This is the free Amazon DEA-C01 practice question bank —
140 of 279 total questions, each with a full explanation, free to
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Question 1
A company runs a multi-tenant Amazon EMR cluster on Amazon EC2 instances. Multiple teams perform interactive query analyses and data transformations on the data in the EMR cluster. The teams can access the cluster only through EMR Studio workspaces and EMR steps.
The teams need to use EMR steps to run Apache Spark jobs to fetch data from an Amazon DynamoDB table. The DynamoDB table contains confidential data that must be accessible to only one specific team. The company needs to ensure that only the appropriate team can access the confidential data in the EMR cluster.
Which solution will meet these requirements?
A. Set up runtime roles for EMR steps.
B. Set up AWS Lake Formation permissions.
C. Set up IAM roles for EMR File System (EMRFS) requests.
D. Set up a DynamoDB resource-based policy.
Show Answer
Correct Answer: A
Explanation: Runtime roles for EMR steps allow a multi-tenant EMR cluster to assume different IAM roles at step execution time. By assigning a role with DynamoDB access only to the specific team’s Spark steps, only that team can read the confidential DynamoDB table. Other teams’ steps run with roles that lack DynamoDB permissions, enforcing isolation. Lake Formation is for data lakes (typically S3), EMRFS roles apply to S3 access, and DynamoDB resource-based policies are not suitable for this EMR step isolation use case.
Question 1
A company uses Amazon Redshift for its data warehouse. A data engineer must query a table named orders.complete_orders_history, which contains 100 columns. The query must return all columns except columns named companyId and unique_system_id.
Which Amazon Redshift SQL statement will meet this requirement?
A.
B.
C.
D.
Show Answer
Correct Answer: C
Explanation: Amazon Redshift supports excluding specific columns when selecting all columns by using the SELECT * EXCLUDE syntax. This allows returning every column from orders.complete_orders_history except companyId and unique_system_id in a single, simple query without listing the remaining ~98 columns explicitly.
Question 2
A company stores Apache Parquet files in an Amazon S3 data lake. The data lake receives thousands of files from multiple sources every hour. The files range in size from 50 KB to 100 KB.
The company is evaluating the implementation of Apache Iceberg tables for the data lake. The company is using AWS Glue Data Catalog as part of the evaluation. The company needs a solution to optimize query performance in Iceberg. The solution must ensure that Iceberg table performance does not degrade when more files are added over time.
Which solution will meet these requirements?
A. Use an AWS Glue job to compact the files into a standard size of 512 MB at the end of each day. Run an AWS Glue crawler to update the Data Catalog.
B. Configure the Data Catalog to automatically compact the files every minute.
C. Configure Iceberg table properties to enable automatic compaction based on thresholds for file size and the number of files.
D. Implement a partition strategy in Amazon S3. Run an AWS Glue crawler to update the Data Catalog every 5 minutes.
Show Answer
Correct Answer: C
Explanation: Apache Iceberg is designed to handle many small files through table maintenance operations such as data file rewrite (compaction). The correct approach is to configure Iceberg table properties and run Iceberg-managed compaction based on file size and file count thresholds, which prevents performance degradation as more files are added. AWS Glue Data Catalog does not perform Iceberg data file compaction itself, and crawlers or Glue catalog settings cannot automatically optimize Iceberg table files. Therefore, enabling Iceberg’s native compaction mechanisms is the correct solution.
Question 2
An application uses an AWS Lambda function that is configured with managed runtimes. The Lambda function successfully writes logs to the default Amazon CloudWatch Logs log group. A data engineer wants to modify the logging behavior to show only ERROR level logs for application logs and WARN level logs for system logs.
Which solution will meet these requirements?
A. Add additional permissions to the Lambda execution role.
B. Set the log level to ERROR in the Lambda function code.
C. Configure the Lambda function to use the JSON log format.
D. Configure the Lambda function to send logs to a custom log group.
Show Answer
Correct Answer: C
Explanation: AWS Lambda managed runtimes support advanced logging controls that distinguish between application logs and system logs. These controls require application logs to be emitted in JSON format so that Lambda can apply separate log level filtering (for example, ERROR for application logs and WARN for system logs) via function configuration, without changing permissions or routing logs to a different log group.
