Free Amazon AWS-DEA-C01 Exam Questions

Question 1

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 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 data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary. The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job. Which rule will meet these requirements?

A : DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0

B : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0

C : DatasetMatch "reference" "city->ref_city, state->ref_state" = 100

D : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 100

Answer: A

Question 4

A sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB. The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files. The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline. The company needs to improve the performance of the second pipeline. Which solution will meet this requirement MOST cost-effectively?

A : Use a larger worker type.

B : Increase the number of workers in the AWS Glue ETL jobs.

C : Use the AWS Glue DynamicFrame grouping option.

D : Enable AWS Glue auto scaling.

Answer: D

Question 5

A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi-nodeAmazon Redshift cluster. The company organizes the data files in the data lake based on the data source ofeach data file.The company loads all the data files into one table in the Redshift cluster by using a separate COPY commandfor each data file location. This approach takes a long time to load all the data files into the table. Thecompany must increase the speed of the data ingestion. The company does not want to increase the cost of theprocess.Which solution will meet these requirements?

A : Use a provisioned Amazon EMR cluster to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

B : Load all the data files in parallel into Amazon Aurora. Run an AWS Glue job to load the data into Amazon Redshift.

C : Use an AWS Glue job to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

D : Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.

Answer: D