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Amazon Amazon-DEA-C01 Exam Prep
AWS Certified Data Engineer - Associate (Page 4 )

Updated On: 20-Aug-2026
Page 4 of 47
PDF with 366 Questions

A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.
Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)

  1. Configure AWS Glue triggers to run the ETL jobs every hour.
  2. Use AWS Glue DataBrew to clean and prepare the data for analytics.
  3. Use AWS Lambda functions to schedule and run the ETL jobs every hour.
  4. Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.
  5. Use the Redshift Data API to load transformed data into Amazon Redshift.

Answer(s): A,D

Explanation:

The correct answer is A D. Here's why:

A: Configure AWS Glue triggers to run the ETL jobs every hour: AWS Glue triggers are a native and straightforward way to schedule and execute Glue ETL jobs. They can be configured to run on a schedule (time-based), based on events (like the completion of another job), or on demand. Using Glue triggers directly addresses the requirement for hourly data updates with minimal operational overhead, as it's a managed feature of AWS Glue itself, requiring no additional services or custom code for scheduling. Lambda (option C) would introduce unnecessary complexity for a simple scheduled execution.
D: Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift: AWS Glue connections provide a centralized and managed way to store and manage connection information to various data sources, including Amazon RDS, MongoDB, and Amazon Redshift. This simplifies the ETL job configuration by allowing you to reference connections instead of hardcoding connection details in each job. This reduces the need to manually configure connections within each ETL script, leading to easier maintenance and reduced operational overhead.
Why other options are not suitable:
B: Use AWS Glue DataBrew to clean and prepare the data for analytics: While DataBrew can be used for data preparation, it's primarily focused on interactive data exploration and visual data transformations, which aren't as suitable for automated, scheduled ETL pipelines as Glue ETL jobs. It does not have the same programmatic flexibility and scaling capabilities as Glue ETL for this use case.
C: Use AWS Lambda functions to schedule and run the ETL jobs every hour: Using Lambda to schedule Glue jobs introduces additional complexity and overhead. You would need to manage the Lambda function, its execution role, and ensure its reliability. Glue triggers provide a more direct and managed approach to scheduling Glue jobs.
E: Use the Redshift Data API to load transformed data into Amazon Redshift: While the Redshift Data API can be used to load data, it's often better suited for executing SQL queries and interacting with Redshift rather than high-volume data loading within an ETL pipeline. Glue ETL jobs, especially with options like dynamicframes.toDF().write.format("redshift") , offer better performance and integration for loading data from other data sources. The Glue connector is also optimized for data loading into Redshift.
Supporting Documentation:
AWS Glue Triggers: https://docs.aws.amazon.com/glue/latest/dg/trigger-definition.html AWS Glue Connections: https://docs.aws.amazon.com/glue/latest/dg/connections-api.html AWS Glue DataBrew: https://aws.amazon.com/databrew/ Redshift Data API: https://docs.aws.amazon.com/redshift-data-api/latest/APIReference/Welcome.html



A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

  1. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.
  2. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
  3. Turn on concurrency scaling in the settings during the creation of any new Redshift cluster.
  4. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.

Answer(s): B

Explanation:

The correct answer is B: Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
Here's a detailed justification:
Amazon Redshift concurrency scaling automatically adds compute capacity to your Redshift cluster to handle increases in concurrent read and write queries. This ensures consistent performance even during peak demand. Concurrency scaling is not a cluster-wide setting enabled during cluster creation (option C) nor is it directly configured through a daily usage quota (option D). RA3 nodes are specifically designed to utilize concurrency scaling effectively.
Workload Management (WLM) allows you to prioritize and manage queries based on their importance. Concurrency scaling is configured at the WLM queue level. By enabling concurrency scaling for specific WLM queues, you allow Redshift to automatically spin up additional compute resources when queries assigned to that queue experience contention due to high concurrency. This distributes the workload across more resources, improving query performance. Redshift Serverless, mentioned in option A, is a different deployment option than a provisioned Redshift cluster using RA3 nodes.
While Redshift Serverless also offers concurrency scaling features, the context specifically refers to an existing Redshift cluster. Therefore, the focus should be on the settings within that cluster.
For more information, refer to the AWS documentation on Amazon Redshift concurrency scaling:
Amazon Redshift Concurrency Scaling Configuring workload management (WLM) for concurrency scaling



A data engineer must orchestrate a series of Amazon Athena queries that will run every day. Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

  1. Use an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
  2. Create an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the Athena query has finished using the Athena Boto3 get_query_execution API call. Configure the workflow to invoke the next query when the current query has finished running.
  3. Use an AWS Glue Python shell job and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
  4. Use an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running.
  5. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.

