 1 (Section: Partitioning Data in S3)

Best practices to optimize cost and performance for AWS Glue streaming ETL jobs (Section: Development tools)

Monitoring with AWS Glue job run insights (Section: Requirements)

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 133)

The data engineer needs to ensure that the data in an Amazon EMR cluster is encrypted both at rest and in transit. The data in Amazon S3 is already encrypted using an AWS KMS key. To meet the requirements, the most suitable solution is to create anEMR security configurationthat specifies the correct KMS key for at-rest encryption and use thePEM filefor in-transit encryption.

Option C: Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.This option configures encryption for both data at rest (using KMS keys) and data in transit (using the PEM file for SSL/TLS encryption). This approach ensures that data is fully protected during storage and transfer.

Options A, B, and D either involve creating unnecessary additional security configurations or make inaccurate assumptions about the way encryption configurations are attached.

The ecommerce company wants to migrate its data pipelines into the AWS Cloud without managing servers, and the solution must orchestrate Python and Bash scripts without refactoring code.Amazon Managed Workflows for Apache Airflow (Amazon MWAA)is the most suitable solution for this scenario.

Option B: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)MWAA is a managed orchestration service that supportsPython and Bash scriptsvia Directed Acyclic Graphs (DAGs) for workflows. It is a serverless, managed version of Apache Airflow, which is commonly used for orchestrating complex data workflows, making it an ideal choice for migrating existing pipelines without refactoring. It supports Python, Bash, and other scripting languages, and the company would not need to manage the underlying infrastructure.

Other options:

AWS Lambda (Option A)is more suited for event-driven workflows but would require breaking down the pipeline into individual Lambda functions, which may require refactoring.

AWS Step Functions (Option C)is good for orchestration but lacks native support for Python and Bash without using Lambda functions, and it may require code changes.

AWS Glue (Option D)is an ETL service primarily for data transformation and not suitable for orchestrating general scripts without modification.

In Amazon Redshift, theSUPERdata type is designed specifically to handle semi-structured data like JSON, Parquet, ORC, and others. By using the SUPER data type, Redshift can ingest and query JSON data without requiring complex data flattening processes, thus reducing the amount of preprocessing required before loading the data. TheSUPERdata type also works seamlessly withRedshift Spectrum, enabling complex queries that can combine both structured and semi-structured datasets, which aligns with the company’s need to use combined datasets to identify potential new customers.

Using the SUPER data type also allows forautomatic parsing and query processingof nested data structures through Amazon Redshift'sPARTITION BYandJSONPATH expressions, which makes this option the most efficient approach with the least effort involved. This reduces the overhead associated with using tools like AWS Glue or Lambda for data transformation.

 This solution meets the requirements of implementing real-time analytics capabilities with the least operational overhead. By creating an external schema in Amazon Redshift, you can access the data from Kinesis Data Streams using SQL queries without having to load the data into the cluster. By creating a materialized view on top of the stream, you can store the results of the query in the cluster and make them available for analysis. By setting the materialized view to auto refresh, you can ensure that the view is updated with the latest data from the stream at regular intervals. This way, you can derive near real-time insights by using existing BI and analytics tools. References:

Amazon Redshift streaming ingestion

Creating an external schema for Amazon Kinesis Data Streams

Creating a materialized view for Amazon Kinesis Data Streams

To achieve the highest throughput and efficiently use cluster resources while loading data into an Amazon Redshift cluster, the optimal approach is to use asingle COPY commandthat ingests data in parallel.

Option D: Use a single COPY command to load the data into the Redshift cluster.TheCOPY commandis designed to load data from multiple files in parallel into a Redshift table, using all the cluster nodes to optimize the load process. Redshift is optimized for parallel processing, and a single COPY command can load multiple files at once, maximizing throughput.

Options A, B, and Ceither involve unnecessary complexity or inefficient approaches, such as using multiple COPY commands or INSERT statements, which are not optimized for bulk loading.

 A data mesh is an architectural framework that organizes data into domains and treats data as products that are owned and offered for consumption by different teams1. A data mesh requires a centralized layer for data governance and access control, as well as a distributed layer for data storage and analysis. AWS Glue can provide data catalogs and ETL operations for the data mesh, but it cannot provide data governance and access control by itself2. Therefore, the company needs to use another AWS service for this purpose. AWS Lake Formation is a service that allows you to create, secure, and manage data lakes on AWS3. It integrates with AWS Glue and other AWS services to provide centralized data governance and access control for the data mesh. Therefore, option E is correct.

