Question 1

A marketing company has an application that stores event data in an Amazon RDS database. The company is replicating this data to Amazon Redshift for reporting and business intelligence (BI) purposes. New event data is continuously generated and ingested into the RDS database throughout the day and captured by a change data capture (CDC) replication task in AWS Database Migration Service (AWS DMS). The company requires that the new data be replicated to Amazon Redshift in near-real time. Which solution meets these requirements?

Options :

A :

Use Amazon Kinesis Data Streams as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Streams and perform an upsert into the Redshift cluster.

B :

Use Amazon S3 as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.

C :

Use Amazon DynamoDB as the destination of the CDC replication task in AWS DMS. Use the COPY command to load data into the Redshift cluster.

D :

Use Amazon Kinesis Data Firehose as the destination of the CDC replication task in AWS DMS. Use an AWS Glue streaming job to read changed records from Kinesis Data Firehose and perform an upsert into the Redshift cluster.

Answer: A

Question 2

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 3

A reseller that has thousands of AWS accounts receives AWS Cost and Usage Reports in an Amazon S3 bucket The reports are delivered to the S3 bucket in the following format
< examp/e-reporT-prefix >/< examp/e-report-rtame >/yyyymmdd-yyyymmdd/<examp/e-report-name> parquet
An AWS Glue crawler crawls the S3 bucket and populates an AWS Glue Data Catalog with a table Business analysts use Amazon Athena to query the table and create monthly summary reports for the AWS accounts The business analysts are experiencing slow queries because of the accumulation of reports from the last 5 years The business analysts want the operations team to make changes to improve query performance
Which action should the operations team take to meet these requirements?

Options :

A : Change the file format to csv.zip.

B : Partition the data by date and account ID

C : Partition the data by month and account ID

D : Partition the data by account ID, year, and month

Answer: B

Question 4

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

Question 5

A manufacturing company has many loT devices in different facilities across the world The company is using Amazon Kinesis Data Streams to collect the data from the devices
The company's operations team has started to observe many WnteThroughputExceeded exceptions The operations team determines that the reason is the number of records that are being written to certain shards The data contains device ID capture date measurement type, measurement value and facility ID The facility ID is used as the partition key
Which action will resolve this issue?

Options :

A : Change the partition key from facility ID to a randomly generated key

B : Increase the number of shards

C : Archive the data on the producers' side

D : Change the partition key from facility ID to capture date

Answer: B

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