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DEA-C01 Free Practice Questions — Page 12

Question 112

A company stores server logs in an Amazon S3 bucket. The company needs to keep the logs for 1 year. The logs are not required after 1 year. A data engineer needs a solution to automatically delete logs that are older than 1 year. Which solution will meet these requirements with the LEAST operational overhead?

A. Define an S3 Lifecycle configuration to delete the logs after 1 year.
B. Create an AWS Lambda function to delete the logs after 1 year.
C. Schedule a cron job on an Amazon EC2 instance to delete the logs after 1 year.
D. Configure an AWS Step Functions state machine to delete the logs after 1 year.
Show Answer
Correct Answer: A
Explanation:
Amazon S3 Lifecycle rules are the native, fully managed way to automatically expire and delete objects after a specified retention period. This meets the 1-year retention requirement with the least operational overhead, unlike Lambda, EC2 cron jobs, or Step Functions, which require additional infrastructure or code maintenance.

Question 113

A data engineer created a table named cloudtrail_logs in Amazon Athena to query AWS CloudTrail logs and prepare data for audits. The data engineer needs to write a query to display errors with error codes that have occurred since the beginning of 2024. The query must return the 10 most recent errors. Which query will meet these requirements?

A. select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logswhere errorcode is not nulland eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessageorder by TotalEvents desclimit 10;
B. select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logs where eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessage order by TotalEvents desc limit 10;
C. select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logswhere eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessageorder by eventname asc limit 10;
D. select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logs where errorcode is not nulland eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessagelimit 10;
Show Answer
Correct Answer: A
Explanation:
The requirement is to display only error events since the beginning of 2024. Filtering with `errorcode IS NOT NULL` excludes successful events, and `eventtime >= '2024-01-01T00:00:00Z'` restricts the time range. Among the choices, only A includes both filters. B does not filter to errors, C neither filters errors nor sorts appropriately, and D does not specify an ordering before limiting results. Although the wording says '10 most recent errors', none of the options orders by event time; A is still the best match because it is the only one that correctly filters for error events in the required time range.

Question 114

A company has an Amazon Redshift data warehouse that users access by using a variety of IAM roles. More than 100 users access the data warehouse every day. The company wants to control user access to the objects based on each user's job role, permissions, and how sensitive the data is. Which solution will meet these requirements?

A. Use the role-based access control (RBAC) feature of Amazon Redshift.
B. Use the row-level security (RLS) feature of Amazon Redshift.
C. Use the column-level security (CLS) feature of Amazon Redshift.
D. Use dynamic data masking policies in Amazon Redshift.
Show Answer
Correct Answer: A
Explanation:
Role-based access control (RBAC) in Amazon Redshift is designed to manage object permissions based on job roles and can be mapped to IAM-based access patterns. Row-level security and column-level security address only specific dimensions of data access, and dynamic data masking obscures sensitive values but does not provide comprehensive authorization. Because the requirement is to control access to database objects according to users' roles, permissions, and data sensitivity across many users, RBAC is the appropriate overall solution.

Question 115

An online retailer uses multiple delivery partners to deliver products to customers. The delivery partners send order summaries to the retailer. The retailer stores the order summaries in Amazon S3. Some of the order summaries contain personally identifiable information (PII) about customers. A data engineer needs to detect PII in the order summaries so the company can redact the PII. Which solution will meet these requirements with the LEAST operational overhead?

A. Amazon Textract
B. Amazon S3 Storage Lens
C. Amazon Macie
D. Amazon SageMaker Data Wrangler
Show Answer
Correct Answer: C
Explanation:
Amazon Macie automatically discovers and identifies sensitive data, including personally identifiable information (PII), in Amazon S3 using managed data identifiers. It is the managed service designed for PII detection in S3 and requires the least operational overhead. Amazon Textract extracts text from documents but does not specialize in PII discovery. S3 Storage Lens provides storage analytics, and SageMaker Data Wrangler is for data preparation, not automated PII detection.

Question 116

A company uses Amazon DataZone as a data governance and business catalog solution. The company stores data in an Amazon S3 data lake. The company uses AWS Glue with an AWS Glue Data Catalog. A data engineer needs to publish AWS Glue Data Quality scores to the Amazon DataZone portal. Which solution will meet this requirement?

A. Create a data quality ruleset with Data Quality Definition language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.
B. Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.
C. Create a data quality ruleset with Data Quality Definition language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.
D. Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.
Show Answer
Correct Answer: C
Explanation:
Amazon DataZone can display AWS Glue Data Quality results for assets discovered through an AWS Glue data source when data quality is enabled. The supported pattern is to create AWS Glue Data Quality rulesets (DQDL) attached to Glue tables and run them on a schedule so scores are written for the cataloged tables. Amazon Redshift is not the data source in this scenario, and using the Evaluate Data Quality ETL transform alone is not the integration required for publishing Data Quality scores into Amazon DataZone.

