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Amazon Web Services DAS-C01 Dumps Questions Answers

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AWS Certified Data Analytics - Specialty

Last Update Sep 20, 2022
Total Questions : 157

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AWS Certified Data Analytics - Specialty Questions and Answers

Questions 1

A company has developed an Apache Hive script to batch process data stared in Amazon S3. The script needs to run once every day and store the output in Amazon S3. The company tested the script, and it completes within 30 minutes on a small local three-node cluster.

Which solution is the MOST cost-effective for scheduling and executing the script?

Options:

A.

Create an AWS Lambda function to spin up an Amazon EMR cluster with a Hive execution step. Set KeepJobFlowAliveWhenNoSteps to false and disable the termination protection flag. Use Amazon CloudWatch Events to schedule the Lambda function to run daily.

B.

Use the AWS Management Console to spin up an Amazon EMR cluster with Python Hue. Hive, and Apache Oozie. Set the termination protection flag to true and use Spot Instances for the core nodes of the cluster. Configure an Oozie workflow in the cluster to invoke the Hive script daily.

C.

Create an AWS Glue job with the Hive script to perform the batch operation. Configure the job to run once a day using a time-based schedule.

D.

Use AWS Lambda layers and load the Hive runtime to AWS Lambda and copy the Hive script. Schedule the Lambda function to run daily by creating a workflow using AWS Step Functions.

Questions 2

An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog. Data analysts want to join this data with call center data stored in Amazon Redshift as part of a dally batch process. The Amazon Redshift cluster is already under a heavy load. The solution must be managed, serverless, well-functioning, and minimize the load on the existing Amazon Redshift cluster. The solution should also require minimal effort and development activity.

Which solution meets these requirements?

Options:

A.

Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambda function. Perform the join with AWS Glue ETL scripts.

B.

Export the call center data from Amazon Redshift using a Python shell in AWS Glue. Perform the join with AWS Glue ETL scripts.

C.

Create an external table using Amazon Redshift Spectrum for the call center data and perform the join with Amazon Redshift.

D.

Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop. Perform the join with Apache Hive.

Questions 3

A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations.

  • Station A, which has 10 sensors
  • Station B, which has five sensors

These weather stations were placed by onsite subject-matter experts.

Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams.

Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created. Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B. Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.

How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?

Options:

A.

Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.

B.

Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream.

C.

Modify the partition key to use the sensor ID instead of the station name.

D.

Reduce the number of sensors in Station A from 10 to 5 sensors.