DEA-C01 Question Bank · Latest 50 Questions Free Preview (Page 3/5)
Questions 21–30 of the latest 50 DEA-C01 questions (5 pages total), with answers and explanations, no login required. Practice all 306 questions online.
Question 21 · No. 277 · Single choice
A retail company needs to implement a solution to capture data updates from multiple Amazon Aurora MySQL databases. The company needs to make the updates available for analytics in near real time. The solution must be serverless and require minimal maintenance. Which solution will meet these requirements with the LEAST operational overhead?
- A. Set up AWS Database Migration Service (AWS DMS) tasks that perform schema conversions for each database. Load the changes into Amazon Redshift Serverless.
- B. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) Connect with Debezium connectors to load data into Amazon Redshift Serverless.
- C. Use AWS Database Migration Service (AWS DMS) to set up binary log replication to Amazon Kinesis Data Streams. Load the data into Amazon Redshift Serverless after schema conversion.
- D. Use Aurora zero-ETL integrations with Amazon Redshift Serverless for each database to load Aurora MySQL changes in Amazon Redshift Serverless.
Answer: D
Explanation: Aurora zero-ETL integrations with Amazon Redshift Serverless is the best solution because it is specifically designed for near real-time data replication from Aurora MySQL to Redshift without requiring ETL pipelines. It is serverless, requires minimal maintenance, and has the LEAST operational overhead compared to other options. Options A and C require managing AWS DMS tasks with schema conversions, adding complexity. Option B requires managing Amazon MSK clusters (not serverless) and Debezium connectors, which adds operational overhead.
Question 22 · No. 278 · Single choice
A healthcare company stores patient records in an on-premises MySQL database. The company creates an application to access the MySQL database. The company must enforce security protocols to protect the patient records. The company currently rotates database credentials every 30 days to minimize the risk of unauthorized access. The company wants a solution that does require the company to modify the application code for each credential rotation. Which solution will meet this requirement with the LEAST operational overhead?
- A. Assign an IAM role access permissions to the database. Configure the application to obtain temporary credentials through the IAM role.
- B. Use AWS Key Management Service (AWS KMS) to generate encryption keys. Configure automatic key rotation. Store the encrypted credentials in an Amazon DynamoDB table.
- C. Use AWS Secrets Manager to automatically rotate credentials. Allow the application to retrieve the credentials by using API calls.
- D. Store credentials in an encrypted Amazon S3 bucket. Rotate the credentials every month by using an S3 Lifecycle policy. Use bucket policies to control access.
Answer: C
Explanation: AWS Secrets Manager is specifically designed to handle automatic credential rotation and retrieval without requiring application code changes. It allows applications to fetch credentials via API calls, meaning when credentials are rotated, the application doesn't need to be modified - it simply calls the Secrets Manager API to get the current valid credentials. This meets both requirements: no code modification for each rotation, and least operational overhead since AWS manages the rotation process. Option A is incorrect because IAM roles work with AWS services, not on-premises MySQL databases. Option B is about encryption keys, not credential rotation/access. Option D is incorrect because S3 Lifecycle policies manage object lifecycle, not credential rotation.
Question 23 · No. 279 · Single choice
A company has an Amazon S3 based data lake. The data lake contains datasets that belong to multiple departments. The data lake ingests millions of customer records each day. A data engineer needs to design an access and storage solution that allows departments to access only the subset of the company's dataset that each department requires. The solution must follow the principle of least privilege. Which solution will meet these requirements with the LEAST operational effort?
- A. Define IAM policies and IAM roles for each department. Specify the S3 access paths from the data lake that each team can access.
- B. Set up Amazon Redshift and Amazon Redshift Spectrum as the primary entry points for the data lake. Define an IAM role that Amazon Redshift can assume. Configure the IAM role to grant access to the data that is in Amazon S3.
- C. Set up AWS Lake Formation. Assign LF-Tags to AWS Glue Data Catalog resources. Enable Lake Formation tag-based access control (LF-TBAC).
- D. Deploy an Amazon RDS for PostgreSQL database that has the aws_s3 extension installed. Configure AWS Step Functions events to invoke an AWS Lambda function to sync the data lake with the database.
Answer: C
Explanation: AWS Lake Formation with LF-Tags and tag-based access control (LF-TBAC) is specifically designed for managing fine-grained access to data lake resources across multiple departments with minimal operational effort. LF-TBAC allows you to assign tags to data catalog resources and then grant permissions based on those tags, which scales efficiently as new datasets are added without requiring frequent policy updates. Option A requires creating and maintaining many IAM policies (high operational overhead). Option B introduces unnecessary Redshift infrastructure complexity. Option D requires additional RDS, Lambda, and Step Functions infrastructure with data synchronization overhead.
