DEA-C01 Question Bank · Latest 50 Questions Free Preview (Page 4/5)
Questions 31–40 of the latest 50 DEA-C01 questions (5 pages total), with answers and explanations, no login required. Practice all 306 questions online.
Question 31 · No. 287 · Single choice
A legal company is building a data pipeline to power an application that will handle peak traffic during business hours. The application will provide information about relevant laws and available lawyers. The legal document database will be updated one time each day. The application must display up-to-date lawyer availability from a calendar database and provide complex full-text search of legal documents. The company wants to use AWS Glue for extract, transform, and load (ETL) processes. Lawyer availability information must be current within 5 minutes of any schedule changes. Which solution will meet these requirements with the LEAST operational overhead?
- A. Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon RDS for the search functionality.
- B. Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon OpenSearch Service for full-text search capabilities.
- C. Use AWS Glue workflows with time-based triggers and event-based triggers. Store processed data in Amazon DynamoDB. Create a custom search solution by using AWS Lambda functions.
- D. Use Amazon EventBridge to schedule all AWS Glue jobs. Store processed data in Amazon RDS. Use Amazon Kendra for full-text search capabilities.
Answer: B
Explanation: Option B is the best choice because it uses AWS Step Functions to orchestrate AWS Glue jobs with both time-based triggers (for daily legal document updates) and event-based triggers (to capture lawyer availability changes within 5 minutes). Storing processed data in Amazon S3 is cost-effective and integrates well with the pipeline. Amazon OpenSearch Service is a managed service specifically designed for complex full-text search capabilities, which meets the requirement for searching legal documents with minimal operational overhead. Option A uses RDS which is not optimized for full-text search. Option C requires building a custom search solution with Lambda, which increases operational overhead. Option D uses Amazon Kendra (designed for ML-powered enterprise search, not complex full-text search) and RDS, which would add more operational complexity.
Question 32 · No. 288 · Single choice
A data engineer needs to deploy a serverless data pipeline. In the pipeline, CSV files are uploaded to an Amazon S3 bucket, which invokes an AWS Lambda function. The Lambda function transforms the CSV files to JSON format and stores the results in a second S3 bucket. The data engineer has created an AWS Serverless Application Model (AWS SAM) template that includes the Lambda function. The data engineer wants to use AWS SAM for the pipeline deployment. Which solution will package and deploy this serverless data pipeline?
- A. Add the first S3 bucket and the S3 event source for the Lambda function to the SAM template. Run the sam build command to prepare the deployment package. Run the sam deploy --guided command to deploy the pipeline.
- B. Run the sam deploy command directly with the --s3-bucket parameter to deploy the Lambda function code. Manually configure the S3 event trigger in the AWS Management Console.
- C. Add the first S3 bucket to the SAM template. Run the sam package template to upload the Lambda function code to Amazon S3. Create an AWS CloudFormation stack from the packaged template. Configure event notifications manually.
- D. Add the first S3 bucket and the S3 event source for the Lambda function to the SAM template. Run the sam build command followed by the aws cloudformation deploy command to deploy the pipeline.
Answer: A
Explanation: Option A is the correct answer because it follows the standard AWS SAM workflow for packaging and deploying serverless applications. It includes the S3 bucket and the S3 event source for the Lambda function in the SAM template (infrastructure as code), uses 'sam build' to prepare the deployment package, and uses 'sam deploy --guided' which is the SAM-specific command to deploy the application interactively. Option B is incorrect because it requires manual configuration in the console, which doesn't fully utilize SAM's infrastructure-as-code capabilities. Option C is outdated because 'sam package' has been deprecated in favor of 'sam build' + 'sam deploy', and it also requires manual event notification configuration. Option D, while functional, uses 'aws cloudformation deploy' instead of the more SAM-specific 'sam deploy' command, making it less aligned with the AWS SAM workflow.
Question 33 · No. 289 · Single choice
A company stores objects in an Amazon S3 bucket. The company crawls the objects so that Amazon Athena can query the data. A data engineer manually moved all objects from the partition with a path prefix of status=01 to the prefix status=02. The status=01 partition location is now empty. However, the status=01 partition location still appears in the AWS Glue Data Catalog metadata. Which Athena command should the data engineer run to resolve the metadata discrepancy?
- A. MSCK REPAIR TABLE
- B. ALTER TABLE DROP PARTITION
- C. ALTER TABLE SET TBLPROPERTIES
- D. ALTER TABLE CHANGE COLUMN
Answer: B
Explanation: The data engineer manually moved all objects from the status=01 partition to the status=02 partition, leaving the status=01 partition location empty. However, the status=01 partition still appears in the AWS Glue Data Catalog metadata. To resolve this discrepancy, the data engineer needs to remove the status=01 partition from the metadata. The MSCK REPAIR TABLE command only adds metadata for partitions that exist in the file system but are missing from the catalog; it does not remove existing partitions from the catalog. ALTER TABLE SET TBLPROPERTIES is used to set table properties, and ALTER TABLE CHANGE COLUMN is used to modify column definitions. Therefore, the correct command is ALTER TABLE DROP PARTITION, which explicitly removes the status=01 partition from the Glue Data Catalog metadata.
