Question 1

A company is deploying a stateful application on Amazon EC2 that requires temporary storage for processing large datasets with maximum I/O. The application must also maintain a smaller subset of essential state data that needs to persist beyond the life of the instance. The company needs a storage solution that ensures the essential data is not lost if the EC2 instance is stopped or terminated.

Which combination of storage solutions should the company choose?

Options :

A : Attach Amazon EBS volumes for essential state data and use Amazon DynamoDB for temporary data.

B : Attach additional Amazon EBS volumes for temporary data processing and rely on the root device volume for essential state data.

C : Attach Amazon EC2 instance store volumes for temporary data processing and Amazon Elastic Block Store (EBS) for essential state data.

D : Implement a combination of Amazon EC2 instance store volumes for rapid I/O and EBS snapshots for daily backups of essential data.

Answer: C

Question 2

A Data Engineering Team at an e-commerce company needs to regularly purge customer data from an Amazon RDS instance to comply with privacy regulations. The data to be deleted is determined based on a set of complex business rules which involve query results from multiple tables.

Additionally, the team must ensure that the deletion process is logged for audit purposes and that the database's performance is not significantly impacted during peak business hours.

Which approach should the team take to meet these requirements most effectively?

Options :

A : Implement an AWS Lambda function that is triggered daily during off-peak hours. The Lambda function executes the deletion commands based on the business rules and writes the deletion logs to Amazon CloudWatch. Use Amazon RDS Read Replicas to ensure the main database-s performance is not affected.

B : Schedule an AWS Data Pipeline job to extract the relevant records to an Amazon S3 bucket, where another job will delete the records. Use Amazon CloudTrail to log all operations. Amazon RDS-s performance is managed by AWS Data Pipeline-s resource allocation.

C : Create an Amazon RDS Event Subscription to notify an AWS Lambda function when it-s time to delete data. The Lambda function processes the deletion in batches to minimize the performance impact and logs actions to a dedicated Amazon DynamoDB table for auditing.

D : Develop a custom script that runs on an Amazon EC2 instance to perform the deletions during off-peak hours. The script logs all operations in Amazon S3 and uses RDS-s native functionality to manage database load during execution.

Answer: A

Question 3

A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB. Which solution will meet these requirements MOST cost-effectively?

Options :

A :

Write a custom Python application. Host the application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.

B : Write a PySpark ETL script. Host the script on an Amazon EMR cluster.

C : Write an AWS Glue PySpark job. Use Apache Spark to transform the data.

D : Write an AWS Glue Python shell job. Use pandas to transform the data.

Answer: D

Question 4

A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3

bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularlyproliferated

to the S3 bucket. The data includes files that are in multiple formats. The data engineer needs to automate the

transfer process and must schedule the process to run periodically.

Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient

way?

Options :

A : AWS DataSync

B : AWS Glue

C : AWS Direct Connect

D : Amazon S3 Transfer Acceleration

Answer: A

Question 5

A Cloud Data Engineering Team needs to migrate their on-premises Oracle database to Amazon Aurora PostgreSQL. They want to ensure that the schema and the code in the Oracle database, including stored procedures and functions, are compatible with Aurora PostgreSQL. They plan to use AWS tools for this conversion and migration.

What would be the easiest way, using AWS services, to convert the Oracle database schema to be compatible with Aurora PostgreSQL and to perform the subsequent data migration?

Options :

A : Implement AWS Glue ETL for the schema conversion, using its data transformation capabilities, followed by AWS DMS for migrating the data.

B : First, use AWS SCT to convert the Oracle database schema to an Aurora PostgreSQL-compatible format, then employ AWS DMS for the actual data migration.

C : Start with Amazon RDS for Oracle-s built-in schema conversion tool and then use AWS DMS for the data migration to Aurora PostgreSQL.

D : Utilize AWS DMS, including its Schema Conversion feature, for an integrated solution that handles both the conversion of the Oracle database schema and the migration to Amazon Aurora PostgreSQL

Answer: D