Question 24 · No. 280 · Single choice
A company needs to store and analyze a large amount of IoT sensor data. The company needs to retain the data indefinitely. The company analyzes the data in an Amazon Redshift cluster. Which solution will meet these requirements MOST cost-effectively?
- A. Store the data in an Amazon S3 bucket in JSON format. Configure auto-copy data ingestion from the S3 bucket to the Redshift cluster.
- B. Store the data in an Amazon S3 bucket in Apache Parquet format. Configure query access through Amazon Redshift Spectrum.
- C. Store the data in an Amazon S3 bucket in JSON format. Configure query access through Amazon Redshift Spectrum.
- D. Store the data in an Amazon S3 bucket in Apache Parquet format. Configure auto-copy data ingestion from the S3 bucket to the Redshift cluster.
Answer: B
Explanation: Option B is the most cost-effective solution because Apache Parquet is a columnar storage format that provides high compression ratios, significantly reducing storage costs compared to JSON. Amazon Redshift Spectrum allows querying data directly in S3 without needing to load it into Redshift, which eliminates the need for expensive Redshift storage and compute resources for data that needs to be retained indefinitely. This combination minimizes both storage costs (Parquet compression in S3) and compute costs (querying directly via Spectrum rather than ingesting into the cluster).
Question 25 · No. 281 · Single choice
A retail company wants to implement real-time analytics for an ecommerce platform. The company needs to collect clickstream data from the company's website and mobile apps. The company needs to store the data for analytics. Which solution will meet these requirements with the LEAST ongoing maintenance?
- A. Use Amazon Data Firehose to ingest the streaming data. Deliver the processed data directly to a provisioned Amazon Redshift cluster.
- B. Deploy agents on Amazon EC2 instances to collect the streaming data. Use the AWS CLI to periodically batch upload the data to an Amazon S3 bucket.
- C. Use Amazon Kinesis Data Streams to collect the streaming data. Use Amazon Data Firehose to deliver the data to an Amazon S3 bucket.
- D. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) broker to collect the data. Store the data in an Amazon RDS DB instance.
Answer: C
Explanation: Amazon Kinesis Data Streams provides a fully managed service for collecting real-time clickstream data, and Amazon Data Firehose provides a fully managed delivery service that loads the data into Amazon S3 for analytics. This combination minimizes ongoing infrastructure maintenance compared with provisioning a Redshift cluster, managing EC2 agents, or operating MSK and RDS.
Question 26 · No. 282 · Single choice
A company creates a new non-production application that runs on an Amazon EC2 instance. The application needs to communicate with an Amazon RDS database instance using Java Database Connectivity (JDBC). The EC2 instances and the RDS database instance are in the same subnet. Which solution will meet this requirement?
- A. Modify the IAM role that is assigned to the database instance to allow connections from the EC2 instances.
- B. Modify the ec2_authorized_hosts parameter in the RDS parameter group to include the EC2 instances. Restart the database instance.
- C. Update the database security group to allow connections from the EC2 instances.
- D. Enable the Amazon RDS Data API and specify the Amazon Resource Name (ARN) of the database instance in the JDBC connection string.
Answer: C
Explanation: To allow an EC2 instance to communicate with an RDS database instance using JDBC, the RDS database's security group must be configured to allow inbound connections from the EC2 instances. Security groups act as virtual firewalls controlling traffic at the instance level. Option A is incorrect because IAM roles attached to RDS instances are used for AWS management tasks (e.g., S3 exports, CloudWatch Logs), not for application-level JDBC authentication. Option B is incorrect because 'ec2_authorized_hosts' is not a valid RDS parameter. Option D is incorrect because the RDS Data API uses an HTTP-based API and is not compatible with JDBC connections.
Question 27 · No. 283 · Single choice
A company uses AWS Step Functions to orchestrate a data pipeline. The company has configured the Step Functions logs to push to Amazon CloudWatch Logs when the log level is FATAL The company has enabled logs for all AWS services in the pipeline. A state named "preprocessing" invokes an AWS Lambda function named "preprocessing." The Lambda function preprocesses data before proceeding to the next state. The company needs to find error details if an error occurs during the data preprocessing. Which CloudWatch Logs log group should the company check to find the error details?
