DEA-C01 Question Bank · Latest 50 Questions Free Preview (Page 2/5)
Questions 11–20 of the latest 50 DEA-C01 questions (5 pages total), with answers and explanations, no login required. Practice all 306 questions online.
Question 11 · No. 267 · Single choice
A global finance company needs to implement near real-time cross-Region synchronization of trading data between trading centers in the us-east-1 Region, the eu-west-2 Region, and the ap-northeast-1 Region. The company must ensure that data is encrypted in transit. The solution must ensure data ordering and consistency and must support cross-Region disaster recovery. The solution must provide data latency of less than 500 milliseconds. Which solution will meet these requirements with the LEAST operational effort?
- A. Deploy Apache Kafka Connect in each AWS Region. Use custom-developed connectors to set up cross-Region data replication. Configure the SSL security protocol.
- B. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) Replicator to establish fully interconnected replication relationships between MSK clusters in the three AWS Regions. Enable TLS encryption and IAM authentication. Set up cross-Region backup configurations.
- C. Deploy Apache Kafka Mirror Maker 2.0 in each AWS Region. Set up custom replication policies to handle cross-Region data synchronization. Configure the SSL security protocol.
- D. Use Amazon Kinesis Data Streams to receive trading data from each AWS Region. Use Amazon Data Firehose to replicate data between Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in each Region. Configure AWS Key Management Service (AWS KMS) encryption and IAM roles to manage access.
Answer: B
Explanation: Option B is the best choice because Amazon MSK Replicator is a fully managed service that handles cross-Region replication between MSK clusters with minimal operational effort. It automatically handles data ordering, consistency, and provides TLS encryption with IAM authentication. The 'fully interconnected replication' feature supports the disaster recovery requirement, and being a managed service, it requires significantly less operational overhead compared to self-managing Apache Kafka Connect (Option A) or MirrorMaker 2.0 (Option C). Option D is overly complex, mixing Kinesis Data Streams and Firehose with MSK clusters, which increases operational complexity.
Question 12 · No. 268 · Single choice
A data engineer at a large company needs to create centralized datasets that are optimized for Amazon Redshift performance. The company has multiple downstream teams that use their own AWS accounts and dedicated Amazon Redshift clusters with RA3 nodes. All downstream teams need access to the centralized datasets. Which solution will provide immediate access to the datasets and maintain the current Amazon Redshift performance?
- A. Copy the datasets to an Amazon S3 bucket by using the UNLOAD command. Register the table definitions in a dedicated AWS Glue Data Catalog schema. Share the schema with the other AWS accounts by using AWS Lake Formation. Use Amazon Redshift Spectrum to access the data.
- B. Create a daily extract, transform, and load (ETL) job to unload the data to an Amazon S3 staging area. Instruct the teams to copy the data into their Amazon Redshift clusters.
- C. Set up Amazon Redshift data sharing between the Amazon Redshift producer clusters and the consumer clusters to provide access to the centralized datasets.
- D. Set up an AWS DataSync job that automatically syncs the data between the Amazon Redshift producer clusters and the consumer clusters.
Answer: C
Explanation: Amazon Redshift data sharing is the optimal solution for this scenario because it provides immediate access to centralized datasets across different AWS accounts without requiring data copying or movement. Data sharing allows consumer Redshift clusters to query live data from producer clusters, maintaining the performance benefits of Redshift's columnar storage and massively parallel processing. Option A involves copying data to S3 and using Redshift Spectrum, which has different performance characteristics. Option B only provides daily access through ETL jobs, not immediate access. Option D is not appropriate as AWS DataSync is not designed for syncing Redshift cluster data and would not maintain performance or provide immediate access.
Question 13 · No. 269 · Single choice
A company is setting up a new Amazon SageMaker Unified Studio domain. Each of the company's business units needs isolated control over its own assets, projects, and metadata. Specific datasets must be shareable with other business units upon approval. The company also requires centralized user authentication and identity mapping. Which solution will meet these requirements?
- A. Configure each business unit as a domain unit with delegated ownership and fine-grained permissions policies. Give users the ability to share assets across domain units with explicit access control. Assign API keys to users for authentication to access the domain portal.
- B. Configure business units as separate domain units with owner permissions. Restrict projects exclusively to owners to prevent data sharing between domains. Configure AWS IAM Identity Center for centralized authentication. Map user profiles to their respective domain units.
- C. Configure business units to be represented as separate domains. Establish isolated environments with no shared administrative policies. Configure AWS IAM Identity Center for centralized authentication. Delegate administration at the domain level.
- D. Configure each business unit as a separate domain unit to manage permissions on assets, projects, and metadata. Configure AWS IAM Identity Center for centralized authentication. Map user profiles to their respective domain units. Enable cross-business unit sharing through access requests. Instruct domain unit owners to approve or deny the requests.
