DAS-C01 Questions Bank

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Question 32

A company has a mobile app that has millions of users. The company wants to enhance the mobile app by including interactive data visualizations that show user trends.

The data for visualization is stored in a large data lake with 50 million rows. Data that is used in the visualization should be no more than two hours old.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

Run an hourly batch process that renders user-specific data visualizations as static images that are stored in Amazon S3.

Precompute aggregated data hourly. Store the data in Amazon DynamoDB. Render the data by using the D3.js JavaScript library.

Embed an Amazon QuickSight Enterprise edition dashboard into the mobile app by using the QuickSight Embedding SDK. Refresh data in SPICE hourly.

Run Amazon Athena queries behind an Amazon API Gateway API. Render the data by using the D3.js JavaScript library.

Question 33

A company owns facilities with IoT devices installed across the world. The company is using Amazon Kinesis Data Streams to stream data from the devices to Amazon S3. The company's operations team wants to get insights from the IoT data to monitor data quality at ingestion. The insights need to be derived in near-real time, and the output must be logged to Amazon DynamoDB for further analysis.

Which solution meets these requirements?

Options:

Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using the default output from Kinesis Data Analytics.

Connect Amazon Kinesis Data Analytics to analyze the stream data. Save the output to DynamoDB by using an AWS Lambda function.

Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function. Save the output to DynamoDB by using the default output from Kinesis Data Firehose.

Connect Amazon Kinesis Data Firehose to analyze the stream data by using an AWS Lambda function. Save the data to Amazon S3. Then run an AWS Glue job on schedule to ingest the data into DynamoDB.

Question 34

A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near-real time. The company uses Amazon Kinesis Data Streams to stream the data to other applications. The dashboard must automatically refresh every 5 seconds. A data analytics specialist must design a solution that requires the least possible implementation effort.

Which solution meets these requirements?

Options:

Use Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

Use Apache Spark Streaming on Amazon EMR to read the data in near-real time. Develop a custom application for the dashboard by using D3.js.

Use Amazon Kinesis Data Firehose to push the data into an Amazon Elasticsearch Service (Amazon ES) cluster. Visualize the data by using a Kibana dashboard.

Use AWS Glue streaming ETL to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

Question 35

A company is building an analytical solution that includes Amazon S3 as data lake storage and Amazon Redshift for data warehousing. The company wants to use Amazon Redshift Spectrum to query the data that is stored in Amazon S3.

Which steps should the company take to improve performance when the company uses Amazon Redshift Spectrum to query the S3 data files? (Select THREE )

Use gzip compression with individual file sizes of 1-5 GB