For data storage and analysis, the company can choose from different AWS services depending on their needs and preferences. However, one of the benefits of a data mesh is that it enables data to be stored and processed in a decoupled and scalable way1. Therefore, using serverless or managed services that can handle large volumes and varieties of data is preferable. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any type of data. Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Therefore, option B is a good choice for data storage and analysis in a data mesh. Option A, C, and D are not optimal because they either use relational databases that are not suitable for storing diverse and unstructured data, or they require more management and provisioning than serverless services. References:

1: What is a Data Mesh? - Data Mesh Architecture Explained - AWS

2: AWS Glue - Developer Guide

3: AWS Lake Formation - Features

[4]: Design a data mesh architecture using AWS Lake Formation and AWS Glue

[5]: Amazon S3 - Features

[6]: Amazon Athena - Features

 Changing the distribution key to the table column that has the largest dimension will help to balance the load more evenly across all five compute nodes. The distribution key determines how the rows of a table are distributed among the slices of the cluster. If the distribution key is not chosen wisely, it can cause data skew, meaning some slices will have more data than others, resulting in uneven CPU load and query performance. By choosing the table column that has the largest dimension, meaning the column that has the most distinct values, as the distribution key, the data engineer can ensure that the rows are distributed more uniformly across the slices, reducing data skew and improving query performance.

The other options are not solutions that will meet the requirements. Option A, changing the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load. The sort key determines the order in which the rows of a table are stored on disk, which can improve the performance of range-restricted queries, but not the load balancing. Option C, upgrading the reserved node from ra3.4xlarge to ra3.16xlarge, will not maintain the current number of compute nodes, as it will increase the cost and the capacity of the cluster. Option D, changing the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the datadistribution or the CPU load either. The primary key is a constraint that enforces the uniqueness of the rows in a table, but it does not influence the data layout or the query optimization. References:

Choosing a data distribution style

Choosing a data sort key

Working with primary keys

The company needs to ingest tables from an on-premises Oracle database into a data lake on Amazon S3 inApache Parquet format. The most efficient solution, requiring the least manual effort, would be to useAWS Database Migration Service (DMS)for continuous data replication.

Option C: Create an AWS Database Migration Service (AWS DMS) task for ongoing replication. Set the Oracle database as the source. Set Amazon S3 as the target. Configure the task to write the data in Parquet format.AWS DMS can continuously replicate data from the Oracle database into Amazon S3, transforming it into Parquet format as it ingests the data. DMS simplifies the process by providing ongoing replication with minimal setup, and it automatically handles the conversion to Parquet format without requiring manualtransformations or separate jobs. This option is theleast effortsolution since it automates both the ingestion and transformation processes.

Other options:

Option A (Apache Sqoop on EMR)involves more manual configuration and management, including setting up EMR clusters and writing Sqoop jobs.

Option B (AWS Glue bookmark job)involves configuring Glue jobs, which adds complexity. While Glue supports data transformations, DMS offers a more seamless solution for database replication.

Option D (RDS and Lambda triggers)introduces unnecessary complexity by involving RDS and Lambda for a task that DMS can handle more efficiently.

Problem Analysis:

Input Requirements: Gaming app generates approximately20 KB data records, which must be ingested and made available tothree internal consumerswithdedicated throughput.

Key Requirements:

High throughput for ingestion from each device.

Dedicated processing bandwidth for each consumer.

Key Considerations:

Amazon Kinesis Data Streamssupports high-throughput ingestion withPutRecords APIfor batch writes.

TheEnhanced Fan-Outfeature providesdedicated throughputto each consumer, avoiding bandwidth contention.

This solution avoids bottlenecks and ensures optimal throughput for the gaming application and consumers.

Solution Analysis:

Option A: Kinesis Data Streams + Enhanced Fan-Out

PutRecords API is designed for batch writes, improving ingestion performance.

Enhanced Fan-Out allows each consumer to process the stream independently with dedicated throughput.

Option B: Data Firehose + Dedicated Throughput Request

Firehose is not designed for real-time stream processing or fan-out. It delivers data to destinations like S3, Redshift, or OpenSearch, not multiple independent consumers.

Option C: Data Firehose + Enhanced Fan-Out

Firehose does not support enhanced fan-out. This option is invalid.

Option D: Kinesis Data Streams + EC2 Instances

Hosting stream-processing applications on EC2 increases operational overhead compared to native enhanced fan-out.

Final Recommendation:

UseKinesis Data Streams with Enhanced Fan-Outfor high-throughput ingestion and dedicated consumer bandwidth.