Answer(s): A,B

Explanation:

A: Use Lambda + start_query_execution (Correct)
Lambda can:
Programmatically start Athena queries
Use the Boto3 start_query_execution API
Return immediately after submission
This is:
Serverless
Very low cost
Simple to implement
However, Lambda alone cannot handle long polling beyond 15 minutes — which is why B is needed.
B: Use Step Functions with Wait + polling (Correct)
Step Functions can:
Orchestrate long-running workflows
Use a Wait state to periodically check status
Call get_query_execution
Trigger next query after previous completes
Benefits:
No Lambda timeout issue
Pay only for state transitions
Very cost-effective
Fully serverless
This is the ideal orchestration pattern for long-running Athena queries.



A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options. The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.
Which extract, transform, and load (ETL) service will meet these requirements?

  1. AWS Glue
  2. Amazon EMR
  3. AWS Lambda
  4. Amazon Redshift

Answer(s): B

Explanation:

The correct answer is B (Amazon EMR) because it best fits the requirements of migrating complex workloads utilizing Apache Pig, Oozie, Spark, HBase, and Flink to AWS while aiming for similar or better performance and reduced operational overhead.
Here's why:
Amazon EMR provides a managed Hadoop framework: EMR simplifies the setup, operation, and scaling of big data frameworks like Hadoop, Spark, HBase, and Flink. It directly supports the existing workloads utilizing these technologies. ( https://aws.amazon.com/emr/ ) Performance: EMR can leverage EC2 instances optimized for compute and memory, allowing for processing petabytes of data in seconds, mirroring the on-premises performance. Reduced Operational Overhead: EMR handles the underlying infrastructure, operating system patching, and framework updates, freeing the company from these tasks. Cost Optimization: EMR supports spot instances to reduce costs for fault-tolerant workloads. It also offers various instance types tailored for specific workloads. Suitable for complex workloads: EMR is designed for running complex, distributed data processing applications.
Now, let's analyze why the other options are less suitable:
AWS Glue: Glue is primarily a serverless ETL service focused on data cataloging, transformation, and loading.
While useful, it's not a direct replacement for the diverse processing capabilities of Spark, Flink, and HBase. Although Glue supports Spark, it might not be as performant or flexible for the company's specific use cases. AWS Lambda: Lambda is suitable for event-driven, serverless compute tasks, but it is not designed for large-scale data processing with frameworks like Spark or Flink. Its execution time limits and memory constraints make it unsuitable for petabyte-scale workloads. Amazon Redshift: Redshift is a data warehouse service, ideal for analytical queries and reporting. It is not a direct replacement for the processing frameworks the company currently uses and is more of a destination for processed data rather than an ETL platform in this context.
While Redshift can perform some transformations, it's not optimized for the complex operations performed by Spark or Flink.
Therefore, Amazon EMR is the most appropriate ETL service to meet the company's requirements for migrating their on-premises workloads to AWS while maintaining performance and reducing operational overhead, due to its native support for the technologies they are already using at scale.



A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII.
Which solution will meet this requirement with the LEAST operational effort?

  1. Use an Amazon Kinesis Data Firehose delivery stream to process the dataset. Create an AWS Lambda transform function to identify the PII. Use an AWS SDK to obfuscate the PII. Set the S3 data lake as the target for the delivery stream.
  2. Use the Detect PII transform in AWS Glue Studio to identify the PII. Obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
  3. Use the Detect PII transform in AWS Glue Studio to identify the PII. Create a rule in AWS Glue Data Quality to obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
  4. Ingest the dataset into Amazon DynamoDB. Create an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data. Use the same Lambda function to ingest the data into the S3 data lake.

