Amazon Exam MLS-C01 Topic 1 Question 116 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 116
Topic #: 1

Each morning, a data scientist at a rental car company creates insights about the previous day's rental car reservation demands. The company needs to automate this process by streaming the data to Amazon S3 in near real time. The solution must detect high-demand rental cars at each of the company's locations. The solution also must create a visualization dashboard that automatically refreshes with the most recent data.

Which solution will meet these requirements with the LEAST development time?

AUse Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.

BUse Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.

CUse Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.

DUse Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.

Show Suggested Answer

Suggested Answer: A

The solution that will meet the requirements with the least development time is to use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3, detect high-demand outliers by using Amazon QuickSight ML Insights, and visualize the data in QuickSight. This solution does not require any custom development or ML domain expertise, as it leverages the built-in features of QuickSight ML Insights to automatically run anomaly detection and generate insights on the streaming data. QuickSight ML Insights can also create a visualization dashboard that automatically refreshes with the most recent data, and allows the data scientist to explore the outliers and their key drivers.References:

1: Simplify and automate anomaly detection in streaming data with Amazon Lookout for Metrics | AWS Machine Learning Blog

2: Detecting outliers with ML-powered anomaly detection - Amazon QuickSight

3: Real-time Outlier Detection Over Streaming Data - IEEE Xplore

4: Towards a deep learning-based outlier detection ... - Journal of Big Data

by Melina at Apr 30, 2025, 04:00 AM

Limited Time Offer

25%

Off

Get Premium MLS-C01 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Lilli

2 days ago

Option A seems like the easiest and most straightforward solution to meet the requirements. Why reinvent the wheel when Amazon has already provided the necessary tools?

upvoted 0 times

...

Leanora

7 days ago

I think option A is the best choice.

upvoted 0 times

...