Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A Cloud Data Engineering Team is working on optimizing the query performance of an Amazon Redshift cluster, serving as the primary data warehouse for their organization. The warehouse contains a large-scale dataset, including extensive historical sales data.

The team has identified that the query performance issues are mainly due to inefficient data scanning and retrieval processes, especially in tables with complex query patterns. They need a solution that specifically optimizes the way data is stored and accessed in these tables to reduce execution times for complex queries.

Which of the following techniques should they implement to improve the query performance on these large tables?

A : Increase the number of nodes in the Redshift cluster to boost the overall processing power and disk I/O, enhancing the query performance on large datasets.

B : Enable automatic vacuum delete on the Redshift cluster to purge the deleted tuples, thus improving query performance on large tables.

C : Use Amazon Redshift Spectrum to offload some of the older, less frequently accessed data to Amazon S3, and query directly from S3 when needed.

D : Implement columnar storage and define SORTKEYs on frequently queried columns in these large tables to optimize data scanning.

Answer: D

Question 2

A company has as JSON file that contains personally identifiable information (PIT) data and non-PII data. The company needs to make the data available for querying and analysis. The non-PII data must be available to everyone in the company. The PII data must be available only to a limited group of employees. Which solution will meet these requirements with the LEAST operational overhead?

A : Store the JSON file in an Amazon S3 bucket. Configure AWS Glue to split the file into one file that contains the PII data and one file that contains the non-PII data. Store the output files in separate S3 buckets. Grant the required access to the buckets based on the type of user.

B : Store the JSON file in an Amazon S3 bucket. Use Amazon Macie to identify PII data and to grant access based on the type of user.

C : Store the JSON file in an Amazon S3 bucket. Catalog the file schema in AWS Lake Formation. Use Lake Formation permissions to provide access to the required data based on the type of user.

D : Create two Amazon RDS PostgreSQL databases. Load the PII data and the non-PII data into the separate databases. Grant access to the databases based on the type of user.

Answer: C

Question 3

A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must

crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the

output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.

Which AWS service or feature will meet these requirements MOST cost-effectively?

A : AWS Step Functions

B : AWS Glue workflows

C : AWS Glue Studio

D : Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

Answer: B

Question 4

A financial services company is storing time-series data in a relational database hosted on Amazon RDS. The company wants to improve query performance for reports that are run on a monthly basis, which involve large scans over historical data. The solution should optimize costs and maintain current data integrity constraints.

Which approach should the company take?

A : Migrate the database to Amazon Redshift and utilize Redshift's materialized views to optimize the query performance for repetitive and predictable analysis patterns.

B : Use Amazon RDS for MySQL and implement read replicas to distribute the read load during the execution of large scan operations.

C : Convert the database to use Amazon Aurora with MySQL compatibility and implement Aurora's parallel query feature for faster data scans.

D : Migrate the database to Amazon DynamoDB and use DynamoDB Accelerator (DAX) to cache frequent read requests and improve response times.

Answer: A

Question 5

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.

Answer: B