DEA-C01 Question Bank · Latest 50 Questions Free Preview (Page 1/5)
Questions 1–10 of the latest 50 DEA-C01 questions (5 pages total), with answers and explanations, no login required. Practice all 306 questions online.
Question 1 · No. 257 · Single choice
A university is developing an educational application that analyzes student essays. The application provides personalized feedback with accurate citations to the university's textbooks. The application needs to process essays in multiple languages. Application responses must include direct references to specific sections in the course materials and must be in the student’s selected language. Which solution will meet these requirements with the LEAST operational overhead?
- A. Build a custom vector database by using Amazon OpenSearch Serverless. Store textbook content as multilingual embeddings. Create an AWS Lambda function that queues the database when generating responses with Amazon Bedrock.
- B. Create a knowledge base in Amazon Bedrock Knowledge Bases with the university’s textbooks. Configure a multilingual model to generate responses with source citations.
- C. Use Amazon Comprehend to detect the language and key topics in the essays. Use Amazon Kendra to search for relevant textbook passages. Create an AWS Lambda function that formats the textbook passages into feedback.
- D. Use Amazon SageMaker to host a custom-trained large language model (LLM) that has been fine-tuned on the university’s textbooks to generate personalized feedback with citations.
Answer: C
Question 2 · No. 258 · Single choice
A media company uploads large video files to Amazon S3 for processing. After processing, the company needs to keep the original files for 90 days in case the files require reprocessing. After 90 days, the company can delete the files to reduce storage costs. The company stores the processed videos in a different S3 bucket. Which S3 Lifecycle configuration will meet these requirements for the original files MOST cost-effectively?
- A. Store the files in S3 Standard for 90 days. Transition the files to S3 Glacier Flexible Retrieval for long-term storage. Then expire the files.
- B. Store the files in S3 Standard for 90 days. Enable versioning. Enable Object Lock on the files for 90 days. Then expire the files.
- C. Store the files in S3 Standard for 90 days. Implement S3 Lifecycle management to expire the files.
- D. Store the files in S3 Intelligent-Tiering for 90 days. Enable versioning. Add S3 Lifecycle management to expire the files.
Answer: C
Explanation: Option C is the most cost-effective solution. The requirements only need to store files in S3 Standard for 90 days and then delete them. S3 Lifecycle management can directly expire (delete) the files after 90 days without unnecessary transitions or additional features. Option A is wasteful because transitioning to S3 Glacier Flexible Retrieval incurs retrieval costs and minimum storage duration charges, which is unnecessary since the files will be deleted anyway. Option B adds unnecessary complexity and cost with versioning and Object Lock, which actually prevents deletion (contradicting the requirement). Option D uses Intelligent-Tiering which has per-object monitoring fees, and versioning adds extra storage costs. Since the files will be deleted after 90 days, no transition to cheaper storage is needed—only a simple expiration policy is required.
Question 3 · No. 259 · Multiple choice
A data engineer is building a serverless. multi-step extract, transform, and load (ETL) pipeline. The pipeline extracts data from an Amazon S3 data lake and transforms the data by using AWS Glue ETL jobs. The pipeline then loads the results into an Amazon Redshift database. The data engineer needs to orchestrate the serverless ETL workflow. Which solutions will meet these requirements? (Choose two.)
- A. Implement the workflow by using AWS Step Functions. Configure Step Functions to coordinate the AWS Glue ETL jobs and handle error conditions with automatic retries.
- B. Use AWS Glue workflows to create a graph of the ETL tasks that visually represents the dependencies between jobs and the job triggers.
- C. Provision an always on Amazon EC2 instance. Create a cron job that invokes the AWS Glue ETL jobs in sequence based on a predefined scheduleю
- D. Use Amazon EventBridge rules to invoke the AWS Glue ETL jobs based on S3 object creation events. Configure the rules to chain the AWS Glue ETL jobs in sequence and handle complex job dependencies.
- E. Build an orchestration solution by using AWS CodePipeline to coordinate the ETL pipeline and infrastructure changes based on the dependencies.
Answer: A, B
Explanation: Option A (AWS Step Functions) is ideal because it is a serverless orchestration service specifically designed to coordinate multi-step workflows with built-in error handling and automatic retries. Option B (AWS Glue workflows) is also correct because AWS Glue workflows are natively designed for orchestrating Glue ETL jobs, providing visual representation of dependencies and triggers. Both meet the serverless requirement. Options C is incorrect because EC2 instances are not serverless. Option D is limited because EventBridge is better suited for simple event-driven triggers rather than complex multi-step ETL orchestration with dependencies. Option E is incorrect because CodePipeline is designed for CI/CD pipelines, not ETL workflow orchestration.