When processingmany small files, the overhead of managing partitions can degrade performance. Rather than scaling workers manually, enablingAWS Glue auto scalingdynamically adjusts worker count based on resource requirements, which iscost-effectiveand addresses the performance concern.

“AWS Glue auto scaling automatically adds or removes workers based on the workload, which is efficient for handling performance bottlenecks when working with many small files.”

–Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Manually scaling up workers or increasing their size (A, B) can be more expensive and less adaptive.

The problem described requires identifying matching records even when there is no unique identifier. AWS Lake FormationFindMatchesis designed for this purpose. It uses machine learning (ML) to deduplicate and find matching records in datasets that do not share a common identifier.

D. Train and use the AWS Lake Formation FindMatches transform in the ETL job:

FindMatchesis a transform available in AWS Lake Formation that uses ML to discover duplicate records or related records that might not have a common unique identifier.

It can be integrated into an AWS Glue ETL job to perform deduplication or matching tasks.

FindMatches is highly effective in scenarios where records do not share a key, such as customer records from different sources that need to be merged or reconciled.

 QuickSight does not have access to the S3 bucket and QuickSight does not have access to decrypt S3 data are two possible factors that could cause the permissions-related errors. Amazon QuickSight is a business intelligence service that allows you to create and share interactive dashboards based on various data sources, including Amazon Athena. Amazon Athena is a serverless query service that allows you to analyze data stored in Amazon S3 using standard SQL. To use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket, you need to grant QuickSight access to both Athena and S3, as well as any encryption keys that are used to encrypt the S3 data. If QuickSight does not have access to the S3 bucket or the encryption keys, it will not be able to read the data from Athena and display it on the dashboard, resulting in an error message that indicates insufficient permissions.

The other options are not factors that could cause the permissions-related errors. Option A, there is no connection between QuickSight and Athena, is not a factor, as QuickSight supports Athena as a native data source, and you can easily create a connection between them using the QuickSight console or the API. Option B, the Athena tables are not cataloged, is not a factor, as QuickSight can automatically discover the Athena tables that are cataloged in the AWS Glue Data Catalog, and you can also manually specify the Athena tables that are not cataloged. Option E, there is no IAM role assigned to QuickSight, is not a factor, as QuickSight requires an IAM role to access any AWS data sources, including Athena and S3, and you can create and assign an IAM role to QuickSight using the QuickSight console or the API. References:

Using Amazon Athena as a Data Source

Granting Amazon QuickSight Access to AWS Resources

Encrypting Data at Rest in Amazon S3

The company wants to use Amazon Kinesis Data Firehose to transform CSV files intoJSONformat and store the files inApache Parquetformat with the least development effort.

Option B: Use Kinesis Data Firehose to convert the CSV files to JSON and to store the files in Parquet format.Kinesis Data Firehose supports data format conversion natively, including converting incoming CSV data to JSON format and storing the resulting files in Parquet format in Amazon S3. This solution requires the least development effort because it uses built-in transformation features of Kinesis Data Firehose.

Other options (A, C, D) involve invoking AWS Lambda functions, which would introduce additional complexity and development effort compared to Kinesis Data Firehose’s native format conversion capabilities.

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that perform custom logic and integrate with other AWS services, such as API Gateway. Lambda automatically scales your application by running code in response to each trigger. You pay only for the compute time you consume1.

Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. You can use ECS to deploy, manage, andscale Docker containers using either Amazon EC2 instances or AWS Fargate, a serverless compute engine for containers2.

Amazon EKS is a fully managed Kubernetes service that allows you to run Kubernetes clusters on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use EKS to deploy, manage, and scale containerized applications using Kubernetes on AWS3.

The solution that meets the requirements with the least operational overhead is to create an AWS Lambda Python function with provisioned concurrency. This solution has the following advantages:

It does not require you to provision, manage, or scale any servers or clusters, as Lambda handles all the infrastructure for you. This reduces the operational complexity and cost of running your code.

It allows you to write your Python script as a Lambda function and integrate it with API Gateway using a simple configuration. API Gateway can invoke your Lambda function synchronously or asynchronously, and return the results to the frontend website.

It ensures that your Lambda function is ready to respond to API requests without any cold start delays, by using provisioned concurrency. Provisioned concurrency is a feature that keeps your function initialized and hyper-ready to respond in double-digit milliseconds. You can specify the number of concurrent executions that you want to provision for your function.

Option A is incorrect because it requires you to deploy a custom Python script on an Amazon ECS cluster. This solution has the following disadvantages:

It requires you to provision, manage, and scale your own ECS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.