Question 34 · No. 290 · Single choice
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. 请提供需要翻译的英文内容。
- B. 请提供需要翻译的英文内容，我才能为您进行翻译。
- C. 请提供需要翻译的英文内容。
- D. 请提供需要翻译的英文内容。
Answer: A
Question 35 · No. 291 · Single choice
A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive maintenance models. A data engineer needs to implement automated data quality checks to identify temperature readings that are outside the expected range of -50°C to 150°C. The data quality checks must also identify records that are missing timestamp values. The data engineer needs a solution that requires minimal coding and can automatically flag the specified issues. Which solution will meet these requirements?
- A. Create an AWS Glue DataBrew project to profile the sensor data Define completeness rules for timestamps. Set up numeric range validation for temperature values.
- B. Use AWS Glue’s Data Quality rules and machine learning (ML)-based anomaly detection to identify missing timestamps and to detect temperature anomalies.
- C. Create an AWS Lambda function to scan the sensor data files to validate temperature ranges. Use AWS Glue Data Catalog tables to check timestamp completeness.
- D. Create an AWS Glue DynamicFrame that uses a custom data quality operator to profile the sensor data. Use Amazon SageMaker Data Wrangler transforms to validate timestamps and temperature ranges.
Answer: A
Explanation: Option A is the correct answer. AWS Glue DataBrew is a visual data preparation tool that requires minimal coding and is ideal for this scenario. It allows the data engineer to profile the sensor data, define completeness rules to identify missing timestamp values, and set up numeric range validation to flag temperature readings outside the -50°C to 150°C range. This meets all requirements: minimal coding, automatic flagging of data quality issues, and validation of both temperature ranges and timestamp completeness. Option B is incorrect because it relies on ML-based anomaly detection for temperature, which detects statistical outliers rather than explicit business-defined ranges. Option C requires custom Lambda function code, violating the minimal coding requirement. Option D is overly complex, involving custom DynamicFrame operators and SageMaker Data Wrangler, which requires significant coding and configuration.
Question 36 · No. 292 · Single choice
Two data engineering teams use separate AWS accounts. Both teams request access to the same datashare in an Amazon Redshift cluster that is in a third AWS account. The datashare is named salesshare. A data engineer must use the Amazon Redshift SQL interface to grant both data engineering teams' access to the datashare. Which command or commands will meet this requirement?
- A. GRANT USAGE ON DATASHARE salesshare TO ACCOUNTS ‘’ AND ‘’;
- B. GRANT USAGE ON DATASHARE salesshare TO NAMESPACES ‘’ AND ‘’;
- C. GRANT USAGE ON DATASHARE salesshare TO ACCOUNT ‘’;
GRANT USAGE ON DATASHARE salesshare TO ACCOUNT ‘’;
- D. GRANT USAGE ON DATASHARE salesshare TO NAMESPACE ‘’;
GRANT USAGE ON DATASHARE salesshare TO NAMESPACE ‘’;
Answer: C
Explanation: The correct answer is C. In Amazon Redshift, when granting access to a datashare for multiple AWS accounts, you need to use separate GRANT statements for each account. Option A uses incorrect syntax - Redshift uses the ACCOUNT keyword (singular), not ACCOUNTS, and the correct separator is a comma, not AND. Option B and D use NAMESPACES, which is incorrect because namespaces are used within the same AWS account, not across different accounts. Since the two data engineering teams are in separate AWS accounts, Option C is the correct approach using two separate GRANT USAGE statements, one for each account.
Question 37 · No. 293 · Single choice
A company runs an Apache Spark application every night in an Amazon EMR cluster. The company uses Amazon EC2 instances to supply compute capacity for the EMR cluster. The company deployed the Spark application in cluster mode. An error occurs in the Spark application. A log for the error is stored in the application's Spark driver standard error logs. A data engineer needs to investigate the error. Where can the data engineer find this error log?
- A. The engineer can connect to the web UI on the live cluster to see the YARN ResourceManager logs.
- B. The engineer can connect to the persistent application UI to see the first YARN container log in the Spark UI.
- C. The engineer can connect to the Amazon EMR console to see the Amazon EMR step logs that are archived in Amazon S3.
- D. The engineer can connect to the primary node of the cluster by using SSH to see the Spark history server logs.
