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Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 357 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 357
Topic #: 1
[All AWS Certified Machine Learning - Specialty 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 data. The data scientist 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?

  • A. Import the data by using the None option.
  • B. Import the data by using the Stratified option.
  • C. Import the data by using the First K option. Infer the value of K from domain knowledge.
  • D. Import the data by using the Randomized option. Infer the random size from domain knowledge.
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Suggested Answer: C 🗳️
by MultiCloudIronMan at Oct. 30, 2024, 10:25 p.m.
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7f1fe73
2 weeks ago
Selected Answer: D
D. Import the data by using the Randomized option. Infer the random size from domain knowledge: This option selects a random sample of the data. Pros: It provides a representative sample of the entire dataset while using fewer compute resources than importing all data. Cons: There's a small chance of missing some rare anomalies, but this risk can be mitigated by choosing an appropriate sample size based on domain knowledge.
upvoted 1 times
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MultiCloudIronMan
2 weeks, 1 day ago
Selected Answer: C
Why Option C? Efficiency: Importing a subset of the data using the First K option minimizes compute resources while still providing a representative sample for exploratory data analysis (EDA). Domain Knowledge: Leveraging domain knowledge to determine the value of K ensures that the subset is relevant and sufficient for meaningful analysis.
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