It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.

It requires you to configure your ECS cluster to integrate with API Gateway, either using an Application Load Balancer or a Network Load Balancer. This adds another layer of complexity to your architecture.

Option C is incorrect because it requires you to deploy a custom Python script that can integrate with API Gateway on Amazon EKS. This solution has the following disadvantages:

It requires you to provision, manage, and scale your own EKS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.

It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.

It requires you to configure your EKS cluster to integrate with API Gateway, either using an Application Load Balancer, a Network Load Balancer, or a service of type LoadBalancer. This adds another layer of complexity to your architecture.

Option D is incorrect because it requires you to create an AWS Lambda function and ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events. This solution has the following disadvantages:

It does not guarantee that your Lambda function will always be warm, as Lambda may scale down your function if it does not receive any requests for a long period of time. This may cause cold start delays when your function is invoked by API Gateway.

It incurs unnecessary costs, as you pay for the compute time of your Lambda function every time it is invoked by the EventBridge rule, even if it does not perform any useful work1.

TheChoicestate type in AWS Step Functions is designed to perform branching logic, i.e., routing execution to different paths based on conditions in the input data.

“The Step FunctionsChoicestate lets you branch the execution flow depending on values in the state’s input. This allows you to run different processing logic based on dynamic conditions like values in the input JSON.”

–Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This makesChoicethe correct answer for content-driven conditional workflows.

In Amazon Redshift, acompound sort keyis designed to optimize the performance of queries that use filtering and join conditions on the columns in the sort key. A compound sort key orders the data based on the first column, followed by the second, and so on. In the scenario given, the compound sort key consists ofRegion ID,Department ID, andRole ID. Therefore, queries that filter on the leading columns of the sort key are more likely to benefit from this order.

Option B: "Select * from Employee where Region ID='North America' and Department ID=20;"This query will perform well because it uses both theRegion IDandDepartment ID, which are the first two columns of the compound sort key. The order of the columns in the WHERE clause matches the order in the sort key, thus allowing the query to scan fewer rows and improve performance.

Option C: "Select * from Employee where Department ID=20 and Region ID='North America';"This query also benefits from the compound sort key because it includesboth Region ID and Department ID, which are the first two columns in the sort key. Although the order in the WHERE clause does not match exactly, Amazon Redshift will still leverage the sort key to reduce the amount of data scanned, improving query speed.

Options A, D, and Eare less optimal because they do not utilize the sort key as effectively:

Option Aonly filters by theRegion ID, which may still use the sort key but does not take full advantage of the compound nature.

Option Duses onlyRole ID, the last column in the compound sort key, which will not benefit much from sorting since it is the third key in the sort order.

Option Efilters onRegion IDandRole IDbut skips theDepartment IDcolumn, making it less efficient for the compound sort key.

The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. References:

Using the Amazon Redshift Data API

Using Amazon EventBridge with Amazon Redshift

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift

The company wants to gather information aboutincomplete multipart uploadsandoutdated versionsin its Amazon S3 buckets to optimize storage costs.

Option B: Use Amazon S3 Inventory configurations reports to gather the information.S3 Inventoryprovides reports that can list incomplete multipart uploads and versions of objects stored in S3. It offers an easy, automated way to track object metadata across buckets, including data necessary for cost optimization, without manual effort.

Options A (AWS CLI), C (S3 Storage Lens), and D (usage reports) either do not specifically gather the required information about incomplete uploads and outdated versions or require more manual intervention.

The NEST TO MAP transformation allows you to combine multiple columns into a single column that contains a JSON object with key-value pairs. This is the easiest way to achieve the desired format for the physical address data, as you can simply select the columns to nest and specify the keys for each column. The NEST TO ARRAY transformation creates a single column that contains an array of values, which is not the same as the JSON object format. The PIVOT transformation reshapes the data by creating new columns from unique values in a selected column, which is not applicable for this use case. Writing a Lambda function in Python requires more coding effort than using AWS Glue DataBrew, which provides a visual and interactive interface for data transformations. References:

7 most common data preparation transformations in AWS Glue DataBrew (Section: Nesting and unnesting columns)

NEST TO MAP - AWS Glue DataBrew (Section: Syntax)

To prevent unintentional file deletions and meet the requirement with minimal operational overhead, enablingS3 Versioningis the best solution.

S3 Versioning:

S3 Versioning allows multiple versions of an object to be stored in the same S3 bucket. When a file is deleted or overwritten, S3 preserves the previous versions, which means you canrecoverfrom accidental deletions or modifications.

