Amazon MLS-C01 Exam - 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

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Beckie

2 months ago

C looks solid too, but not sure about using RCF.

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Rosalyn

3 months ago

I think B is better for accuracy with SageMaker.

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Antione

3 months ago

D? Really? QuickSight ML Insights might not cut it.

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Lavina

3 months ago

Wait, can QuickSight really handle all that? Sounds too easy!

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Kris

3 months ago

A seems the quickest with Firehose and QuickSight!

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Lisha

4 months ago

I feel like using Random Cut Forest in SageMaker might take longer to implement, but it could provide more accurate outlier detection.

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Carlee

4 months ago

I practiced a similar question where we had to choose between Firehose and Streams, and I think Firehose is generally better for direct data delivery to S3.

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Sueann

4 months ago

I'm not entirely sure, but I think using QuickSight ML Insights could be more straightforward than training a model in SageMaker.

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Darnell

4 months ago

I remember we discussed Kinesis Data Firehose being easier to set up than Kinesis Data Streams, which might save time.

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Kris

4 months ago

This is a tricky one. I like that option C uses the RCF model in SageMaker, as that seems like a robust way to detect the high-demand outliers. But I'm a bit concerned about the complexity of setting that up compared to the QuickSight ML Insights in option A. And Kinesis Data Streams versus Firehose - I'm not sure there's a clear winner there. Hmm, I might have to go with option A as the path of least resistance.

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Minna

5 months ago

Okay, let me think this through step-by-step. We need to get the data from the rental car company into S3 in near real-time, so Kinesis is a good choice there. Then we need to detect high-demand outliers, and the ML Insights in QuickSight seem like they could handle that pretty easily. And QuickSight can definitely handle the visualization requirements. I think option A is the simplest and most straightforward solution.

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Marisha

5 months ago

Hmm, I'm a bit unsure about this one. The requirements mention near real-time streaming and automatic refreshing, so I'm wondering if Kinesis Data Streams might be a better fit than Firehose. And I'm not super familiar with the RCF model in SageMaker - I'd have to look into that a bit more. Maybe option B would be the safer bet?

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Bambi

5 months ago

This looks like a pretty straightforward question. I think option C is the way to go - it uses Kinesis Data Firehose to stream the data to S3, then leverages the RCF model in SageMaker to detect high-demand outliers, and finally visualizes everything in QuickSight. Seems like the most complete solution with the least amount of custom development.

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Cordell

8 months ago

Ah, the age-old battle between simplicity and complexity. I say, go with whatever gets the job done the fastest. Time is money, my friends!

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Wilda

7 months ago

Agreed, simplicity is key when it comes to saving time and money.

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Lisbeth

8 months ago

Use Amazon Kinesis Data Firehose to stream the data to Amazon S3 and Amazon QuickSight to create the visualization dashboard.

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Ashanti

9 months ago

Option D? Really? Why would you want to use Kinesis Streams and QuickSight ML Insights when Firehose and SageMaker RCF are available? That's just making things harder than they need to be.

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Anastacia

9 months ago

Option C is the one for me. Kinesis Firehose and SageMaker RCF? That's a winning combo right there. Plus, QuickSight is so easy to use.

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Elizabeth

9 months ago

Hmm, Option B with the Random Cut Forest model in SageMaker could be interesting. Might be a bit more complex, but it could provide more advanced analytics.

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Santos

7 months ago

True, Option A could be easier to implement and still meet the requirements.

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Carli

7 months ago

What about Option A with Kinesis Data Firehose and Amazon Redshift? It seems like a simpler solution.

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Sonia

7 months ago

I agree, it might be a bit more complex but the advanced analytics could be worth it.

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Cassandra

8 months ago

Option B with the Random Cut Forest model in SageMaker sounds like a good choice.

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Eveline

9 months ago

I agree with Leanora, option A seems like the most straightforward solution for this scenario.

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Leanora

9 months ago

But option A uses Amazon Kinesis Data Firehose, which can stream data directly to S3 with minimal development time.

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Lenita

10 months ago

I disagree, I believe option C is more efficient.

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Lonna

10 months ago

I agree with Lilli. Option A is the way to go. It's the classic 'keep it simple, stupid' approach.

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Lottie

9 months ago

Agreed. It's important to prioritize simplicity and effectiveness when implementing a solution like this.

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Truman

9 months ago

I think so too. Using Amazon Kinesis Data Firehose and QuickSight makes the process streamlined and easy to manage.

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Thea

9 months ago

Option A is definitely the most efficient choice. It covers all the requirements without unnecessary complexity.

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Lilli

10 months 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?

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