Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A company stores its processed data in an S3 bucket. The company has a strict data access policy. Thecompany uses IAM roles to grant teams within the company different levels of access to the S3 bucket.The company wants to receive notifications when a user violates the data access policy. Each notificationmust include the username of the user who violated the policy.Which solution will meet these requirements?

A : Use AWS Config rules to detect violations of the data access policy. Set up compliance alarms.

B : Use Amazon CloudWatch metrics to gather object-level metrics. Set up CloudWatch alarms.

C : Use AWS CloudTrail to track object-level events for the S3 bucket. Forward events to Amazon CloudWatch to set up CloudWatch alarms.

D : Use Amazon S3 server access logs to monitor access to the bucket. Forward the access logs to an Amazon CloudWatch log group. Use metric filters on the log group to set up CloudWatch alarms.

Answer: C

Question 2

As a Cloud Data Engineering Consultant, you are assisting a client in setting up a data warehouse using Amazon Redshift. The client's data warehouse stores sensitive financial information and is used by various departments within the organization, each requiring different levels of access. The client emphasizes the need for secure data sharing practices that would allow specific teams to access only the data relevant to their work. They seek your advice on implementing a solution within Amazon Redshift to grant permissions effectively and securely for sharing data across different teams.

To meet the client's need for secure, role-specific data access in their Amazon Redshift data warehouse, which of the following approaches would you recommend?

A : Utilize Redshift Spectrum to create external schemas and tables, granting access to these external tables to specific IAM roles assigned to different teams.

B : Configure Amazon Redshift Security Groups to control which teams can access the data warehouse, assigning specific IP address ranges for each team.

C : Leverage Amazon Redshift's Row-Level Security (RLS) feature to control access to rows of data based on user attributes such as team or role, enabling fine-grained access control within shared tables.

D : Implement Redshift Data Sharing to share live data across different Redshift clusters, using Redshift's ALTER DATASHARE command to add and manage consumer clusters' access to the data.

Answer: C

Question 3

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 4

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 5

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