Enabling versioning requires minimal overhead, as it is abucket-level settingand does not require additional backup processes or data replication.

Users can recover specific versions of files that were unintentionally deleted, meeting the needs of the data engineer to avoid accidental data loss.

 Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB offers two capacity modes for throughput capacity: provisioned and on-demand. In provisioned capacity mode, you specify the number of read and write capacity units per second that you expect your application to require. DynamoDB reserves the resources to meet your throughput needs with consistent performance. In on-demand capacity mode, you pay per request and DynamoDB scales the resources up and down automatically based on the actual workload. On-demand capacity mode is suitable for unpredictable workloads that can vary significantly over time1.

The solution that meets the requirements in the most cost-effective way is to use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times and lower capacity during off-peak times. This solution has the following advantages:

It allows you to optimize the cost and performance of your DynamoDB table by adjusting the provisioned capacity according to your predictable workload patterns. You can use scheduled scaling to specify the date and time for the scaling actions, and the new minimum and maximum capacity limits. For example, you can schedule higher capacity for every Monday morning and lower capacity for weekends2.

It enables you to take advantage of the lower cost per unit of provisioned capacity mode compared to on-demand capacity mode. Provisioned capacity mode charges a flat hourly rate for the capacity you reserve, regardless of how much you use. On-demand capacity mode charges for each read and write request you consume, with no minimum capacity required. For predictable workloads, provisioned capacity mode can be more cost-effective than on-demand capacity mode1.

It ensures that your application performs consistently during peak usage times by having enough capacity to handle the increased load. You can also use auto scaling to automatically adjust the provisioned capacity based on the actual utilization of your table, and set a target utilization percentage for your table or global secondary index. This way, you can avoid under-provisioning or over-provisioning your table2.

Option A is incorrect because it suggests increasing the provisioned capacity to the maximum capacity that is currently present during peak load times. This solution has the following disadvantages:

It wastes money by paying for unused capacity during off-peak times. If you provision the same high capacity for all times, regardless of the actual workload, you are over-provisioning your table and paying for resources that you don’t need1.

It does not account for possible changes in the workload patterns over time. If your peak load times increase or decrease in the future, you may need to manually adjust the provisioned capacity to match the new demand. This adds operational overhead and complexity to your application2.

Option B is incorrect because it suggests dividing the table into two tables and provisioning each table with half of the provisioned capacity of the original table. This solution has the following disadvantages:

It complicates the data model and the application logic by splitting the data into two separate tables. You need to ensure that the queries are evenly distributed across both tables, and that the data is consistent and synchronized between them. This adds extra development and maintenance effort to your application3.

It does not solve the problem of adjusting the provisioned capacity according to the workload patterns. You still need to manually or automatically scale the capacity of each table based on the actual utilization and demand. This may result in under-provisioning or over-provisioning your tables2.

Option D is incorrect because it suggests changing the capacity mode from provisioned to on-demand. This solution has the following disadvantages:

It may incur higher costs than provisioned capacity mode for predictable workloads. On-demand capacity mode charges for each read and write request you consume, with no minimum capacity required. For predictable workloads, provisioned capacity mode can be more cost-effective than on-demand capacity mode, as you can reserve the capacity you need at a lower rate1.

It may not provide consistent performance during peak usage times, as on-demand capacity mode may take some time to scale up the resources to meet the sudden increase in demand. On-demand capacity mode uses adaptive capacity to handle bursts of traffic, but it may not be able to handle very large spikes or sustained high throughput. In such cases, you may experience throttling or increased latency.

Amazon EC2 instances can use two types of storage volumes: instance store volumes and Amazon EBS volumes. Instance store volumes are ephemeral, meaning they are only attached to the instance for the duration of its life cycle. If the instance is stopped, terminated, or fails, the data on the instance store volume is lost. Amazon EBS volumes are persistent, meaning they can be detached from the instance and attached to another instance, and the data on the volume is preserved. To meet the requirement of persisting the data even if the EC2 instances are terminated, the data engineer must use Amazon EBS volumes to store the application data. The solution is to launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume, which is the default option for most AMIs. Then, the data engineer must attach an Amazon EBS volume to each instance and configure the application to write the data to the EBS volume. This way, the data will be saved on the EBS volume and can be accessed by another instance if needed. The data engineer can apply the default settings to the EC2 instances, as there is no need to modify the instance type, security group, or IAM role for this solution. The other options are either not feasible or not optimal. Launching new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data (option A) or by using an AMI that is backed by a root Amazon EBS volume that contains the application data (option B) would not work, as the data on the AMI would be outdated and overwritten by the new instances. Attaching an additional EC2 instance store volume to contain the application data (option D) would not work, as the data on the instance store volume would be lost if the instance is terminated. References:

Amazon EC2 Instance Store

Amazon EBS Volumes

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.1: Amazon EC2

The performance issue in Amazon Redshift Spectrum queries arises due to the nature of CSV files, which are row-based storage formats. Spectrum is more optimized for columnar formats, which significantly improve performance by reducing the amount of data scanned. Also, partitioning data based on relevant columns like order date can further reduce the amount of data scanned, as queries can focus only on the necessary partitions.

