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Question

A Machine Learning Engineer is creating an integration test for a machine learning system that must validate how feature engineering, model training, evaluation, deployment, and inference pipelines interact. They need to choose a test-implementation approach that follows best practices for comprehensive integration testing.

Which approach accomplishes this?

A Implement property-based tests that generate random input data and verify that each pipeline component produces outputs within expected statistical ranges.
B Design a test that loads production data, runs the complete feature engineering pipeline, trains a model on the features, and evaluates model performance against baseline metrics.
C Implement component-level smoke tests that verify each pipeline stage can start up successfully and produce non-empty outputs when given sample data.
D Create separate unit tests for each pipeline component and run them sequentially to verify individual functionality before combining them and evaluating model performance against baseline metrics.

Explanation

Comprehensive ML integration testing runs connected pipeline stages with realistic data and validates the trained model against baseline performance metrics. This verifies that feature generation, training, and evaluation work together and that the integrated system produces an acceptable model outcome.

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