Free Amazon AWS-DEA-C01 Exam Questions

Question 1

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 2

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 3

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 4

A Data Engineering Team is tasked with enhancing the CI/CD workflow of their complex ETL pipeline on AWS. The pipeline involves frequent but varying updates—from minor feature additions to major architectural overhauls. The team uses GitHub for version control and manages a diverse array of AWS resources in their projects.

They require a CI/CD solution that not only automates build and test processes upon each commit but also allows for customizable, condition-based deployment strategies (e.g., deploying only specific branches or tags, or based on commit messages).

Given these requirements, which AWS service or combination of services would be the most effective?

A : AWS Lambda and AWS CodePipeline, where a Lambda function is triggered by GitHub webhooks to determine if the deployment should proceed, followed by CodePipeline managing the build, test, and deployment phases.

B : AWS CodeCommit and AWS CodePipeline, with CodeCommit to replace GitHub for enhanced integration within the AWS ecosystem, and CodePipeline to orchestrate the entire process including condition-based deployments.

C : AWS CodePipeline, integrating both GitHub and AWS CodeBuild. CodePipeline to manage the overall workflow and CodeBuild for the build and testing phases, leveraging pipeline actions for deployment conditions.

D : AWS CodeBuild and AWS CodeDeploy, with CodeBuild handling the build and test processes and CodeDeploy managing advanced deployment strategies based on GitHub webhook events.

Answer: C

Question 5

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