Amazon Exam DAS-C01 Topic 1 Question 86 Discussion

Actual exam question for Amazon's DAS-C01 exam

Question #: 86
Topic #: 1

A social media company is using business intelligence tools to analyze data for forecasting. The company is using Apache Kafka to ingest dat

a. The company wants to build dynamic dashboards that include machine learning (ML) insights to forecast key business trends.

The dashboards must show recent batched data that is not more than 75 minutes old. Various teams at the company want to view the dashboards by using Amazon QuickSight with ML insights.

Which solution will meet these requirements?

AReplace Kafka with Amazon Managed Streaming for Apache Kafka (Amazon MSK). Use AWS Data Exchange to store the data in Amazon S3. Use SPICE in QuickSight Enterprise edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.

BReplace Kafka with an Amazon Kinesis data stream. Use AWS Data Exchange to store the data in Amazon S3. Use SPICE in QuickSight Standard edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.

CConfigure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to store the data in Amazon S3 with a max buffer size of 60 seconds. Use SPICE in QuickSight Enterprise edition to refresh the data from Amazon S3 each hour. Use QuickSight to create a dynamic dashboard that includes forecasting and ML insights.

DConfigure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to store the data in Amazon S3 with a max buffer size of 60 seconds. Refresh the data in QuickSight Standard edition SPICE from Amazon S3 by using a scheduled AWS Lambda function. Configure the Lambda function to run every 75 minutes and to invoke the QuickSight API to create a dynamic dashboard that includes forecasting and ML insights.

Show Suggested Answer

Suggested Answer: C

by Tiffiny at Feb 09, 2024, 10:23 PM

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Jesusa

6 months ago

Interesting point, Youlanda. Option C does sound like a good choice as well.

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Youlanda

6 months ago

I think option C is the most efficient solution. It utilizes Amazon Kinesis Data Firehose effectively.

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Von

7 months ago

Option D seems a bit more complicated with the use of Lambda functions. I still prefer option A.

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Mammie

7 months ago

I'm not sure, option D also sounds like it could work. What do you think?

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Jesusa

7 months ago

I agree with Von. Option A seems to meet all the requirements.

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Von

7 months ago

I think option A is the best solution.

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Brandee

8 months ago

Haha, I'm just hoping I don't get this one wrong on the exam. Though, to be honest, I'm leaning towards option A. Using Amazon MSK and AWS Data Exchange seems like a straightforward approach, and the SPICE refresh should keep the data up-to-date.

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Kandis

8 months ago

I'm not too sure about that, Lili. Option D also looks promising, with the scheduled Lambda function refreshing the data in QuickSight every 75 minutes. That might be a more cost-effective solution than the Enterprise edition.

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Lili

8 months ago

I think option C might be the way to go. Using the Kafka-Kinesis-Connector to publish the data to a Kinesis Data Firehose delivery stream could help us meet the freshness requirement. And the Enterprise edition of QuickSight should give us the SPICE and ML capabilities we need.

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Thaddeus

8 months ago

This is a tricky one! I'm not sure which option would be the best solution. We need to consider the requirements carefully, like the 75-minute data freshness and the need for ML insights in the dashboards.

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