Question 1

A company plans to migrate its on-premises Oracle database to Amazon Aurora PostgreSQL. They need to ensure a seamless migration of both the database schema and the data with minimal downtime. The company also requires a tool to convert the source database schema to be compatible with Amazon Aurora PostgreSQL.

Which combination of AWS services should the company use to migrate the database schema and data?

Options :

A : Utilize Amazon RDS for data migration and AWS Lambda for schema conversion.

B : Use AWS Database Migration Service (DMS) for data migration and AWS Schema Conversion Tool (SCT) for schema conversion.

C : Implement AWS Glue for both schema conversion and data migration.

D : Apply Amazon S3 transfer acceleration for data migration and Amazon Athena for schema conversion.

Answer: B

Question 2

A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company

has an ecommerce application that generates a dataset that contains personally identifiable information (PII).

The company has an internal analytics application that does not require access to the PII.

To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to

implement a solution that with redact PII dynamically, based on the needs of each application that accesses the

dataset.

Which solution will meet the requirements with the LEAST operational overhead?

Options :

A :

Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

B :

Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.

C :

Use AWS Glue to transform the data for each application. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

D :

Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.

Answer: B

Question 3

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 4

A data engineer at a financial analysis firm is responsible for enriching their AWS-based data lake with external financial datasets. The firm requires regular updates of stock market data and economic indicators from various data providers, which should be integrated seamlessly into their Amazon S3-based data lake for further processing and analysis.

Which AWS service should the data engineer use to automate the ingestion of these external financial datasets into their data lake with minimal effort?

Options :

A : Implement an AWS Glue crawler to extract data from external data sources and load it into the data lake.

B : Configure Amazon AppFlow to create flows between the data providers and S3, syncing the financial datasets to the data lake.

C : Set up a recurring AWS Lambda function to retrieve and upload data from third-party APIs to their S3 data lake.

D : Use AWS Data Exchange to subscribe to relevant financial datasets and directly import them into their S3 data lake.

Answer: D

Question 5

A digital media company processes large volumes of image and video files daily. These files are stored in an Amazon S3 bucket. The company needs to analyze metadata extracted from these files (such as file size, type, creation date) and store this information for quick retrieval and analysis. The solution should automatically handle new files as they are uploaded and be scalable to handle increasing data volumes.

Which AWS service should the company implement to extract, store, and manage the metadata efficiently?

Options :

A : Use AWS Glue to run ETL jobs on new files in S3, extract metadata, and store it in Amazon RDS for querying.

B : Configure Amazon S3 Event Notifications to trigger AWS Lambda functions for metadata extraction and storage in Amazon DynamoDB.

C : Utilize Amazon Athena to query S3 metadata directly and store the query results in Amazon ElastiCache for quick access.

D : Implement Amazon Kinesis Data Firehose to capture file metadata and load it into Amazon Redshift for analysis.

Answer: B