Question 3
A company needs to optimize storage costs for an Amazon S3 bucket. The S3 bucket receives 10 million objects every day. The objects range in size from 2 KB to 5 MB. The objects need to be immediately accessible for the first 60 days. Users access objects infrequently from 61 to 180 days. The objects must be accessible within an hour from 181 to 365 days. The company can delete the objects after 365 days.
Which solution will meet these requirements?
A. Use S3 Intelligent-Tiering to automatically transition objects. Select the Archive Access tier for Intelligent-Tiering. Configure an S3 bucket policy to expire objects that are older than 365 days.
B. Create an S3 Lifecycle policy to move objects. Configure the policy to move objects from S3 Standard to S3 Standard-Infrequent Access (S3 Standard-IA) after 60 days. Move the objects to S3 Glacier Flexible Retrieval after 180 days. Expire objects after 365 days.
C. Enable S3 Inventory. Use a daily inventory report to configure an S3 Batch Operations job that moves objects from S3 Standard to S3 Standard-Infrequent Access (S3 Standard-IA) after 60 days. Move objects to S3 Glacier Flexible Retrieval after 180 days. Expire objects after 365 days.
D. Enable S3 Inventory. Run an AWS Lambda function each day to fetch an inventory report and move objects from S3 Standard to S3 Standard-Infrequent Access (S3 Standard-IA) after 60 days. Move objects to S3 Glacier Flexible Retrieval after 180 days. Expire objects after 365 days.
Show Answer
Correct Answer: B
Explanation: An S3 Lifecycle policy directly matches the access requirements over time with the lowest operational complexity. Objects remain in S3 Standard for the first 60 days (immediate access), transition to S3 Standard-IA from 61–180 days (infrequent access but still millisecond retrieval), then move to S3 Glacier Flexible Retrieval from 181–365 days (retrieval within minutes to hours, which meets the 1-hour requirement). Finally, objects are expired after 365 days.
Although some objects are smaller than 128 KB (the minimum billable size for Standard-IA), this does not make the solution invalid; it only affects cost efficiency, not correctness. Options C and D are unnecessarily complex, and Intelligent-Tiering with Archive Access (Option A) does not precisely control transition timing and introduces monitoring and archive-tier constraints that are not required here.
Question 3
A data engineer is writing a query to join two tables in Amazon Athena. The data engineer needs to choose the correct join order for the tables to optimize query performance.
Which solution will meet these requirements?
A. Specify the smaller table on the left side of the join and the larger table on the right side of the join.
B. Specify the larger table on the left side of the join and the smaller table on the right side of the join.
C. Use AWS Glue to pre-process the tables before performing the join.
D. Use table statistics to automatically determine the join order.
Show Answer
Correct Answer: B
Explanation: Amazon Athena (based on Presto/Trino) uses distributed hash joins where the right-side table is typically the build side and may be broadcast to worker nodes. Placing the smaller table on the right reduces memory usage and network overhead, resulting in better performance. Therefore, the larger table should be on the left and the smaller table on the right.
Question 4
A company needs to aggregate and filter a large amount of streaming data in real-time with low latency. The company needs to store the data in Amazon S3 for analysis.
Which solution will meet these requirements in the MOST operationally efficient way?
A. Use Amazon Kinesis Data Streams with provisioned capacity and AWS Lambda functions to perform custom transformations and to integrate with Amazon S3.
B. Use Amazon Data Firehose with built-in data transformations. Deliver the data directly to Amazon S3.
C. Use Amazon Kinesis Data Streams and Amazon Managed Service for Apache Flink to perform complex processing and to integrate with Amazon S3.
D. Use Amazon Data Firehose and AWS Lambda functions to perform custom transformations and to deliver the data to Amazon S3.