A. Configure the third-party application to create the files in a columnar format:

Columnar formats(like Parquet or ORC) store data in a way that is optimized for analytical queries because they allow queries to scan only the columns required, rather than scanning all columns in a row-based format like CSV.

Amazon Redshift Spectrum works much more efficiently with columnar formats, reducing the amount of data that needs to be scanned, which improves query performance.

Problem Analysis:

The company uses AWS Glue for ETL pipelines and must enforcedata quality checksduring pipeline execution.

The goal is to implement quality checks withminimal implementation effort.

Key Considerations:

AWS Glue provides anEvaluate Data Quality transformthat allows for defining quality checks directly in the pipeline.

DQDL (Data Quality Definition Language)simplifies the process by allowing declarative rule definitions.

Solution Analysis:

Option A: SQL Transform

SQL queries can implement rules but require manual effort for each rule and do not integrate natively with Glue.

Option B: Evaluate Data Quality Transform + DQDL

AWS Glue’s built-inEvaluate Data Quality transformis designed for this use case.

Allows defining thresholds and rules in DQDL with minimal coding effort.

Option C: Custom Transform with PyDeequ

PyDeequ is a powerful library but adds unnecessary complexity compared to Glue’s native features.

Option D: Custom Transform with Great Expectations

Similar to PyDeequ, Great Expectations adds operational complexity and external dependencies.

Final Recommendation:

Use theEvaluate Data Quality transformwithDQDLto implement data quality rules in AWS Glue pipelines.

The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog that provides a central metadata repository for various data sources and formats. You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMR andAmazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWS Glue crawlers to automatically discover and catalog the metadata from Hive, and use the AWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as the Hive metastore. The other options are either more complex or require additional steps, such as setting up Apache Ranger for security, managing a Hive metastore on an EMR cluster or an RDS instance, or migrating the metadata manually. References:

Using the AWS Glue Data Catalog as the metastore for Hive (Section: Specifying AWS Glue Data Catalog as the metastore)

Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue Data Catalog)

AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadata from an existing Hive metastore)

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 131)

To migrate data from an Amazon RDS for PostgreSQL DB instance in theeu-east-1Region (Account_A) to an Amazon Redshift cluster in theeu-west-1Region (Account_B), AWS DMS needs a replication instance located in the target region (in this case,eu-west-1) to facilitate the data transfer between regions.

Option A: Set up an AWS DMS replication instance in Account_B in eu-west-1.Placing theDMS replication instancein the target account and region (Account_B in eu-west-1) is the most efficient solution. The replication instance can connect to the source RDS PostgreSQL in eu-east-1 and migrate the data tothe Redshift cluster in eu-west-1. This setup ensures data is replicated across AWS accounts and regions.

Options B, C, and D place the replication instance in either the wrong account or region, which increases complexity without adding any benefit.

Apache Parquet is a columnar storage format that is much more space-efficient than row-based formats like CSV, especially for analytics workloads. Transforming data from CSV to Parquet significantly reduces storage costs and improves query performance. According to the study guide:

“Parquet is a columnar storage file format that is optimized for use with analytics workloads, providing efficient storage and fast query performance.”

–Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

By switching to Parquet, the company can reduce both storage size and retrieval times, making it the optimal choice for cost-effective data analysis.

Problem Analysis:

The company ingests geolocation records (10 bytes each) at 10,000 records per second intoKinesis Data Streams.

Data transmission delays are acceptable, but the solution must maximize throughput efficiency.

Key Considerations:

TheKinesis Producer Library (KPL)batches records and uses aggregation to optimize shard throughput.

Efficiently handles high-throughput scenarios with minimal operational overhead.

Solution Analysis:

Option A: Kinesis Agent

Designed for file-based ingestion; not optimized for geolocation records.

Option B: KPL

Aggregates records into larger payloads, significantly improving shard throughput.

Suitable for applications generating small, high-frequency records.