Answer: D
Explanation: Option D is the correct choice because it satisfies all three requirements: (1) Each business unit is configured as a separate domain unit within the single SageMaker Unified Studio domain, which provides isolated control over assets, projects, and metadata. (2) AWS IAM Identity Center is configured for centralized user authentication, and user profiles are mapped to their respective domain units for identity mapping. (3) Cross-business unit sharing is enabled through access requests, with domain unit owners approving or denying the requests, which directly meets the requirement for sharing specific datasets upon approval. Option A uses API keys instead of centralized authentication (fails requirement 3). Option B restricts projects to owners only, preventing the required data sharing upon approval (fails requirement 2). Option C uses separate domains rather than domain units, which doesn't fit the scenario of setting up a single new domain and creates unnecessary administrative complexity.
Question 14 · No. 270 · Single choice
A global company currently uses Amazon Redshift to store data and Amazon Quick Suite (previously known as Amazon QuickSight) to generate reports. A team of business analysts have varying levels of technical expertise. Some analysts lack SQL knowledge. All the analysts need to create new reports frequently. The company wants to use natural program language queries to create dashboards and reports more efficiently. Which solution will meet these requirements with the LEAST operational effort?
- A. Use Quick Suite dashboards that have zero-ETL access to Amazon Redshift.
- B. Enable Amazon Q in Quick Suite. Generate Quick Suite dashboards and reports.
- C. Integrate Tableau with Amazon Redshift to give Tableau direct access to the data.
- D. Use Quick Suite dashboards that have federated query access to Amazon Redshift.
Answer: B
Explanation: The key requirements are: 1) natural language queries, 2) support for analysts with no SQL knowledge, 3) least operational effort, and 4) already using Quick Suite. Amazon Q in Quick Suite (formerly QuickSight Q) is purpose-built for natural language queries, allowing users to ask questions in plain English without needing SQL. Since the company is already using Quick Suite, enabling Amazon Q adds minimal operational overhead. Option A (zero-ETL) only addresses data integration, not natural language. Option C introduces Tableau, adding significant operational effort. Option D (federated query) enables cross-source querying but does not provide natural language capabilities.
Question 15 · No. 271 · Single choice
A global ecommerce company processes customer transactions, inventory updates, and user activity logs across multiple AWS services. The company needs a scalable, fully managed, and event-driven orchestration solution to coordinate complex extract, transform, and load (ETL) workflows. The solution must use AWS Glue and Amazon EMR to process data. The data will be stored in Amazon Redshift and Amazon S3. The solution must support dependency management, automated retries, and data pipeline monitoring. Which solution will meet these requirements?
- A. Use AWS Step Functions to define an express workflow that invokes the data transformation and loading tasks across Amazon EMR and AWS Glue.
- B. Create AWS Lambda functions for each step of the workflow Configure Amazon EventBridge to invoke AWS Glue jobs. Configure the Lambda functions to process and move data through the pipeline.
- C. Use Apache Airflow on Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to create Directed Acyclic Graphs (DAGs) to manage ETL workflows.
- D. Create an AWS Lambda function that runs each step of the workflow. Create an Amazon EventBridge scheduled rule to invoke the function every day.
Answer: C
Question 16 · No. 272 · Single choice
A company needs to implement real-time analytics for a retail shopping platform. The company wants to capture clickstream data, process the data, and load the data into Amazon Redshift for analysis. The solution must handle hundreds of megabytes of data every second. Which solution will meet these requirements with the LEAST query latency for analytics?
- A. Use Amazon Data Firehose to capture the data. Store the data in an Amazon S3 bucket. Use the COPY command to load data into Amazon Redshift.
- B. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to capture the data. Use Amazon EMR to process the data. Use federated queries to access data in Amazon Redshift.
- C. Use Amazon Kinesis Data Streams to capture the data. Use Amazon Redshift streaming ingestion to load data directly into materialized views.
- D. Use Amazon DynamoDB Streams to capture the data. Use AWS Glue to process the data. Use a zero-ETL integration to load the data into Amazon Redshift.
Answer: C
Explanation: Option C is the best choice because Amazon Kinesis Data Streams is purpose-built for real-time streaming data at high throughput (hundreds of megabytes per second), making it ideal for clickstream data. Amazon Redshift streaming ingestion allows data from Kinesis Data Streams to be loaded directly into materialized views with minimal delay, providing the LEAST query latency. Option A uses Firehose with S3 and COPY, which is batch-oriented and has high latency. Option B uses federated queries which actually query external data rather than loading it into Redshift for analysis. Option D uses DynamoDB Streams, which is designed for capturing changes to DynamoDB tables, not for high-volume clickstream data capture.
Question 17 · No. 273 · Single choice
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.