Question 4 · No. 260 · Single choice
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB. Amazon RDS. Amazon Redshift. and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time. Which solution will meet these requirements?
- A. Capture data from DynamoDB by using DynamoDB Streams. Migrate data from Amazon RDS by using AWS DMS. Export Amazon Redshift data. Store all data in Amazon S3. Use Redshift Spectrum to run queries.
- B. Set up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.
- C. Deploy an Amazon EMR cluster powered by Apache Spark to ingest, process, and merge datasets from multiple sources. Run analytical workloads on the merged data.
- D. Use Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
Answer: D
Explanation: Amazon Athena Federated Query is the optimal solution because it allows querying data directly from multiple sources (DynamoDB, RDS, Redshift, and S3) without moving or copying the data. This approach minimizes data movement, which is critical for a one-time performance report. Federated queries use data source connectors to execute queries where the data resides, reducing both data transfer costs and query execution time. Unlike options A, B, and C, which all require data extraction, transformation, and centralization into a single store (S3), Athena Federated Query eliminates unnecessary data movement while still providing the ability to perform joins across heterogeneous data sources. This makes it the best choice for a one-time analytical task with strict requirements on avoiding data movement and minimizing query time.
Question 5 · No. 261 · Single choice
A company processes a CSV file that contains millions of transaction records every day. The file is stored in Amazon S3. Each transaction must be validated before updating a database. The company needs a solution that will process the data in parallel. The solution must use error handling that stops the entire process if more than 15% of the records fail validation. Which solution will meet these requirements with the LEAST operational overhead?
- A. Create an AWS Batch job that processes chunks of the file in parallel with a custom error tracking mechanism.
- B. Use AWS Step Functions Distributed Map state with the ToleratedFailurePercentage field set to 15%.
- C. Deploy an Amazon EMR cluster with Spark to process the file Configure a custom failure threshold to 15%.
- D. Use AWS Lambda with S3 Batch Operations to process the file and track validation failures to be less than 15%.
Answer: B
Explanation: AWS Step Functions Distributed Map state is specifically designed for parallel processing of large datasets (millions of records) stored in S3. It provides built-in error handling through the ToleratedFailurePercentage field, which automatically stops the execution when the failure threshold is exceeded. As a fully managed service, it requires minimal operational overhead compared to AWS Batch (requires managing compute environments), Amazon EMR (requires cluster management), or AWS Lambda with S3 Batch Operations (has limitations on processing large datasets and requires custom error tracking).
Question 6 · No. 262 · Single choice
A company needs to collect logs for an Amazon RDS for MySQL database and make the logs available for audits. The logs must track each user that modifies data in the database or makes changes to the database instance. Which solution will meet these requirements?
- A. Enable Amazon CloudWatch Logs. Create metric filters to monitor database changes and instance-level changes. Configure automated notification systems to send near real-time alerts for suspicious database operations.
- B. Configure an Amazon EventBridge rule to monitor database activity. Create an AWS Lambda function to process EventBridge events and store them in Amazon OpenSearch Service.
- C. Configure AWS CloudTrail to log API calls. Use Amazon CloudWatch Logs for basic monitoring. Use IAM policies to control access to the logs. Set up scheduled reporting for log audits.
- D. Enable and configure native Amazon RDS database audit logging. Enable Amazon CloudWatch Logs. Configure metric filters and alarms. Configure AWS CloudTrail audit logging.
Answer: D
Explanation: Option D is the correct answer because it combines native Amazon RDS database audit logging (which captures data-level changes made by users within the database) with AWS CloudTrail (which captures instance-level API calls and changes) and Amazon CloudWatch Logs (which stores the logs and enables metric filters and alarms for monitoring). Option A only uses CloudWatch Logs which doesn't natively track user-level database modifications. Option B uses EventBridge which is designed for application events, not database activity streams. Option C relies on CloudTrail for API calls but does not capture data modifications within the database itself. Therefore, Option D is the most comprehensive solution that meets both requirements: tracking user data modifications and database instance changes.
Question 7 · No. 263 · Single choice
A company is building data processing pipelines by using AWS Glue. The pipelines access data stored in Amazon S3. The company has organized the data into folders with prefixes that represent different classification levels. The company needs to restrict AWS Glue jobs to access only specific prefixes based on the data classification. The company must also restrict access to business hours (9 AM to 5 PM). Which elements must the company include in a custom IAM policy to meet these requirements?
