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MLA-C02 practice question: SageMaker Feature Store (1 of 2)

Data Preparation for ML and AI A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A ski resort runs a dynamic lift-ticket pricing model. Its prediction service needs about 40 features per request, such as recent bookings and snow depth for each resort, and it must answer within tens of milliseconds.

The same features must also be available as historical snapshots so the team can retrain the model each week, and the training and serving copies must come from the same feature definitions.

Which design meets these requirements?

Choose one answer.

  1. A. Create a feature group with only the online store enabled, since the prediction service is the most important consumer.
  2. B. Create a SageMaker Feature Store feature group with the online store enabled for the prediction service and the offline store enabled for training datasets.
  3. C. Keep the features in Amazon S3 as Parquet files only, and have the prediction service read the files for each request.
  4. D. Write the features to an Amazon DynamoDB table for serving, and export a copy to Amazon S3 with a separate script before each training run.
Show the answer and explanation

Answer: B

  • A. Wrong: The online store keeps current values for lookups, so it does not give the weekly retraining job the historical snapshots it needs. The team would have to build a second pipeline for training data.
  • B. Right: Correct. The online store serves low-latency lookups for inference, and the offline store keeps historical records in Amazon S3 for training. Both stores come from the same feature group definition. Learn more: Create, store, and share features with Feature Store (opens in a new tab)
  • C. Wrong: Reading files from S3 for each request is far slower than the tens-of-milliseconds target. The option also gives no shared feature definitions between training and serving.
  • D. Wrong: DynamoDB can serve the low-latency reads, but the two copies come from separate code paths and can drift apart. The team would own the consistency that Feature Store provides.

Explanation

Feature Store separates the same feature definitions into an online store for low-latency inference and an offline store in Amazon S3 for historical training data. Serving and training then use the same feature group. Single-store designs and separate DynamoDB and S3 copies each miss one requirement.

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