Answer(s): B

Explanation:

Here's a detailed justification for why option B is the best solution, along with supporting explanations and links:
Option B utilizes the "Detect PII" transform within AWS Glue Studio for identifying and obfuscating Personally Identifiable Information (PII) directly within the data integration process. AWS Glue Studio provides a visual interface to design and run ETL (Extract, Transform, Load) jobs, simplifying the data transformation pipeline. This minimizes operational overhead as it avoids writing custom code for PII detection. AWS Glue's PII detection capabilities use machine learning algorithms, specifically designed to identify sensitive data types, thus reducing the need for complex regex patterns or manual configurations.
An AWS Step Functions state machine is used to orchestrate the overall data pipeline, providing a managed, serverless environment to control the flow of data from source to S3 data lake. This orchestrates the data ingestion process, ensuring that the PII detection and obfuscation occur before the data lands in the S3 data lake. Step Functions provide built-in error handling, retries, and monitoring features, further reducing the operational effort of the data pipeline.
Options A, C, and D are less optimal.
Option A involves Kinesis Data Firehose and Lambda.
While Firehose is suitable for real-time streaming, it might be overkill for batch ingestion. Writing a Lambda function to identify and obfuscate PII adds significant operational overhead compared to using the built-in capabilities of AWS Glue.
Option C utilizes AWS Glue Data Quality rules to obfuscate the data, requiring creation and maintenance of custom rules.
While it's feasible, it is more work than leveraging the built-in PII detection features in Glue Studio. Also, the question asks for obfuscation, not just detection.
Option D using DynamoDB as an intermediate store is inefficient and adds unnecessary complexity. DynamoDB is not primarily intended as an ETL staging area for data lake ingestion, and requires managing another database.
In essence, option B provides the LEAST operational effort by leveraging AWS Glue's built-in PII detection and obfuscation and AWS Step Functions to orchestrate the pipeline. It avoids the complexity of managing custom code, using less suitable services (DynamoDB), or using the wrong service for the job (Data Firehose when the job isn't real-time).
Supporting Links:
AWS Glue Studio: https://aws.amazon.com/glue/studio/ AWS Step Functions: https://aws.amazon.com/step-functions/ AWS Glue Data Quality: https://aws.amazon.com/blogs/big-data/validating-data-quality-with-aws-glue-data-quality/



A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data. The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.
Which solution will meet these requirements with the LEAST operational overhead?

  1. AWS Glue workflows
  2. AWS Step Functions tasks
  3. AWS Lambda functions
  4. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows

Answer(s): B

Explanation:

The best answer is
B. AWS Step Functions tasks . Here's why:
Orchestration: Both AWS Glue workflows and Step Functions can orchestrate ETL tasks. However, Step Functions excels at this due to its visual workflow designer, state management, and error handling capabilities. Automation: Step Functions allows you to define workflows using state machines that automatically trigger and manage the execution of AWS services like Glue and EMR, reducing manual intervention. Minimal Operational Overhead: While Glue workflows offer some orchestration, they are primarily focused on Glue jobs. Step Functions is a dedicated orchestration service, specifically designed for complex workflows, making it easier to manage and monitor ETL pipelines. Lambda: AWS Lambda functions can be part of an ETL process, but managing complex ETL workflows solely with Lambda would result in a highly distributed, difficult-to-manage architecture. Amazon MWAA: Amazon MWAA is a powerful orchestration tool, but it introduces more operational overhead compared to Step Functions. MWAA requires managing an Apache Airflow environment, including infrastructure, scaling, and maintenance.
While powerful, it's overkill for simple to moderately complex ETL orchestration scenarios. Step Functions offers: retry mechanisms, branching logic, and integration with other AWS services for monitoring and alerting. It's also serverless, meaning you don't have to manage any infrastructure. Step Functions and Glue: A common pattern is to use Step Functions to orchestrate Glue jobs. Step Functions triggers the Glue jobs, monitors their progress, and handles any errors.
Glue workflows tend to be simpler and more appropriate for orchestrating related Glue jobs, whereas Step Functions is a more versatile and robust solution for orchestrating complex workflows involving different AWS services. Given the need for automated orchestration and minimal manual effort for multiple ETL workflows involving Glue and EMR, Step Functions offers the least operational overhead.
Supporting Links:
AWS Step Functions : Official AWS documentation for Step Functions. AWS Glue Workflows : Official AWS documentation for Glue workflows. AWS Whitepaper - Building Data Lakes on AWS : Provides guidance on designing and implementing data lakes, including ETL orchestration.