Option C: Kinesis Firehose

Firehose is for delivery to destinations like S3 or Redshift and is not optimized for direct ingestion to Kinesis Data Streams.

Option D: Kinesis SDK

The SDK lacks advanced features like aggregation, resulting in lower throughput efficiency.

Final Recommendation:

UseKinesis Producer Library (KPL)for its built-in aggregation and batching capabilities.

Step 1: Log Collection (FluentBit and CloudWatch)

Option Asuggests using FluentBit to collect logs and OpenTelemetry to collect traces.

FluentBitis a lightweight log processor that integrates withAmazon EKSto collect and forward logs from Kubernetes clusters. It is widely used with minimal overhead, making it an ideal choice for log collection in this scenario. FluentBit is also natively compatible with AWS services.

OpenTelemetryis a popular framework to collect traces from distributed applications. It provides observability, making it easier to monitor microservices.

This combination allows you to effectively gather both logs and traces with minimal setup and configuration, aligning with the goal of least development effort.

CloudWatchcan be used to monitor logs (Option B and C). However, for applications that need more custom and fine-grained control over logging mechanisms, FluentBit and OpenTelemetry are the preferred choice in microservice environments.

Step 2: Log and Trace Correlation (Amazon OpenSearch)

Option D(Amazon OpenSearch) is specifically designed to search, analyze, and visualize logs, metrics, and traces in real-time. OpenSearch allows you tocorrelate logs and traceseffectively.

WithAmazon OpenSearch, you can set up dashboards that help in visualizing both logs and traces together, which assists in identifying any failure points across the entire request flow.

It offers integrations withFluentBitandOpenTelemetry, ensuring that both logs from the EKS cluster and application traces are centrally collected, stored, and correlated without additional heavy development.

Step 3: Why Other Options Are Not Suitable

Option B (Amazon Kinesis)is designed for real-time data streaming and analytics but is not as well-suited for tracing microservice requests and logs correlation compared to OpenSearch.

Option C (Amazon MSK)provides a managed Kafka streaming service, but this adds complexity when trying to integrate and correlate logs and traces from a microservice environment. Setting up Kafka requires more development effort compared to using FluentBit and OpenTelemetry.

Option E (AWS Glue)is primarily an ETL (Extract, Transform, Load) service. While Glue is powerful for data processing, it is not a native tool for log and trace correlation, and using it would add unnecessary complexity for this use case.

Conclusion:

To meet the requirements with the least development effort:

UseFluentBitfor log collection andOpenTelemetryfor tracing (Option A).

Correlate logs and traces usingAmazon OpenSearch(Option D).

This approach leverages AWS-native services designed for seamless integration with microservices hosted on Amazon EKS and ensures effective monitoring with minimal overhead.

AWS Database Migration Service (AWS DMS) is a service that helps you migrate databases to AWS quickly and securely. You can use AWS DMS to migrate the Hive metastore from the on-premises Hadoop clusters into Amazon S3, which is a highly scalable, durable, and cost-effective object storage service. AWS Glue Data Catalog is a serverless, managed service that acts as a central metadata repository for your data assets. You can use AWS Glue Data Catalog to scan the Amazon S3 bucket that contains the migrated Hive metastore and create a data catalog that is compatible with Apache Hive and other AWS services. This solution meets the requirements of migrating the data catalog into a persistent storage solution and using a serverless solution. This solution is also the most cost-effective, as it does not incur any additional charges for running Amazon EMR or Amazon Aurora MySQL clusters. The other options are either not feasible or not optimal. Configuring a Hive metastore in Amazon EMR (option B) or an external Hive metastore in Amazon EMR (option C) would require running and maintaining Amazon EMR clusters, which would incur additional costs and complexity. Using Amazon Aurora MySQL to store the company’s data catalog (option C) would also incur additional costs and complexity, as well as introduce compatibility issues with Apache Hive. Configuring a new Hive metastore in Amazon EMR (option D) would not migrate the existing data catalog, but create a new one, which would result in data loss and inconsistency. References:

Using AWS Database Migration Service

Populating the AWS Glue Data Catalog

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.2: AWS Glue Data Catalog

Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers theleast operational effortby avoiding complex data movement and transformation processes.

Amazon Athena Federated Queries:

Athena’s federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena’s support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.

The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.

