Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A Cloud Data Engineering Consultant has been tasked with organizing access control for a team of analysts using an Amazon Redshift data warehouse. The team requires varying levels of access to sensitive financial data for reporting and analysis. The consultants need to define a strategy that provides the necessary access for the analysts to perform their duties while ensuring that principles of least privilege and data governance are upheld.

Which of the following actions should the consultant take to appropriately manage user access and permissions within the Amazon Redshift database? (Select TWO)

A : Implement row-level security within Redshift tables to control access to rows of data based on user roles, ensuring analysts only see data relevant to their analysis.

B : Grant superuser privileges to all analysts to ensure they have the ability to query all necessary tables within the Redshift cluster, then audit queries periodically for compliance.

C : Enable Amazon Redshift Spectrum to manage external tables and use IAM policies to grant access to these tables.

D : Set up database groups within Redshift and assign users to groups based on their access requirements, then grant group-specific permissions to the relevant schemas and tables.

E : Create individual IAM users for each analyst with attached policies that define the level of access to Redshift resources, and use those IAM users for database authentication.

Answer: A,D

Question 2

A company needs to load customer data that comes from a third party into an Amazon Redshift datawarehouse. The company stores order data and product data in the same data warehouse. The company wantsto use the combined dataset to identify potential new customers.A data engineer notices that one of the fields in the source data includes values that are in JSON format.How should the data engineer load the JSON data into the data warehouse with the LEAST effort?

A : Use the SUPER data type to store the data in the Amazon Redshift table.

B : Use AWS Glue to flatten the JSON data and ingest it into the Amazon Redshift table.

C : Use Amazon S3 to store the JSON data. Use Amazon Athena to query the data.

D : Use an AWS Lambda function to flatten the JSON data. Store the data in Amazon S3.

Answer: A

Question 3

Files from multiple data sources arrive in an Amazon S3 bucket on a regular basis. A data engineer wants to ingest new files into Amazon Redshift in near real time when the new files arrive in the S3 bucket. Which solution will meet these requirements?

A : Use the query editor v2 to schedule a COPY command to load new files into Amazon Redshift.

B : Use the zero-ETL integration between Amazon Aurora and Amazon Redshift to load new files into Amazon Redshift.

C : Use AWS Glue job bookmarks to extract, transform, and load (ETL) load new files into Amazon Redshift.

D : Use S3 Event Notifications to invoke an AWS Lambda function that loads new files into Amazon Redshift.

Answer: D

Question 4

A Data Engineering Team is responsible for managing and monitoring a large-scale data processing application using AWS services. The application periodically processes vast amounts of unstructured data and stores the results in Amazon S3. The team needs to set up a system to closely monitor the data processing jobs and get alerted if any job fails or underperforms.

Which combination of AWS services should the data engineering team use for effective monitoring and alerting?

A : Leverage AWS Step Functions to manage the data processing workflows and use Amazon CloudWatch alarms to monitor each step's execution status.

B : Use Amazon CloudWatch for monitoring, combined with AWS Lambda functions to check the health of the data processing jobs and send notifications using Amazon SNS.

C : Implement Amazon QuickSight for real-time analytics on the job performance data and set up Amazon SES to email the reports to the team.

D : Utilize AWS X-Ray for tracing the processing jobs and integrate it with Amazon SES to alert the team about any anomalies or failures.

Answer: B

Question 5

A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. TheETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, andload data into Amazon Redshift.The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python toorchestrate the jobs.Which service will meet these requirements?

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

B : AWS Step Functions

C : AWS Glue

D : Amazon EventBridge

Answer: A