Question 1

The Cloud Data Engineering team at an e-commerce company is tasked with analyzing historical sales data stored in CSV files in Amazon S3. They require a solution that allows them to directly query this data for their daily sales report, which will include product names, quantities sold, and total sales amounts. The solution should enable them to perform this task without the overhead of cluster management or data loading processes.

Which two steps are essential to set up this process (Select TWO)

Options :

A : Create an Amazon Redshift Spectrum external schema for the CSV files and run the query from an Amazon Redshift cluster.

B : Implement an AWS Lambda function to convert the CSV files into JSON format, as Athena cannot query CSV files directly.

C : Use AWS Data Pipeline to transfer the CSV files into an Amazon RDS instance and then query the data with Athena.

D : Write an Amazon Athena SQL query to select the necessary fields, aggregate the quantity and sales amount, and group the results by product name.

E : Use the AWS Glue Data Catalog to catalog the CSV files and define a schema that maps to the fields in the sales data.

Answer: D,E

Question 2

A company needs to set up a data catalog and metadata management for data sources that run in the AWS

Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of

data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data

stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3.

The company needs a solution that will update the data catalog on a regular basis. The solution also must

detect changes to the source metadata.

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

Options :

A :

Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.

B :

Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.

C :

Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.

D : Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon

RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is

in Amazon S3 to infer the schema and to automatically update the Data Catalog.

Answer: B

Question 3

A company saves customer data to an Amazon S3 bucket. The company uses server-side encryption with

AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes personally identifiable information

(PII) such as social security numbers and account details.

Data that is tagged as PII must be masked before the company uses customer data for analysis. Some users

must have secure access to the PII data during the preprocessing phase. The company needs a lowmaintenance solution to mask and secure the PII data throughout the entire engineering pipeline.

Which combination of solutions will meet these requirements? (Select TWO.)

Options :

A :

Use AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask the PII data before analysis.

B :

Use Amazon GuardDuty to monitor access patterns for the PII data that is used in the engineering pipeline.

C : Configure an Amazon Made discovery job for the S3 bucket.

D :

Use AWS Identity and Access Management (IAM) to manage permissions and to control access to the PII data

E : Write custom scripts in an application to mask the PII data and to control access.

Answer: A,D

Question 4

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 5

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

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