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Amazon Exam MLS-C01 Topic 1 Question 112 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 112
Topic #: 1
[All MLS-C01 Questions]

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?

Show Suggested Answer Hide 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.


Contribute your Thoughts:

Gerald
2 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
30 days ago
I agree. It's important to choose the method that requires the least amount of compute resources.
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Malcom
1 months ago
That makes sense. 'First K' method could help in understanding the data better.
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Juliana
1 months ago
I think 'K' in option C refers to the number of samples to import.
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Catrice
1 months ago
Option C sounds interesting. 'First K' method could be a good choice.
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Fernanda
2 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
2 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
1 months ago
Yeah, option D might also be a good choice if you can infer the random size accurately.
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Aleshia
2 months ago
I think option C could work well if you have good domain knowledge.
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Kris
2 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
2 months ago
I disagree, I believe option D would require the least amount of compute resources.
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Rex
2 months 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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Salina
2 months ago
Yes, it's a safer approach when we lack domain knowledge to determine the sample size.
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Corrina
2 months ago
I agree, Option D seems like a good choice for random sampling.
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Kallie
3 months ago
I'd go with option C. Seems like a good way to sample the data and get a representative subset without wasting too many resources.
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Leanna
1 months ago
Option C seems like a more efficient approach.
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Alesia
1 months ago
I would go with option A, keeping it simple.
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Fabiola
2 months ago
I agree, it's a smart way to sample the data.
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Sabrina
2 months ago
I think option C is a good choice.
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Kris
3 months ago
I think option C is the best choice.
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