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AIP-C01 practice question: Validating data quality before it reaches a model (Glue Data Quality, Lambda checks) (1 of 2)

Foundation Model Integration, Data Management, and Compliance A free question from our original AIP-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A logistics company keeps shipment records in Parquet files in an Amazon S3 data lake. A nightly AWS Glue job converts the records into JSON documents, which are then ingested into a knowledge base that dispatchers query.

In recent weeks, records with missing tracking numbers, empty origin fields and duplicate shipment IDs reached the knowledge base, and dispatchers received wrong answers.

The data engineering team wants bad records caught before ingestion, with the rules written as declarative checks instead of custom code, and the clean records should still flow on schedule.

Which change meets these requirements?

Choose one answer.

  1. A. Add a Lambda function that runs after each knowledge base sync, finds documents with empty fields, and deletes them from the index, so that dispatchers stop seeing them.
  2. B. Ask dispatchers to report wrong answers every week, and have an engineer correct the affected shipment records by hand in the S3 bucket before the next nightly run.
  3. C. Turn on Amazon Macie for the S3 bucket, so that it flags records with missing tracking numbers and duplicate shipment IDs before the nightly Glue job runs.
  4. D. Define Glue Data Quality rulesets for completeness and uniqueness in the nightly Glue job, and route failing records to a quarantine prefix before ingestion runs.
Show the answer and explanation

Answer: D

  • A. Wrong: The bad documents would already be indexed and visible to dispatchers before the function removed them. Checking after ingestion also means the team has to write custom deletion code, which the requirements avoid.
  • B. Wrong: Manual review finds problems only after dispatchers have already received wrong answers. It does not provide the automated pre-ingestion checks the team asked for.
  • C. Wrong: Macie discovers sensitive data such as personal information in S3, not missing fields or duplicate keys. It would not enforce the quality rules, so this option does not meet the requirement.
  • D. Right: Glue Data Quality rules are declarative and run inside the existing Glue job, so no custom checking code is needed. Failing records are quarantined before ingestion, while clean records continue on the normal schedule. Learn more: AWS Glue Data Quality (opens in a new tab)

Explanation

Glue Data Quality checks records before they become documents, using declarative rules inside the existing nightly job. Quarantining failed records keeps the bad data out of the knowledge base while clean records still flow. Checks after ingestion, manual review and Macie each miss at least one requirement.

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