Question 1

A company ingests a large set of sensor data in nested JSON format from different sources and stores it in an Amazon S3 bucket. The sensor data must be joined with performance data currently stored in an Amazon Redshift cluster. A business analyst with basic SQL skills must build dashboards and analyze this data in Amazon QuickSight. A data engineer needs to build a solution to prepare the data for use by the business analyst. The data engineer does not know the structure of the JSON file. The company requires a solution with the least possible implementation effort. Which combination of steps will create a solution that meets these requirements? (Select THREE.)

Options :

A : Use an AWS Glue ETL job to convert the data into Apache Parquet format and write to Amazon S3.

B : Use an AWS Glue crawler to catalog the data.

C :

Use an AWS Glue ETL job with the ApplyMapping class to un-nest the data and write to Amazon Redshift tables.

D :

Use an AWS Glue ETL job with the Regionalize class to un-nest the data and write to Amazon Redshift tables.

E :

Use QuickSight to create an Amazon Athena data source to read the Apache Parquet files in Amazon S3.

F : Use QuickSight to create an Amazon Redshift data source to read the native Amazon Redshift tables.

Answer: B,D,F

Question 2

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 3

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 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 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

Free DAS-C01 Mock Exam – Practice Online Confidently

Buying Options: