Question 1

A data analyst is designing an Amazon QuickSight dashboard using centralized sales data that resides in Amazon Redshift. The dashboard must be restricted so that a salesperson in Sydney. Australia, can see only the Australia view and that a salesperson in New York can see only United States (US) data.

What should the data analyst do to ensure the appropriate data security is in place?

Options :

A : Place the data sources for Australia and the US into separate SPICE capacity pools.

B : Set up an Amazon Redshift VPC security group for Australia and the US.

C : Deploy QuickSight Enterprise edition to implement row-level security (RLS) to the sales table.

D : Deploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.

Answer: D

Question 2

An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadat
a. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync
Which solution will simplify permissions management with minimal development effort?

Options :

A : Set AWS Identity and Access Management (1AM) permissions tor AWS Glue

B : Use AWS Lake Formation permissions

C : Manage AWS Glue and S3 permissions by using bucket policies

D : Use Amazon Cognito user pools.

Answer: B

Question 3

A financial company uses Apache Hive on Amazon EMR for ad-hoc queries. Users are complaining of sluggish performance.
A data analyst notes the following:
Approximately 90% of queries are submitted 1 hour after the market opens.
Hadoop Distributed File System (HDFS) utilization never exceeds 10%.
Which solution would help address the performance issues?

Options :

A : Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch CapacityRemainingGB metric.

B : Create instance fleet configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric. Create an automatic scaling policy to scale in the instance fleet based on the CloudWatch YARNMemoryAvailablePercentage metric.

C : Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch CapacityRemainingGB metric. Create an
automatic scaling policy to scale in the instance groups based on the CloudWatch CapacityRemainingGB metric.

D : Create instance group configurations for core and task nodes. Create an automatic scaling policy to scale out the instance groups based on the Amazon CloudWatch YARNMemoryAvailablePercentage metric. Create an automatic scaling policy to scale in the instance groups based on the CloudWatch YARNMemoryAvailablePercentage metric.

Answer: D

Question 4

A company has a business unit uploading .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job writes processed data from the created tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creating the Amazon Redshift table appropriately. When the AWS Glue job is rerun for any reason in a day, duplicate records are introduced into the Amazon Redshift table.
Which solution will update the Redshift table without duplicates when jobs are rerun?

Options :

A : Modify the AWS Glue job to copy the rows into a staging table. Add SQL commands to replace the existing rows in the main table as postactions in the DynamicFrameWriter class.

B : Load the previously inserted data into a MySQL database in the AWS Glue job. Perform an upsert operation in MySQL, and copy the results to the Amazon Redshift table.

C : Use Apache Spark-s DataFrame dropDuplicates() API to eliminate duplicates and then write the data to Amazon Redshift.

D : Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.

Answer: A

Question 5

A company has a mobile app that has millions of users. The company wants to enhance the mobile app by including interactive data visualizations that show user trends. The data for visualization is stored in a large data lake with 50 million rows. Data that is used in the visualization should be no more than two hours old. Which solution will meet these requirements with the LEAST operational overhead?

Options :

A :

Run an hourly batch process that renders user-specific data visualizations as static images that are stored in Amazon S3.

B :

Precompute aggregated data hourly. Store the data in Amazon DynamoDB. Render the data by using the D3.js JavaScript library.

C :

Embed an Amazon QuickSight Enterprise edition dashboard into the mobile app by using the QuickSight Embedding SDK. Refresh data in SPICE hourly.

D :

Run Amazon Athena queries behind an Amazon API Gateway API. Render the data by using the D3.js JavaScript library.

Answer: A