The most cost-effective solution in this case is to apply a lifecycle policy to transition records to Amazon S3 Standard-IA storage after 30 days. Here’s why:

Amazon S3 Lifecycle Policies: Amazon S3 offers lifecycle policies that allow you to automatically transition objects between different storage classes to optimize costs. For data that is frequently accessed in the first 30 days and infrequently accessed after that, transitioning from theS3 Standard storage class to S3 Standard-Infrequent Access (S3 Standard-IA)after 30 days makes the most sense. S3 Standard-IA is designed for data that is accessed less frequently but stillneeds to be retained, offering lower storage costs than S3 Standard with a retrieval cost for access.

Cost Optimization: S3 Standard-IA offers a lower price per GB than S3 Standard. Since the data will be accessed infrequently after 30 days, using S3 Standard-IA will lower storage costs while still allowing for immediate retrieval when necessary.

Compliance with Regulations: Since the records need to be immediately accessible for the first 30 days, the use ofS3 Standardfor that period ensures compliance with regulatory requirements. After 30 days, transitioning toS3 Standard-IAcontinues to meet access requirements for infrequent access while reducing storage costs.

Alternatives Considered:

Option B (S3 Intelligent-Tiering): While S3 Intelligent-Tiering automatically moves data between access tiers based on access patterns, it incurs a small monthly monitoring and automation charge per object. It could be a viable option, but transitioning data toS3 Standard-IAdirectly would be more cost-effective since the pattern of access is well-known (frequent for 30 days, infrequent thereafter).

Option C (S3 Glacier Deep Archive): Glacier Deep Archive is the lowest-cost storage class, but it is not suitable in this case because the data needs to be accessed immediately within 30 days and on an infrequent basis thereafter. Glacier Deep Archive requires hours for data retrieval, which is not acceptable for infrequent access needs.

Option D (S3 Standard-IA for all records): UsingS3 Standard-IA for all recordswould result in higher costs for the first 30 days, as the data is frequently accessed. S3 Standard-IA incurs retrieval charges, making it less suitable for frequently accessed data.

AWS Data Exchange is a service that makes it easy to find, subscribe to, and use third-party data in the cloud. It provides a secure and reliable way to access and integrate data from various sources, such as data providers, public datasets, or AWS services. Using AWS Data Exchange, you can browse and subscribe to data products that suit your needs, and then use API calls or the AWS Management Console to export the data to Amazon S3, where you can use it with your existing analytics platform. This solution minimizes the effort and time required to incorporate third-party datasets, as you do not need to set up and manage data pipelines, storage, or access controls. You also benefit from the data quality and freshness provided by the data providers, who can update their data products as frequently as needed12.

The other options are not optimal for the following reasons:

B. Use API calls to access and integrate third-party datasets from AWS. This option is vague and does not specify which AWS service or feature is used to access and integrate third-party datasets. AWS offers a variety of services and features that can helpwith data ingestion, processing, and analysis, but not all of them are suitable for the given scenario. For example, AWS Glue is a serverless data integration service that can help you discover, prepare, and combine data from various sources, but it requires you to create and run data extraction, transformation, and loading (ETL) jobs, which can add operational overhead3.

C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. This option is not feasible, as AWS CodeCommit is a source control service that hosts secure Git-based repositories, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is a service that enables you to capture, process, and analyze data streams in real time, such as clickstream data, application logs, or IoT telemetry. It does not support accessing and integrating data from AWS CodeCommit repositories, which are meant for storing and managing code, not data .

D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). This option is also not feasible, as Amazon ECR is a fully managed container registry service that stores, manages, and deploys container images, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams does not support accessing and integrating data from Amazon ECR, which is meant for storing and managing container images, not data .

The company is facing performance issues loading data into Amazon Redshift because it is issuing separate COPY commands for each data file location. The most efficient way to increase the speed of data ingestion into Redshift without increasing the cost is to use amanifest file.

Option D: Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.A manifest file provides a list of all the data files, allowing the COPY command to load all files in parallel from different locations in Amazon S3. This significantly improves the loading speed without adding costs, as it optimizes the data loading process in a single COPY operation.

Other options (A, B, C) involve additional steps that would either increase the cost (provisioning clusters, using Glue, etc.) or do not address the core issue of needing a unified and efficient COPY process.

The most efficient and low-operational-overhead solution for ingesting data into Amazon Redshift from Amazon Kinesis Data Streams is to useAmazon Redshift streaming ingestion. This feature allows Redshift to directly ingest streaming data from Kinesis Data Streams and process it in real-time.

Amazon Redshift Streaming Ingestion:

Redshift supports native streaming ingestion from Kinesis Data Streams, allowing real-time data to be queried usingmaterialized views.

This solution reduces operational complexity because you don't need intermediary services like Amazon Kinesis Data Firehose or S3 for batch loading.