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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions:

1. 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 Amazon Aurora with a Multi-AZ deployment to store product data. Use read replicas. Create indexes for primary and secondary attributes.
B) Use Amazon ElastiCache (Redis OSS) and Amazon S3 to store product data. Use Amazon Athena to run flexible secondary attribute queries.
C) 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.
D) Use an Amazon OpenSearch Serverless cluster with dynamic scaling to store product data. Index product data by primary and secondary attributes.

2. A company uses Amazon Redshift to store order transactions from the current day. The company has an orders table that contains the previous order data. The company also has a staging table that contains new or updated order records. The company needs to remove stale records from the orders table and insert the most recent data in the orders table from the staging table. Several downstream applications need the orders table to display up-to-date information.
Which solution will meet these requirements?

A) Use Amazon Athena federated queries to read stale records from the orders table. Delete the stale records and insert the records from the staging table into the orders table.
B) Write an Amazon Redshift stored procedure that deletes the stale records from the orders table and inserts new records from the staging table.
C) Use Amazon Redshift Spectrum to delete stale records from the orders table and insert records from the staging table into the orders table.
D) Unload the orders table and the staging table to Amazon S3. Delete stale orders table data and insert new staging table data in Amazon S3 by using Amazon Athena. Copy the orders S3 table to the orders Amazon Redshift table.

3. A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

A) Turn on concurrency scaling in the settings during the creation of and new Redshift cluster.
B) Turn on concurrency scaling for the daily usage quota for the Redshift cluster.
C) Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
D) Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.

4. A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks.
The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team ' s BI cluster.
The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.
Which solution will meet these requirements?

A) Create materialized views based on the sales team ' s requirements. Grant the sales team direct access to the ETL cluster.
B) Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.
C) Create database views based on the sales team ' s requirements. Grant the sales team direct access to the ETL cluster.
D) Set up the sales team Bl cluster as a consumer of the ETL cluster by using Redshift data sharing.

5. 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) Integrate Tableau with Amazon Redshift to give Tableau direct access to the data.
B) Use Quick Suite dashboards that have federated query access to Amazon Redshift.
C) Use Quick Suite dashboards that have zero-ETL access to Amazon Redshift.
D) Enable Amazon Q in Quick Suite. Generate Quick Suite dashboards and reports.

Solutions: