Amazon Amazon-DEA-C01 Quiz 1 Topic 2 Questions 1-5

Question: 1

A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks.

The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster.

The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.

Which solution will meet these requirements?

ASet up the sales team Bl cluster as a consumer of the ETL cluster by using Redshift data sharing.

BCreate materialized views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.

CCreate database views based on the sales team's requirements. Grant the sales team direct access to the ETL cluster.

DUnload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

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Question: 2

A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.

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

AUse Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.

BUse Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.

CUse Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.

DUse Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.

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Question: 3

A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_dat

a. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.

The data engineer's original query is as follows:

SELECT product_name, sum(sales_amount)

FROM sales_data

WHERE year = 2023

GROUP BY product_name

How should the data engineer modify the Athena query to meet these requirements?

AReplace sum(sales amount) with count(*J for the aggregation.

BChange WHERE year = 2023 to WHERE extractlyear FROM sales data) = 2023.

CAdd HAVING sumfsales amount) > 0 after the GROUP BY clause.

DRemove the GROUP BY clause

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Question: 4

A data engineer needs to use Amazon Neptune to develop graph applications.

Which programming languages should the engineer use to develop the graph applications? (Select TWO.)

AGremlin

BSQL

CANSI SQL

DSPARQL

ESpark SQL

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Question: 5

A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets and Amazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.

Which solution will meet these requirements with the LEAST effort?

AUse an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format.

BUse an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.

CUse Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.

DUse scripts to scan data elements and to assign data classifications based on the format of the data.

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Amazon Amazon-DEA-C01 Quiz:1 Topic:2 Questions:1-5