A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class. A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year. The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?

  1. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
  2. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
  3. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
  4. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.

Answer(s): C

Explanation:

The correct answer is C because it provides the most cost-effective solution while maintaining high availability as defined by the problem constraints. Here's why:
S3 Standard-IA after 6 months: The data is accessed once or twice a month between 6 months and 2 years. S3 Standard-IA is designed for infrequently accessed data but offers rapid access when needed. It's more cost-effective than S3 Standard for this usage pattern while still providing high availability (data stored in multiple Availability Zones). S3 Glacier Deep Archive after 2 years: After 2 years, the data is accessed only once or twice a year. S3 Glacier Deep Archive is the lowest-cost storage option within S3, ideal for long-term archiving where retrieval times of up to 12 hours are acceptable.
Why other options are incorrect:
A & D (Using S3 One Zone-IA): S3 One Zone-IA stores data in a single Availability Zone.
While cheaper than S3 Standard-IA, it sacrifices availability. If that Availability Zone becomes unavailable, the data is lost. The problem states that high availability must be maintained, so this violates that requirement. B (Using S3 Glacier Flexible Retrieval): S3 Glacier Flexible Retrieval (formerly S3 Glacier) is suitable for infrequently accessed data with retrieval times ranging from minutes to hours.
While it is cheaper than Standard-IA, Glacier Deep Archive offers a lower cost for the given access pattern of once or twice a year. Choosing Glacier Flexible Retrieval over Glacier Deep Archive would thus be less cost-effective.
In summary, option C correctly balances the need for cost optimization with the requirement for high availability by leveraging S3 Standard-IA for the period of monthly access and S3 Glacier Deep Archive for long-term, infrequently accessed data.
Supporting Links:
S3 Storage Classes: https://aws.amazon.com/s3/storage-classes/ S3 Lifecycle Policies: https://docs.aws.amazon.com/AmazonS3/latest/userguide/lifecycle-configuration-examples.html



A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks. The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster. The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.
Which solution will meet these requirements?

  1. Set up the sales team BI cluster as a consumer of the ETL cluster by using Redshift data sharing.
  2. Create materialized views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.
  3. Create database views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.
  4. Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

Answer(s): A

Explanation:

The correct answer is A, using Redshift data sharing. Here's why:
Redshift Data Sharing: This feature allows you to securely share live data across Redshift clusters without data duplication or movement. The sales team's BI cluster can directly query the data residing in the ETL cluster, without impacting the performance of the ETL cluster's operations. This minimizes computing resource usage on the ETL cluster, fulfilling the requirement.
Why other options are not optimal:
B: Materialized Views & Direct Access: While materialized views could provide pre-computed summaries, they involve data duplication and require refreshing, consuming ETL cluster resources. Granting direct access increases the risk of unintended interference with ETL operations.
C: Database Views & Direct Access: Database views don't materialize data but still place a load on the ETL cluster when the sales team queries them, impacting the ETL cluster's performance. Direct access again poses security and operational risks.
D: Unload to S3 and Redshift Spectrum: This approach involves significant overhead: unloading data to S3 (consuming ETL resources), maintaining an S3 bucket, creating and managing Redshift Spectrum tables, and incurring Spectrum query costs for the sales team. It's a complex and inefficient solution for simple data sharing. Benefits of Data Sharing: Zero data duplication: Saves storage costs. Real-time access: The sales team gets the latest data directly from the ETL cluster. Security: Granular access control through data sharing policies. Minimal impact on ETL cluster: Queries are executed against the consumer cluster (sales team's cluster). In conclusion: Redshift data sharing provides a secure, efficient, and cost-effective way to share data between Redshift clusters without disrupting existing ETL operations, aligning perfectly with the given requirements.
Authoritative Links:
Amazon Redshift Data Sharing Working with data sharing in Amazon Redshift



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