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Amazon Web Services DAS-C01 Exam Sample Questions


Question # 1

A retail company’s data analytics team recently created multiple product sales analysis dashboards for the
average selling price per product using Amazon QuickSight. The dashboards were created from .csv files
uploaded to Amazon S3. The team is now planning to share the dashboards with the respective external
product owners by creating individual users in Amazon QuickSight. For compliance and governance reasons,
restricting access is a key requirement. The product owners should view only their respective product analysis
in the dashboard reports.
Which approach should the data analytics team take to allow product owners to view only their products in the
dashboard?

A.

Separate the data by product and use S3 bucket policies for authorization.

B.

Separate the data by product and use IAM policies for authorization.

C.

Create a manifest file with row-level security.

D.

Create dataset rules with row-level security.



B.

Separate the data by product and use IAM policies for authorization.






Question # 2

An insurance company has raw data in JSON format that is sent without a predefined schedule through an
Amazon Kinesis Data Firehose delivery stream to an Amazon S3 bucket. An AWS Glue crawler is scheduled
to run every 8 hours to update the schema in the data catalog of the tables stored in the S3 bucket. Data
analysts analyze the data using Apache Spark SQL on Amazon EMR set up with AWS Glue Data Catalog as
the metastore. Data analysts say that, occasionally, the data they receive is stale. A data engineer needs to
provide access to the most up-to-date data.
Which solution meets these requirements?

A.

Create an external schema based on the AWS Glue Data Catalog on the existing Amazon Redshift
cluster to query new data in Amazon S3 with Amazon Redshift Spectrum.

B.

Use Amazon CloudWatch Events with the rate (1 hour) expression to execute the AWS Glue crawler
every hour.

C.

Using the AWS CLI, modify the execution schedule of the AWS Glue crawler from 8 hours to 1 minute.

D.

Run the AWS Glue crawler from an AWS Lambda function triggered by an S3:ObjectCreated:* event
notification on the S3 bucket.



A.

Create an external schema based on the AWS Glue Data Catalog on the existing Amazon Redshift
cluster to query new data in Amazon S3 with Amazon Redshift Spectrum.






Question # 3

A banking company is currently using an Amazon Redshift cluster with dense storage (DS) nodes to store
sensitive data. An audit found that the cluster is unencrypted. Compliance requirements state that a database
with sensitive data must be encrypted through a hardware security module (HSM) with automated key
rotation.
Which combination of steps is required to achieve compliance? (Choose two.)

A.

Set up a trusted connection with HSM using a client and server certificate with automatic key rotation.

B.

Modify the cluster with an HSM encryption option and automatic key rotation.

C.

Create a new HSM-encrypted Amazon Redshift cluster and migrate the data to the new cluster.

D.

Enable HSM with key rotation through the AWS CLI.

E.

Enable Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) encryption in the HSM.



B.

Modify the cluster with an HSM encryption option and automatic key rotation.


D.

Enable HSM with key rotation through the AWS CLI.






Question # 4

A marketing company wants to improve its reporting and business intelligence capabilities. During the
planning phase, the company interviewed the relevant stakeholders, and discovered that:
The operations team reports are run hourly for the current month’s data.
The sales team wants to use multiple Amazon QuickSight dashboards to show a rolling view of the last
30 days based on several categories. The sales team also wants to view the data as soon as it reaches the
reporting backend.
The finance team’s reports are run daily for last month’s data and once a month for the last 24 months of
data.
Currently, there is 400 TB of data in the system with an expected additional 100 TB added every month. The
company is looking for a solution that is as cost-effective as possible.
Which solution meets the company’s requirements?

A.

Store the last 24 months of data in Amazon Redshift. Configure Amazon QuickSight with Amazon
Redshift as the data source.

B.

Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Set up an external schema and table for Amazon Redshift Spectrum. Configure Amazon QuickSight with Amazon Redshift as the data source.

C.

Store the last 24 months of data in Amazon S3 and query it using Amazon Redshift Spectrum.
Configure Amazon QuickSight with Amazon Redshift Spectrum as the data source.

D.

Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Use a
long- running Amazon EMR with Apache Spark cluster to query the data as needed. Configure Amazon QuickSight with Amazon EMR as the data source.



B.

Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Set up an external schema and table for Amazon Redshift Spectrum. Configure Amazon QuickSight with Amazon Redshift as the data source.






Question # 5

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?

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.



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





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