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Amazon Web Services DAS-C01 Exam Sample Questions


Question # 1

A large ride-sharing company has thousands of drivers globally serving millions of unique customers every
day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema
includes the following tables.
A trips fact table for information on completed rides.
A drivers dimension table for driver profiles.
A customers fact table holding customer profile information.
The company analyzes trip details by date and destination to examine profitability by region. The drivers data
rarely changes. The customers data frequently changes.
What table design provides optimal query performance?

A.

Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the
drivers and customers tables.

B.

Use DISTSTYLE EVEN for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table.
Use DISTSTYLE EVEN for the customers table.

C.

Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the
drivers table. Use DISTSTYLE EVEN for the customers table.

D.

Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact
tables.



A.

Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the
drivers and customers tables.





Question # 2

A company wants to improve user satisfaction for its smart home system by adding more features to its
recommendation engine. Each sensor asynchronously pushes its nested JSON data into Amazon Kinesis Data
Streams using the Kinesis Producer Library (KPL) in Java. Statistics from a set of failed sensors showed that,
when a sensor is malfunctioning, its recorded data is not always sent to the cloud.
The company needs a solution that offers near-real-time analytics on the data from the most updated sensors.
Which solution enables the company to meet these requirements?

A.

Set the RecordMaxBufferedTime property of the KPL to "-1" to disable the buffering on the sensor side. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL script. Push the enriched data to a fleet of Kinesis data streams and enable the data transformation feature to flatten the JSON file. Instantiate a dense storage Amazon Redshift cluster and use it as the destination for the Kinesis Data Firehose delivery stream.

B.

Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with
the AWS SDK for Java. Use Kinesis Data Analytics to enrich the data based on a company-developed
anomaly detection SQL script. Direct the output of KDA application to a Kinesis Data Firehose delivery
stream, enable the data transformation feature to flatten the JSON file, and set the Kinesis Data Firehose destination to an Amazon Elasticsearch Service cluster.

C.

Set the RecordMaxBufferedTime property of the KPL to "0" to disable the buffering on the sensor side.

D.

Connect for each stream a dedicated Kinesis Data Firehose delivery stream and enable the data
transformation feature to flatten the JSON file before sending it to an Amazon S3 bucket. Load the S3
data into an Amazon Redshift cluster.

E.

Update the sensors code to use the PutRecord/PutRecords call from the Kinesis Data Streams API with the AWS SDK for Java. Use AWS Glue to fetch and process data from the stream using the Kinesis Client Library (KCL). Instantiate an Amazon Elasticsearch Service cluster and use AWS Lambda to directly push data into it.



A.

Set the RecordMaxBufferedTime property of the KPL to "-1" to disable the buffering on the sensor side. Use Kinesis Data Analytics to enrich the data based on a company-developed anomaly detection SQL script. Push the enriched data to a fleet of Kinesis data streams and enable the data transformation feature to flatten the JSON file. Instantiate a dense storage Amazon Redshift cluster and use it as the destination for the Kinesis Data Firehose delivery stream.





Question # 3

A manufacturing company has been collecting IoT sensor data from devices on its factory floor for a year and is storing the data in Amazon Redshift for daily analysis. A data analyst has determined that, at an expected ingestion rate of about 2 TB per day, the cluster will be undersized in less than 4 months. A long-term solution is needed. The data analyst has indicated that most queries only reference the most recent 13 months of data,
yet there are also quarterly reports that need to query all the data generated from the past 7 years. The chief technology officer (CTO) is concerned about the costs, administrative effort, and performance of a long-term solution. Which solution should the data analyst use to meet these requirements?

A.

Create a daily job in AWS Glue to UNLOAD records older than 13 months to Amazon S3 and delete
those records from Amazon Redshift. Create an external table in Amazon Redshift to point to the S3 location. Use Amazon Redshift Spectrum to join to data that is older than 13 months.

B.

Take a snapshot of the Amazon Redshift cluster. Restore the cluster to a new cluster using dense storage nodes with additional storage capacity.

C.

Execute a CREATE TABLE AS SELECT (CTAS) statement to move records that are older than 13
months to quarterly partitioned data in Amazon Redshift Spectrum backed by Amazon S3.

D.

Unload all the tables in Amazon Redshift to an Amazon S3 bucket using S3 Intelligent-Tiering. Use
AWS Glue to crawl the S3 bucket location to create external tables in an AWS Glue Data Catalog.
Create an Amazon EMR cluster using Auto Scaling for any daily analytics needs, and use Amazon
Athena for the quarterly reports, with both using the same AWS Glue Data Catalog.



B.

Take a snapshot of the Amazon Redshift cluster. Restore the cluster to a new cluster using dense storage nodes with additional storage capacity.





Question # 4

A company is planning to create a data lake in Amazon S3. The company wants to create tiered storage based on access patterns and cost objectives. The solution must include support for JDBC connections from legacy clients, metadata management that allows federation for access control, and batch-based ETL using PySpark and Scala Operational management should be limited.
Which combination of components can meet these requirements? (Choose three.)

A.

AWS Glue Data Catalog for metadata management

B.

Amazon EMR with Apache Spark for ETL

C.

AWS Glue for Scala-based ETL

D.

Amazon EMR with Apache Hive for JDBC clients

E.

Amazon Athena for querying data in Amazon S3 using JDBC drivers



B.

Amazon EMR with Apache Spark for ETL


E.

Amazon Athena for querying data in Amazon S3 using JDBC drivers






Question # 5

A global company has different sub-organizations, and each sub-organization sells its products and services in
various countries. The company's senior leadership wants to quickly identify which sub-organization is the
strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format.
Which approach can provide the visuals that senior leadership requested with the least amount of effort?

A.

Use Amazon QuickSight with Amazon Athena as the data source. Use heat maps as the visual type.

B.

Use Amazon QuickSight with Amazon S3 as the data source. Use heat maps as the visual type.

C.

Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.

D.

Use Amazon QuickSight with Amazon S3 as the data source. Use pivot tables as the visual type.



C.

Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.




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