Question 117

A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse. Which solution will meet these requirements?

A. Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.
B. Create an Amazon Elastic File System (Amazon EFS) file system. Enable logging for the Amazon Redshift cluster. Write logs to the EFS file system.
C. Create an Amazon Aurora MySQL database. Enable logging for the Amazon Redshift cluster. Write the logs to a table in the Aurora MySQL database.
D. Create an Amazon Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.
Show Answer
Correct Answer: A
Explanation:
Amazon Redshift supports audit logging for user activity, user connections, and database activity by enabling audit logging and delivering the log files to an Amazon S3 bucket. EFS, Aurora MySQL, and EBS are not supported destinations for Redshift audit logs.

Question 118

A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

A. Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.
B. Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.
C. Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.
D. Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.
Show Answer
Correct Answer: B
Explanation:
AWS Schema Conversion Tool (AWS SCT) is the recommended service to convert heterogeneous database schemas such as Teradata to Amazon Redshift. After the schema is converted and applied, AWS Database Migration Service (AWS DMS) is used to migrate the data with minimal operational effort. AWS DMS alone does not provide full heterogeneous schema conversion, and manual schema migration requires more effort.

Question 119

A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries. The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes. Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Redshift Serverless to load all the data into Amazon Redshift managed storage (RMS).
B. Use Amazon Athena to load all the data into Amazon S3 in Apache Parquet format.
C. Use Amazon Redshift provisioned clusters to load all the data into Amazon Redshift managed storage (RMS).
D. Use Amazon Aurora PostgreSQL to load all the data into Aurora.
Show Answer
Correct Answer: A
Explanation:
Amazon Redshift Serverless is designed for analytics workloads with unpredictable usage patterns. It provides a fully managed, serverless data warehouse that automatically scales based on demand, minimizing operational overhead while delivering fast query performance for Amazon QuickSight direct queries. Athena is a query service rather than a central data warehouse and may not provide the same consistent interactive performance for BI dashboards. Redshift provisioned clusters require capacity planning and management. Aurora PostgreSQL is an OLTP database and is not the best fit for an enterprise analytics data warehouse.

Question 120

A company has AWS resources in multiple AWS Regions. The company has an Amazon EFS file system in each Region where the company operates. The company’s data science team operates within only a single Region. The data that the data science team works with must remain within the team's Region. A data engineer needs to create a single dataset by processing files that are in each of the company's Regional EFS file systems. The data engineer wants to use an AWS Step Functions state machine to orchestrate AWS Lambda functions to process the data. Which solution will meet these requirements with the LEAST effort?

A. Peer the VPCs that host the EFS file systems in each Region with the VPC that is in the data science team’s Region. Enable EFS file locking. Configure the Lambda functions in the data science team's Region to mount each of the Region specific file systems. Use the Lambda functions to process the data.
B. Configure each of the Regional EFS file systems to replicate data to the data science team's Region. In the data science team’s Region, configure the Lambda functions to mount the replica file systems. Use the Lambda functions to process the data.
C. Deploy the Lambda functions to each Region. Mount the Regional EFS file systems to the Lambda functions. Use the Lambda functions to process the data. Store the output in an Amazon S3 bucket in the data science team’s Region.
D. Use AWS DataSync to transfer files from each of the Regional EFS files systems to the file system that is in the data science team's Region. Configure the Lambda functions in the data science team's Region to mount the file system that is in the same Region. Use the Lambda functions to process the data.
Show Answer
Correct Answer: C
Explanation:
The requirement states that the data the data science team works with must remain within the team's Region. Replicating or transferring raw EFS data from other Regions into the data science Region (B and D) violates that locality requirement. EFS cannot be mounted cross-Region through VPC peering as suggested in A, so A is not feasible. Deploying Lambda functions in each Region to process the local EFS data and sending only the processed output to a central S3 bucket in the data science team's Region satisfies the locality requirement with Step Functions orchestrating the regional Lambdas. Sources: https://aws.amazon.com/efs/faq

Question 121

A data engineer is using Amazon QuickSight to build a dashboard to report a company’s revenue in multiple AWS Regions. The data engineer wants the dashboard to display the total revenue for a Region, regardless of the drill-down levels shown in the visual. Which solution will meet these requirements?

A. Create a table calculation.
B. Create a simple calculated field.
C. Create a level-aware calculation - aggregate (LAC-A) function.
D. Create a level-aware calculation - window (LAC-W) function.
Show Answer
Correct Answer: C
Explanation:
Level-aware calculation - aggregate (LAC-A) functions compute aggregations at a specified level of detail independent of the visual's drill-down level. This allows the dashboard to always show the total revenue for each Region regardless of the dimensions currently displayed. Table calculations and LAC-W operate over the displayed result set, and a simple calculated field cannot fix the aggregation level.

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