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Amazon Web Services DAS-C01 Exam Dumps

Exam Name: AWS Certified Data Analytics - Specialty
Certification Name: AWS Certified Data Analytics

Amazon Web Services DAS-C01 exam dumps are created by industry top professionals and after that its also verified by expert team. We are providing you updated AWS Certified Data Analytics - Specialty exam questions answers. We keep updating our AWS Certified Data Analytics practice test according to real exam. So prepare from our latest questions answers and pass your exam.

  • Total Questions: 207
  • Last Updation Date: 16-Apr-2024

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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.)

 

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

 

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

 

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

 

Enable HSM with key rotation through the AWS CLI.

 

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


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


Enable HSM with key rotation through the AWS CLI.





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?

 

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.

 

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.

 

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

 

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.

 

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.


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.





A company that produces network devices has millions of users. Data is collected from the devices on an
hourly basis and stored in an Amazon S3 data lake.
The company runs analyses on the last 24 hours of data flow logs for abnormality detection and to
troubleshoot and resolve user issues. The company also analyzes historical logs dating back 2 years to discover
patterns and look for improvement opportunities.
The data flow logs contain many metrics, such as date, timestamp, source IP, and target IP. There are about 10
billion events every day.
How should this data be stored for optimal performance?

 

In Apache ORC partitioned by date and sorted by source IP

 

In compressed .csv partitioned by date and sorted by source IP

 

In Apache Parquet partitioned by source IP and sorted by date

 

In compressed nested JSON partitioned by source IP and sorted by date


In compressed nested JSON partitioned by source IP and sorted by date



Amazon Web Services DAS-C01 dumps

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