Answer: B
Explanation: In Spark cluster mode, the Spark driver runs inside a YARN container. Its standard error output is collected with that container's logs and can be viewed in the persistent Spark application UI. The YARN ResourceManager logs contain cluster-level information, EMR step logs may not include the complete driver error log, and the Spark History Server's own logs are not the application driver's error log.
Question 38 · No. 294 · Single choice
A company needs to build a data pipeline to process a 1-TB file from an Amazon S3 bucket. The pipeline needs to create three DataFrames based on business logic. The pipeline must save all three DataFrames to a second S3 bucket in parallel. The company needs to set the pipeline to be the target of an Amazon EventBridge rule that matches file uploads to the source S3 bucket. Which solution will meet these requirements with the LEAST maintenance overhead?
- A. Configure an Apache Spark Streaming application on Amazon EMR to process data from the S3 source bucket in batches, create DataFrames, and save the output to the destination S3 bucket.
- B. Configure three AWS Lambda functions to process the business logic and to save the DataFrames to the destination S3 bucket in parallel.
- C. Configure an AWS Glue workflow to run three AWS Glue jobs in parallel to process the file.
- D. Configure an AWS Step Functions state machine to initiate an AWS Glue workflow to run three AWS Glue jobs in parallel to process the file.
Answer: C
Explanation: Option C is the best choice because AWS Glue is a serverless ETL service that requires minimal maintenance overhead. AWS Glue workflows can orchestrate three Glue jobs to run in parallel, process large data files (1-TB is well within Glue's capabilities), and can be directly triggered by an Amazon EventBridge rule. Option A (EMR) requires more maintenance because you need to manage clusters, patching, and scaling. Option B (Lambda) is impractical for processing 1-TB files due to execution time limits and resource constraints. Option D (Step Functions + Glue workflow) adds unnecessary complexity with an additional orchestration layer, increasing maintenance overhead.
Question 39 · No. 295 · Single choice
A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand. Which solution will meet these requirements?
- A. Use an Amazon DynamoDB table with on-demand capacity to store product data. Store products by primary key. Use global secondary indexes (GSIs) to store secondary attributes.
- B. Use Amazon Aurora with a Multi-AZ deployment to store product data. Use read replicas. Create indexes for primary and secondary attributes.
- C. Use an Amazon OpenSearch Serverless cluster with dynamic scaling to store product data. Index product data by primary and secondary attributes.
- D. Use Amazon ElastiCache (Redis OSS) and Amazon S3 to store product data. Use Amazon Athena to run flexible secondary attribute queries.
Answer: A
Explanation: Amazon DynamoDB with on-demand capacity is the ideal solution for this scenario because it provides: 1) On-demand capacity that automatically handles unpredictable traffic spikes without manual intervention; 2) Single-digit millisecond latency at any scale, which meets the performance requirement; 3) Flexible schema (NoSQL) that naturally supports variable product attributes without schema migrations; 4) Primary key-based retrieval for Product ID lookups; 5) Global Secondary Indexes (GSIs) that enable flexible querying on secondary attributes like Category and Brand. Option B (Aurora) requires fixed schema and struggles with sudden traffic spikes. Option C (OpenSearch Serverless) typically has higher latency than single-digit milliseconds. Option D (ElastiCache + Athena) cannot meet the low latency requirement as Athena queries on S3 are not single-digit millisecond responses.
Question 40 · No. 296 · Single choice
A company must retain specific data for 1 year. A data engineer observes that one of the company’s Amazon S3 buckets contains millions of objects that are older than 3 years. Versioning is enabled on the bucket. To reduce costs, the data engineer implements an S3 Lifecycle rule to expire objects after 365 days. The new S3 Lifecycle rule causes the object count to double instead of decrease. Which additional step must the data engineer take to permanently delete the old objects?
- A. Disable versioning on the S3 bucket.
- B. Use an AWS Lambda function to run a Python job to identify and delete objects that are older than 365 days.
- C. Suspend versioning on the S3 bucket.
- D. Add an additional S3 Lifecycle rule to delete the current and expired versions of objects that are older than 365 days.
Answer: D
Explanation: When versioning is enabled on an S3 bucket, a lifecycle rule that expires current objects only creates delete markers; the previous versions remain as noncurrent versions in the bucket. This is why the object count doubled—the current versions got delete markers, but all the previous versions are still stored. To permanently delete old objects, the data engineer needs an additional lifecycle rule that explicitly deletes the noncurrent (previous) versions of objects. Option D correctly addresses this by adding a rule to delete both current and expired versions of objects older than 365 days. Options A and C (disable or suspend versioning) do not remove existing noncurrent versions. Option B (using Lambda) is not a managed solution and adds operational overhead compared to native S3 Lifecycle rules.