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Amazon Web Services DAS-C01 Exam Sample Questions


Question # 1

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 # 2

A financial services company needs to aggregate daily stock trade data from the exchanges into a data store.
The company requires that data be streamed directly into the data store, but also occasionally allows data to be
modified using SQL. The solution should integrate complex, analytic queries running with minimal latency.
The solution must provide a business intelligence dashboard that enables viewing of the top contributors to
anomalies in stock prices.
Which solution meets the company’s requirements?

A.

Use Amazon Kinesis Data Firehose to stream data to Amazon S3. Use Amazon Athena as a data source
for Amazon QuickSight to create a business intelligence dashboard

B.

Use Amazon Kinesis Data Streams to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard

C.

Use Amazon Kinesis Data Firehose to stream data to Amazon Redshift. Use Amazon Redshift as a data source for Amazon QuickSight to create a business intelligence dashboard.

D.

Use Amazon Kinesis Data Streams to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.



D.

Use Amazon Kinesis Data Streams to stream data to Amazon S3. Use Amazon Athena as a data source for Amazon QuickSight to create a business intelligence dashboard.





Question # 3

A software company hosts an application on AWS, and new features are released weekly. As part of the
application testing process, a solution must be developed that analyzes logs from each Amazon EC2 instance
to ensure that the application is working as expected after each deployment. The collection and analysis
solution should be highly available with the ability to display new information with minimal delays.
Which method should the company use to collect and analyze the logs?

A.

Enable detailed monitoring on Amazon EC2, use Amazon CloudWatch agent to store logs in Amazon
S3, and use Amazon Athena for fast, interactive log analytics.

B.

Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to
Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and visualize using
Amazon QuickSight.

C.

Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to
Kinesis Data Firehose to further push the data to Amazon Elasticsearch Service and Kibana.

D.

Use Amazon CloudWatch subscriptions to get access to a real-time feed of logs and have the logs
delivered to Amazon Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and Kibana.



D.

Use Amazon CloudWatch subscriptions to get access to a real-time feed of logs and have the logs
delivered to Amazon Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and Kibana.





Question # 4

A media content company has a streaming playback application. The company wants to collect and analyze
the data to provide near-real-time feedback on playback issues. The company needs to consume this data and
return results within 30 seconds according to the service-level agreement (SLA). The company needs the
consumer to identify playback issues, such as quality during a specified timeframe. The data will be emitted as
JSON and may change schemas over time.
Which solution will allow the company to collect data for processing while meeting these requirements?

A.

Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure an S3 event
trigger an AWS Lambda function to process the data. The Lambda function will consume the data and
process it to identify potential playback issues. Persist the raw data to Amazon S3.

B.

Send the data to Amazon Managed Streaming for Kafka and configure an Amazon Kinesis Analytics for Java application as the consumer. The application will consume the data and process it to identify
potential playback issues. Persist the raw data to Amazon DynamoDB.

C.

Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to
trigger an event for AWS Lambda to process. The Lambda function will consume the data and process it to identify potential playback issues. Persist the raw data to Amazon DynamoDB.

D.

Send the data to Amazon Kinesis Data Streams and configure an Amazon Kinesis Analytics for Java
application as the consumer. The application will consume the data and process it to identify potential
playback issues. Persist the raw data to Amazon S3.



B.

Send the data to Amazon Managed Streaming for Kafka and configure an Amazon Kinesis Analytics for Java application as the consumer. The application will consume the data and process it to identify
potential playback issues. Persist the raw data to Amazon DynamoDB.





Question # 5

A regional energy company collects voltage data from sensors attached to buildings. To address any known
dangerous conditions, the company wants to be alerted when a sequence of two voltage drops is detected
within 10 minutes of a voltage spike at the same building. It is important to ensure that all messages are
delivered as quickly as possible. The system must be fully managed and highly available. The company also
needs a solution that will automatically scale up as it covers additional cites with this monitoring feature. The
alerting system is subscribed to an Amazon SNS topic for remediation.
Which solution meets these requirements?

A.

Create an Amazon Managed Streaming for Kafka cluster to ingest the data, and use an Apache Spark
Streaming with Apache Kafka consumer API in an automatically scaled Amazon EMR cluster to
process the incoming data. Use the Spark Streaming application to detect the known event sequence and send the SNS message.

B.

Create a REST-based web service using Amazon API Gateway in front of an AWS Lambda function.
Create an Amazon RDS for PostgreSQL database with sufficient Provisioned IOPS (PIOPS). In the
Lambda function, store incoming events in the RDS database and query the latest data to detect the
known event sequence and send the SNS message.

C.

Create an Amazon Kinesis Data Firehose delivery stream to capture the incoming sensor data. Use an
AWS Lambda transformation function to detect the known event sequence and send the SNS message

D.

Create an Amazon Kinesis data stream to capture the incoming sensor data and create another stream for
alert messages. Set up AWS Application Auto Scaling on both. Create a Kinesis Data Analytics for Java
application to detect the known event sequence, and add a message to the message stream. Configure an
AWS Lambda function to poll the message stream and publish to the SNS topic.



D.

Create an Amazon Kinesis data stream to capture the incoming sensor data and create another stream for
alert messages. Set up AWS Application Auto Scaling on both. Create a Kinesis Data Analytics for Java
application to detect the known event sequence, and add a message to the message stream. Configure an
AWS Lambda function to poll the message stream and publish to the SNS topic.




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