AWS Certified Data Engineer Associate DEA-C01 Practice Question
Your team uses Amazon SageMaker Pipelines to orchestrate preprocessing, training, and model registration steps. Compliance reviewers ask for a programmatic way to answer questions such as "Which S3 dataset and processing job produced the model currently deployed to the fraud-detection endpoint?" Which AWS solution provides the fastest, fully managed way to capture and query this lineage without adding custom metadata stores?
Schedule AWS Glue crawlers after each pipeline step and use Glue Data Catalog versioning to derive lineage.
Deliver SageMaker job events to CloudTrail, store them in Amazon Athena, and join logs to reconstruct lineage.
Write pipeline metadata to a DynamoDB table from AWS Step Functions and query the table for lineage details.
Enable Amazon SageMaker ML lineage tracking and query the lineage graph with the SageMaker Lineage API.
Amazon SageMaker ML lineage tracking automatically creates lineage entities for every SageMaker Pipeline execution, including artifacts for datasets, processing jobs, models, and endpoints. You can explore the relationships by calling the SageMaker Lineage API or using the SDK's get_lineage_graph and query_lineage methods, satisfying auditors with no additional infrastructure. Glue crawlers catalog data but do not connect it to model artifacts, CloudTrail logs require complex correlation logic, and a custom DynamoDB table would duplicate functionality already built into SageMaker and add operational overhead.
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What is Amazon SageMaker ML lineage tracking?
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How does the SageMaker Lineage API work?
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Why doesn’t AWS Glue or DynamoDB work for lineage tracking in SageMaker Pipelines?
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What is Amazon SageMaker ML lineage tracking?
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How does the SageMaker Lineage API work?
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Why not use AWS Glue for lineage tracking in SageMaker?
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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