Amazon MLS-C01 Exam - Topic 1 Question 112 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 112
Topic #: 1

A data scientist needs to create a model for predictive maintenance. The model will be based on historical data to identify rare anomalies in the data.

The historical data is stored in an Amazon S3 bucket. The data scientist needs to use Amazon SageMaker Data Wrangler to ingest the dat

a. The data scientists also needs to perform exploratory data analysis (EDA) to understand the statistical properties of the data.

Which solution will meet these requirements with the LEAST amount of compute resources?

AImport the data by using the None option.

BImport the data by using the Stratified option.

CImport the data by using the First K option. Infer the value of K from domain knowledge.

DImport the data by using the Randomized option. Infer the random size from domain knowledge.

Show Suggested Answer

Suggested Answer: C

To perform efficient exploratory data analysis (EDA) on a large dataset for anomaly detection, using the First K option in SageMaker Data Wrangler is an optimal choice. This option allows the data scientist to select the first K rows, limiting the data loaded into memory, which conserves compute resources.

Given that the First K option allows the data scientist to determine K based on domain knowledge, this approach provides a representative sample without requiring extensive compute resources. Other options like randomized sampling may not provide data samples that are as useful for initial analysis in a time-series or sequential dataset context.

by Bette at Feb 07, 2025, 08:00 PM

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Josephine

3 months ago

Randomized could work, but I doubt it’s the best choice here.

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Lavonne

3 months ago

Stratified sounds good too, but not sure it's the least resource-heavy.

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Mi

3 months ago

Wait, why would you use the None option? That seems risky.

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Daniel

4 months ago

Definitely agree with C, it's efficient!

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Raina

4 months ago

I think option C makes the most sense for EDA.

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Rory

4 months ago

I vaguely remember that the Randomized option might help in getting a representative sample, but I'm uncertain if it’s the best for minimizing compute usage.

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Josue

4 months ago

I feel like the Stratified option could be useful for ensuring we capture anomalies, but it might not be the most efficient in terms of compute.

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Allene

4 months ago

I think the First K option might be a good choice since it allows for quick ingestion, but I can't recall if it really uses the least resources.

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Gussie

5 months ago

I remember we discussed the importance of selecting the right data import option in SageMaker, but I'm not sure which one minimizes compute resources the most.

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Annice

5 months ago

I'm a bit confused by the "First K" and "Randomized" options. I'm not sure how I would determine the right values for K or the random size. That seems a bit risky without more context about the data. I might stick with the safer option A or B.

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Loreta

5 months ago

I think the key here is to find the right balance between resource usage and getting a good understanding of the data. The Stratified option might be a good compromise - it should give me a more representative sample without using too many resources.

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Shaniqua

5 months ago

Hmm, I'm not sure. The question mentions the need to perform exploratory data analysis, so I'm wondering if the None option will give me enough data to do that effectively. Maybe I should consider the other options that could provide a more representative sample.

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Meghann

5 months ago

This seems like a straightforward question. I'd go with option A - importing the data using the None option. That should be the least resource-intensive approach.

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Gerald

12 months ago

I'm going with option C. It's the 'First K' method, which is obviously the best choice since 'K' stands for 'Kool-Aid'.

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Gennie

11 months ago

I agree. It's important to choose the method that requires the least amount of compute resources.

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Malcom

11 months ago

That makes sense. 'First K' method could help in understanding the data better.

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Juliana

11 months ago

I think 'K' in option C refers to the number of samples to import.

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Catrice

11 months ago

Option C sounds interesting. 'First K' method could be a good choice.

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Fernanda

12 months ago

Woohoo, let's import the data using the 'Enchant' option! I heard it makes the data more magical and reduces compute needs by 420%.

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Carline

12 months ago

Option B sounds interesting, but I wonder if the data is truly stratified. Might be better to stick with a simpler approach like C or D.

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Helaine

11 months ago

Yeah, option D might also be a good choice if you can infer the random size accurately.

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Aleshia

11 months ago

I think option C could work well if you have good domain knowledge.

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Kris

12 months ago

But with option C, we can infer the value of K from domain knowledge, which could save on resources.

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Rozella

1 year ago

I disagree, I believe option D would require the least amount of compute resources.

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Rex

1 year ago

Hmm, I'm not sure. Option D might be better if we don't have much domain knowledge to infer the right sample size. Randomized sampling could be a safer bet.

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