Answer: A
Explanation: AWS Glue DataBrew is specifically designed for data profiling and data preparation. It can directly read Apache Parquet files from Amazon S3 without needing to move or copy the data to another location. DataBrew provides built-in profiling capabilities that automatically generate statistics and data quality insights with minimal operational overhead. The other options are less operationally efficient: Apache Flink (B) is designed for stream processing, not batch profiling; Redshift Spectrum (C) requires setting up and managing a Redshift cluster; and QuickSight (D) is a BI/visualization tool, not a dedicated data profiling service. Therefore, Option A is the most operationally efficient solution.
Question 18 · No. 274 · Single choice
A company uses an Amazon Redshift cluster to manage data, including vendor sales data. The company wants to store a copy of the vendor data in an Amazon S3 bucket. A data engineer sets up an AWS Glue job to upload the data to the S3 bucket data on a schedule. The data engineer set up a network connection to allow private traffic between Amazon Redshift and Amazon S3. What is the next step required to meet this requirement?
- A. Create an IAM role that has permission to write to the S3 bucket. Associate the IAM role with the Amazon Redshift cluster.
- B. Add the S3 bucket to an AWS Glue Data Catalog. Configure Amazon Redshift Spectrum to access the Data Catalog.
- C. Enable the Amazon Redshift data sharing feature. Set the S3 bucket as a target bucket for data sharing.
- D. Store login credentials for Amazon Redshift in AWS Secrets Manager. Add a reference to the secret to the Glue job configuration.
Answer: A
Explanation: The correct answer is A because the company needs IAM permissions to allow the data movement between Amazon Redshift and Amazon S3. Since the AWS Glue job is responsible for uploading vendor data to the S3 bucket, it requires an IAM role with permission to write to the S3 bucket. Associating this IAM role with the Amazon Redshift cluster (or the Glue job) establishes the necessary authentication and authorization for the private network connection to work properly. Option B (Redshift Spectrum) is incorrect because it's used for querying data in S3 from Redshift, not for uploading data to S3. Option C (Redshift data sharing) is for sharing data between Redshift clusters, not relevant here. Option D (Secrets Manager) is unnecessary because Glue can authenticate to Redshift using IAM roles without storing credentials in Secrets Manager.
Question 19 · No. 275 · Single choice
A data engineer needs a fully automated solution to check for new data in multiple databases and process data that the solution finds. The solution must run every hour. The solution must be compatible with Amazon RDS, Amazon DynamoDB, and Amazon OpenSearch Service. The solution must be able to process up to 10 MB of data at one time. The solution must be optimized for costs and operational overhead. The solution must have robust error handling capabilities. Which solution will meet these requirements?
- A. Use Amazon EventBridge to invoke AWS Step Functions every hour to deploy an AWS Lambda function to check for data. Configure Step Functions steps to process data that the Lambda function finds. Implement error handling in each state.
- B. Use Amazon EventBridge to invoke an AWS Lambda function every hour to check for data. Configure the function to send a message to an Amazon Simple Queue Service (Amazon SQS) queue when the function finds new data. Use a second Lambda function to read the queue and perform the processing.
- C. Configure an Apache Spark application to run on Amazon EMR to check for data. Implement error handling in the application. Use Amazon EventBridge to invoke the application every hour.
- D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to create a workflow that runs a directed acyclic graph (DAG) every hour to check for data. Configure the DAG to process identified data. Implement error handling in a Python operator.
Answer: B
Explanation: Option B is the best solution because it meets all requirements with optimal cost and operational efficiency. Amazon EventBridge can trigger an AWS Lambda function every hour to check for new data across Amazon RDS, Amazon DynamoDB, and Amazon OpenSearch Service. Lambda is compatible with all three database types and can process up to 10 MB of data efficiently. When new data is found, the first Lambda function sends a message to an Amazon SQS queue, which decouples the data checking from the data processing. A second Lambda function reads from the queue and processes the data. This architecture is cost-optimized because it uses only Lambda and SQS (both pay-per-use with no idle costs), has minimal operational overhead (fully managed services, no clusters to maintain), and provides robust error handling through SQS features such as Dead Letter Queues (DLQ) for failed messages, visibility timeouts, and automatic retries. Option A uses Step Functions which adds unnecessary cost for this use case. Option C uses Amazon EMR which has high operational overhead and cost. Option D uses Amazon MWAA which requires cluster management and has higher operational overhead.
Question 20 · No. 276 · Single choice
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.
Answer: B
Explanation: In Amazon Athena (built on Presto), the best practice for optimizing join performance is to place the larger table on the left side of the join and the smaller table on the right side. This is because Athena/Presto can broadcast the smaller table (on the right) to all worker nodes, which reduces the amount of data that needs to be shuffled across the network during the join operation. Options C and D are not standard practices for join order optimization in Athena, and option A would put the larger table on the right side, which is less efficient for broadcast joins.