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MLA-C02 practice question: SageMaker Feature Store (2 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 regional bank has built a feature group in SageMaker Feature Store for customer spending features. The fraud team wants to build a training dataset from the last 18 months of feature values without affecting the online lookups used during card authorizations.

The team wants to query the history with standard SQL, and it does not want to build or maintain its own export pipeline for the feature data.

Which approach meets these requirements?

Choose one answer.

  1. A. Query the online store with repeated GetRecord calls for each customer and date, and save the results to a local file on a notebook instance.
  2. B. Turn on a time-to-live setting on the online store so that old records are kept for exactly 18 months, and query that store with Athena.
  3. C. Enable the offline store on the feature group, and query its history in Amazon S3 with Athena through the Glue Data Catalog table Feature Store builds.
  4. D. Copy each feature value from the online store into a new Amazon DynamoDB table every hour, and run a nightly scan of that table to build the training dataset.
Show the answer and explanation

Answer: C

  • A. Wrong: The online store is designed for current lookups at authorization time, and per-record calls for 18 months of history are slow and costly. The result also does not use the SQL access the team wants.
  • B. Wrong: Athena queries Amazon S3 and the Data Catalog rather than the online store, so this does not provide the SQL path. A time-to-live setting also deletes records rather than keeping the history for training.
  • C. Right: Correct. The offline store keeps historical feature records in Amazon S3, and by default Feature Store builds an AWS Glue Data Catalog table for the feature group, so Athena can query it with SQL. Training queries never touch the online store used for authorizations. Learn more: Create a dataset from your feature groups (opens in a new tab)
  • D. Wrong: Hourly copies add a second pipeline and scan the table during the nightly build, which is slow and expensive. The offline store already keeps this history and is queryable directly.

Explanation

The offline store keeps feature history in Amazon S3, and Feature Store builds an AWS Glue Data Catalog table for it by default, so Athena can query the history with SQL. The online store stays dedicated to low-latency reads at authorization time. Pulling history through the online store or copying it into other tables adds cost and a second pipeline.

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