Question 1

A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has

several use cases. The company must implement permission controls to separate query processes and access to

query history among users, teams, and applications that are in the same AWS account.

Which solution will meet these requirements?

Options :

A :

Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.

B :

Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an 1AM policy that uses the tags to apply appropriate permissions to the workgroup.

C :

Create an JAM role for each use case. Assign appropriate permissions to the role for each use case. Associate the role with Athena.

D :

Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.

Answer: B

Question 2

A Data Engineering team at a healthcare analytics company needs to manage complex data pipelines processing sensitive patient data. The pipelines integrate data from hospital records, insurance claims, and clinical trials. As regulatory requirements strictly enforce the accuracy and traceability of data, the team is required to implement a solution that ensures the data's origin, transformations, and usage are transparent and auditable.

Which approach would best ensure the accuracy and trustworthiness of data by enabling comprehensive data lineage?

Options :

A : Implement AWS Glue ETL jobs with metadata tables in AWS Glue Data Catalog, capturing and storing lineage information for all data transformations and movements.

B : Deploy AWS Data Pipeline to orchestrate the data workflows and enable logging in Amazon CloudWatch to monitor data movement and transformation tasks.

C : Use Amazon S3 event notifications to trigger AWS Lambda functions for each data operation, logging every data movement and transformation in Amazon DynamoDB.

D : Utilize Amazon Athena with partition projection to query data directly from Amazon S3, enabling SQL query logging for tracing data access and usage.

Answer: A

Question 3

A company uses an on-premises Microsoft SQL Server database to store financial transaction data. The

company migrates the transaction data from the on-premises database to AWS at the end of each month. The

company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL

Server database has increased recently.

The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal

downtown for the applications that access the database.

Which AWS service should the company use to meet these requirements?

Options :

A : AWS Lambda

B : AWS Database Migration Service (AWS DMS)

C : AWS Direct Connect

D : AWS DataSync

Answer: B

Question 4

A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class. The data supports analytics workloads that have unpredictable and variable data access patterns. The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs. Which solution will meet these requirements with the LEAST operational overhead?

Options :

A :

Use S3 Storage Lens standard metrics to determine when to move objects to more cost-optimized storage classes. Create S3 Lifecycle policies for the S3 buckets to move objects to cost-optimized storage classes. Continue to refine the S3 Lifecycle policies in the future to optimize storage costs.

B :

Use S3 Storage Lens activity metrics to identify S3 buckets that the company accesses infrequently Configure S3 Lifecycle rules to move objects from S3 Standard to the S3 Standard-Infrequent Access (S3 Standard-IA) and S3 Glacier storage classes based on the age of the data.

C : Use S3 Intelligent-Tiering. Activate the Deep Archive Access tier.

D : Use S3 Intelligent-Tiering. Use the default access tier.

Answer: D

Question 5

An e-commerce company uses Amazon RDS for PostgreSQL to manage their product inventory. The database contains a table named products with columns id, name, price, and quantity_in_stock. The company needs to write an SQL query to find all products that have a price greater than $50 and a quantity in stock less than 100.

Which SQL query should the company use to retrieve the correct data from the products table in their PostgreSQL database?

Options :

A : FIND * IN products WHERE price IS GREATER THAN 50 AND quantity_in_stock IS LESS THAN 100;

B : SELECT * FROM products WHERE price > 50 AND quantity_in_stock < 100;

C : SELECT * FROM products WHERE price > $50 AND quantity_in_stock < 100;

D : GET * FROM products WHEN price > 50 & quantity_in_stock < 100;

Answer: B

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