Online Access Free DEA-C01 Practice Test

A company needs to implement a data mesh architecture in which domains for trading, risk, and compliance teams each have own their data. The teams need to share specific views with one another. The teams have over 1,000 tables across 50 databases in AWS Glue Data Catalog. All three teams use Amazon Athena to perform on-demand analysis. The teams use Amazon Redshift to generate complex reports. The compliance team must audit all data access. Access to personally identifiable information (PII) data must be restricted.
The company requires a scalable solution to meet the team requirements. The solution must provide the ability to perform analysis across team domains.
Which solution will meet these requirements?

• A.Use AWS Glue Data Catalog views to perform analysis. Use AWS CloudTrail logs to audit data access. Use AWS Lake Formation to manage access permissions. Use security definer views to mask PII.
• B.Create views in Athena for on-demand analysis. Use the Athena views in Amazon Redshift to perform cross-domain analytics. Use AWS CloudTrail to audit data access. Use AWS Lake Formation to establish fine-grained access control.
• C.Use AWS Lake Formation to set up cross-domain access to tables. Set up fine-grained access controls.
• D.Create materialized views and enable Amazon Redshift datashares for each domain. Configure cross-domain access policies.

A company is creating a new data pipeline to populate a data lake. A data analyst needs to prepare and standardize the data before a data engineering team can perform advanced data transformations. The data analyst needs a solution to process the data that does not require writing new code.
Which solution will meet these requirements with the LEAST operational effort?

• A.Use Amazon SageMaker Canvas and SageMaker Data Wrangler to write to a new dataset.
  Ensure that the data engineers add additional transformations to complete the pipeline by using AWS Glue.
• B.Use AWS Glue Studio with data preparation recipe transformations. Ensure that the data engineers add additional transformations to complete the pipeline.
• C.Create a document that includes the data preparation rules. Ensure that the data engineers implement the rules in AWS Glue.
• D.Use Python and Pandas in an AWS Glue Studio notebook. Ensure that the data engineers add additional transformations to complete the pipeline.

A data engineer set up an AWS Lambda function to read an object that is stored in an Amazon S3 bucket. The object is encrypted by an AWS KMS key.
The data engineer configured the Lambda function's execution role to access the S3 bucket.
However, the Lambda function encountered an error and failed to retrieve the content of the object.
What is the likely cause of the error?

• A.The S3 bucket is located in a different AWS Region than the Region where the data engineer works. Latency issues caused the Lambda function to encounter an error.
• B.The Lambda function is using an outdated SDK version, which caused the read failure.
• C.The data engineer misconfigured the permissions of the S3 bucket. The Lambda function could not access the object.
• D.The Lambda function's execution role does not have the necessary permissions to access the KMS key that can decrypt the S3 object.

A company stores customer data in an Amazon S3 bucket. The company must permanently delete all customer data that is older than 7 years. Which solution will meet this requirement?

• A.Use Amazon Athena to query the S3 bucket for objects that are older than 7 years. Configure Athena to delete the results.
• B.Configure an S3 Lifecycle policy to enable S3 Object Lock on all objects that are older than 7 years.
• C.Configure an S3 Lifecycle policy to move objects that are older than 7 years to S3 Glacier Deep Archive.
• D.Configure an S3 Lifecycle policy to permanently delete objects that are older than 7 years.

A company needs a solution to process streaming data by using Apache Spark in a Kubernetes environment. The solution must support event-driven scaling and optimize resource utilization.
The company needs to integrate the solution with existing Kubernetes infrastructure deployed on Amazon Elastic Kubernetes Service (Amazon EKS).
Which solution will meet these requirements with the LEAST operational overhead?

• A.Use Amazon EMR on Amazon EKS with Kubernetes Event-Driven Autoscaling (KEDA) for event- driven scaling of Spark applications.
• B.Implement Apache Spark on Amazon EKS with AWS Auto Scaling groups and custom scaling policies based on metrics events collected by the Kubernetes Metrics Server.
• C.Deploy self-managed Apache Spark on Amazon EKS by using custom auto-scaling policies based on Kubernetes Event-Driven Autoscaling (KEDA) operators.
• D.Use Amazon EMR on Amazon EKS with the Kubernetes Cluster Autoscaler to scale Spark applications based on metrics events generated by the Kubernetes Metrics Server.