- A. A Resource element with S3 object Amazon Resource Name (ARN) patterns that use wildcards for each prefix and a Condition element that uses the $util.time variable with TimeGreaterThan and TimeLessThan operators
- B. A Resource element with S3 object Amazon Resource Name (ARN) patterns that use wildcards for each prefix and a Condition element that uses the aws:CurrentTime condition key with DateGreaterThan and DateLessThan operators
- C. A Condition element that uses the s3:prefix condition key to restrict folder access and aws:CurrentTime with DateGreaterThanEquals and DateLessThanEquals to restrict hours of operation
- D. A Condition element that uses the s3:ResourceAccount condition key to restrict bucket access and a Deny statement that applies outside of business hours
Answer: B
Explanation: The correct answer is B because restricting AWS Glue jobs to specific S3 prefixes requires a Resource element with S3 object ARN patterns using wildcards (e.g., arn:aws:s3:::bucket-name/prefix/*). For time-based access restrictions, aws:CurrentTime is the correct IAM condition key, and DateGreaterThan and DateLessThan operators are used to define business hours (9 AM to 5 PM). Option A uses an invalid $util.time variable which doesn't exist in IAM. Option C incorrectly suggests using s3:prefix as a condition key (prefix restriction should be in the Resource element). Option D uses s3:ResourceAccount which restricts by account, not by prefix, and doesn't properly address the time restriction requirement.
Question 8 · No. 264 · Single choice
A company stores historical customer data in an Amazon Redshift table. A column named Email contains null entries and values that are not email addresses. The quality of the Email column is critical for multiple downstream processes. A data engineer must create an AWS Glue Data Quality rule that fails when the percentage of valid email addresses in the Email column is less than 90%. Which component of an AWS Glue Data Quality rule will meet these requirements?
- A. Uniqueness "Email" matches "[%@%.%]" with a threshold set to > 0.9
- B. ColumnValues "Email” matches "[%@%.%]" with a threshold set to > 0.1
- C. ColumnValues "Email" matches "[%@%.%]" with a threshold set to > 0.9
- D. UniqueValueRatio "Email" matches "[%@%.%]" with a threshold set to > 0.1
Answer: C
Explanation: The correct component is ColumnValues because it allows checking if values in a column match a specified regex pattern. The pattern '[%@%.%]' is a basic email format pattern that checks for the presence of characters, '@', and '.'. The threshold should be set to > 0.9 to represent the 90% requirement - the rule will fail if the percentage of valid email addresses drops below 90%. Option A uses Uniqueness which checks for unique values, not valid emails. Option B uses the correct component but with an incorrect threshold (0.1 instead of 0.9). Option D uses UniqueValueRatio which checks for the ratio of unique values, not email validity.
Question 9 · No. 265 · Single choice
A company stores sensitive transaction data in an Amazon S3 bucket. A data engineer must implement controls to prevent accidental deletions. Which solution will meet this requirement?
- A. Enable versioning on the S3 bucket and configure MFA delete.
- B. Configure an S3 bucket policy rule that denies the creation of S3 delete markers.
- C. Create an S3 Lifecycle rule that moves deleted files to S3 Glacier Deep Archive.
- D. Set up AWS Config remediation actions to prevent users from deleting S3 objects.
Answer: A
Explanation: Enabling versioning on the S3 bucket preserves multiple versions of objects, allowing recovery from accidental deletions. Configuring MFA delete adds an additional layer of security by requiring multi-factor authentication to permanently delete object versions, which directly prevents accidental deletions. This is the AWS recommended best practice for protecting S3 data from accidental deletion.
Question 10 · No. 266 · Single choice
A company’s application needs to search and analyze data in near real time. The application must handle up to 1,000 requests each second with low query latency. The company wants a solution that individual data teams can own and configure to meet each team's cost and performance optimization requirements. Which solution will meet these requirements?
- A. Use Amazon S3 buckets to store the data. Use Amazon Athena to query and analyze the data. Assign each data team a separate S3 bucket prefix to optimize queries.
- B. Use streams in Amazon Kinesis Data Streams and Amazon Managed Service for Apache Flink to query and analyze the data. Assign each data team a separate stream to manage and consume.
- C. Use Amazon OpenSearch Service clusters with indexing to query the data. Assign each data team a separate cluster to configure for storage and queries.
- D. Use Amazon Aurora clusters that run on Aurora I/O-Optimized instances. Assign each data team a separate Aurora cluster to configure for storage and queries.
Answer: C
Explanation: The correct answer is C. Amazon OpenSearch Service is specifically designed for search and analytics workloads with low query latency. It supports near real-time indexing and searching, which directly meets the requirement to 'search and analyze data in near real time' with 'low query latency'. Additionally, assigning each data team a separate cluster allows them to independently configure and optimize their own cost and performance settings. Option A (Athena) is for batch queries on S3, not real-time. Option B (Kinesis/Flink) is for stream processing, not specifically search. Option D (Aurora) is a relational database not optimized for search workloads.