Show Answer
Correct Answer: B
Explanation: The requirement is real-time aggregation and filtering with low latency, delivery to Amazon S3, and MOST operational efficiency. Amazon Data Firehose is fully managed, scales automatically, and can deliver streaming data directly to S3 with built-in data transformations (such as record format conversion and basic processing) without managing shards, clusters, or stream applications. Kinesis Data Streams with Lambda or Apache Flink requires more operational overhead. Therefore, using Amazon Data Firehose with built-in transformations is the most operationally efficient solution.
Question 4
A company has a data pipeline that processes transaction data in real time. The company needs a notification system that alerts different teams based on the type of processing error without any delay. For security-related errors, the system must immediately notify the security team. For data validation errors, the system must notify the data quality team. For system errors, the system must notify the operations team.
Which solution will meet these requirements with the LEAST operational overhead?
A. Create an Amazon Simple Notification Service (Amazon SNS) topic with an AWS Lambda function subscriber that evaluates the error type and forwards the error to the appropriate email addresses.
B. Configure Amazon EventBridge rules with distinct event patterns for each error type. Route each error type to a dedicated Amazon Simple Notification Service (Amazon SNS) topic for team-specific alerts.
C. Use Amazon Simple Queue Service (Amazon SQS) with message attributes to categorize errors. Allow each team to poll their respective SQS queue for relevant errors.
D. Set up Amazon CloudWatch alarms with different metrics for each error type. Invoke a different Amazon Simple Notification Service (Amazon SNS) notification each time a metrics threshold is crossed.
Show Answer
Correct Answer: B
Explanation: Amazon EventBridge can natively filter and route events in real time based on event patterns without custom code. Creating distinct rules for each error type and routing them directly to dedicated SNS topics provides immediate notifications, clear separation of concerns, and minimal operational overhead. The other options require additional code (Lambda), polling (SQS), or indirect metric-based detection (CloudWatch alarms), all of which increase complexity or delay.
Question 5
A retail company stores point-of-sale transaction data in an Amazon RDS for MySQL database. The company maintains historical sales analytics in Amazon Redshift. The company needs to create daily reports that combine the current day's transactions with historical sales patterns for trend analysis. The company requires a solution that provides near real-time insights while minimizing data transfer costs and maintenance overhead.
Which solution will meet these requirements?
A. Configure AWS Database Migration Service (AWS DMS) to continuously replicate data from RDS for MySQL to Amazon Redshift. Use Redshift queries to create consolidated reports.
B. Implement Amazon Redshift federated queries to directly access RDS for MySQL data and join it with existing Redshift tables in a single query.
C. Use AWS Glue to create an extract, transform, and load (ETL) pipeline that runs every hour to copy incremental data from RDS for MySQL to Amazon Redshift. Generate reports.
D. Export RDS for MySQL data to an Amazon S3 bucket on a regular schedule. Use the COPY command to load the data into Amazon Redshift staging tables. Join the data with historical data.
Show Answer
Correct Answer: B
Explanation: Amazon Redshift federated queries allow Amazon Redshift to query Amazon RDS for MySQL data in place and join it with existing Redshift tables. This provides near real-time access to the current day’s transactions without continuously copying data, minimizing data transfer costs and reducing operational and maintenance overhead compared with replication or ETL-based approaches.
Question 5
A company stores a 100 MB dataset in an Amazon S3 bucket as an Apache Parquet file. A data engineer needs to profile the data before performing data preparation steps on the data.
Which solution will meet this requirement in the MOST operationally efficient way?
A. Create a profile job on the dataset in AWS Glue DataBrew. Review the profile job results.
B. Stream the data into Amazon Managed Service for Apache Flink for SQL queries. Use the Apache Flink dashboard to profile the data.
C. Ingest the data into Amazon Redshift Spectrum. Use SQL queries to profile the data.
D. Load the data into an Amazon QuickSight dataset. Build a topic to profile the data with questions.
Show Answer
Correct Answer: A
Explanation: AWS Glue DataBrew provides built-in data profiling jobs for datasets in Amazon S3, including Parquet files. It requires no infrastructure setup, delivers comprehensive statistics (schema, distributions, nulls, anomalies), and is designed specifically for profiling and preparation workflows. The other options require additional ingestion, infrastructure, or visualization setup and are less operationally efficient for simple profiling.
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