- A. The Step Functions TaskFailed event in the /aws/vendedlogs/states log group
- B. The AWS CloudTrail logs SendTaskFailure event in the CloudTrail/logs/preprocessing log group
- C. The Lambda logs in the laws/lambda/preprocessing log group
- D. The Step Functions TaskSucceeded event in the /aws/vendedlogs/states log group
Answer: C
Explanation: When an AWS Lambda function is invoked and an error occurs, detailed error information including stack traces and error messages is automatically written to the Lambda function's own CloudWatch Logs log group at /aws/lambda/<function-name>. While Step Functions does log TaskFailed events to /aws/vendedlogs/states, these logs only indicate that the task failed and provide limited context. To get the detailed error information from the preprocessing Lambda function itself, the company should check the Lambda function's dedicated log group at /aws/lambda/preprocessing, which contains the full error details from the function's execution.
Question 28 · No. 284 · Single choice
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.
Answer: B
Explanation: Option B is the correct answer because it is the only option that addresses log level filtering. Setting the log level to ERROR in the Lambda function code will filter the application logs to show only ERROR level entries. Options A (permissions), C (JSON format), and D (custom log group) do not control log levels—permissions affect authorization, JSON format affects log structure, and a custom log group only changes the destination. In AWS Lambda managed runtimes, application log levels are controlled by the code's logging configuration.
Question 29 · No. 285 · Single choice
A data engineer needs to validate the quality of files that are uploaded to an Amazon S3 bucket every day. The files are in CSV and JSON formats and schema variations exist. The data engineer needs a repeatable process to monitor data quality metrics such as null values, format inconsistencies, and outliers. The process must provide reusable rule-based profiling and scale across multiple datasets with minimal manual effort or custom code. Which solution will meet these requirements?
- A. Use AWS Glue Studio to create a daily extract, transform, and load (ETL) pipeline that merges datasets and applies validation logic by using PySpark transformations.
- B. Use AWS Glue extract, transform, and load (ETL) jobs with dynamic frames and the ResolveChoice class to align schemas and detect structural differences between files.
- C. Use AWS Glue DataBrew to configure profiling jobs and reusable recipe actions. Schedule the profiling jobs and reusable recipe actions to run against each dataset in Amazon S3.
- D. Use AWS Glue DataBrew to generate profiling dashboards for each dataset. Monitor quality metrics through manual review.
Answer: C
Explanation: Option C is the best solution because AWS Glue DataBrew is specifically designed for data profiling and quality monitoring without requiring custom code. It provides built-in profiling jobs that can detect null values, format inconsistencies, and outliers in CSV and JSON files. DataBrew's reusable recipes allow for rule-based profiling that can be applied across multiple datasets, and the scheduled jobs eliminate manual effort. Option A requires custom PySpark code (contradicting the requirement for minimal custom code). Option B focuses on schema alignment rather than data quality metrics. Option D relies on manual review of dashboards, which contradicts the requirement for minimal manual effort.
Question 30 · No. 286 · Single choice
A company needs a solution to process streaming data by using Apache Spark in a Kubernetes environment. The solution must support event-driven scaling and optimize resource utilization. The company needs to integrate the solution with existing Kubernetes infrastructure deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Which solution will meet these requirements with the LEAST operational overhead?
- A. Deploy self-managed Apache Spark on Amazon EKS by using custom auto-scaling policies based on Kubernetes Event-Driven Autoscaling (KEDA) operators.
- B. Use Amazon EMR on Amazon EKS with Kubernetes Event-Driven Autoscaling (KEDA) for event-driven scaling of Spark applications.
- C. Use Amazon EMR on Amazon EKS with the Kubernetes Cluster Autoscaler to scale Spark applications based on metrics events generated by the Kubernetes Metrics Server.
- D. Implement Apache Spark on Amazon EKS with AWS Auto Scaling groups and custom scaling policies based on metrics events collected by the Kubernetes Metrics Server.
Answer: B
Explanation: Option B is the best choice because Amazon EMR on Amazon EKS is a managed service that reduces operational overhead significantly compared to self-managed Apache Spark. EMR on EKS is specifically designed to run Apache Spark workloads on Kubernetes with optimized resource utilization. Additionally, KEDA (Kubernetes Event-Driven Autoscaling) provides true event-driven scaling capabilities, which aligns perfectly with the requirement for event-driven scaling. This combination delivers the LEAST operational overhead while meeting all the stated requirements.