PMLE Architecting Low-Code ML Solutions Practice Question

A financial services company uses Document AI to process loan applications. They want to ensure that any documents the model cannot process with high confidence are reviewed by a human before finalizing the decision. Which Document AI feature should they enable?

⚠ Common exam trap

PMLE often tests the difference between model improvement features (retraining, AutoML) and operational review features (HITL) — candidates may choose retraining when the requirement is for a human review step, not model accuracy improvement.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Human-in-the-Loop (HITL)

Human-in-the-Loop (HITL) in Document AI allows you to route documents that the model processes with low confidence to human reviewers for validation or correction before finalizing. This directly matches the requirement to have a human review any documents the model cannot process with high confidence.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
AutoML Tables model retraining
Why it's wrong here
AutoML Tables builds predictive models on structured tabular data; it cannot route low-confidence document extractions to reviewers. Human-in-the-Loop is the feature that pauses processing and sends uncertain documents for manual validation, which is what the scenario demands.
✗
Cloud DLP for data inspection
Why it's wrong here
Cloud DLP inspects and redacts sensitive data such as personally identifiable information; it does not evaluate model confidence or trigger human review. Human-in-the-Loop is the correct feature when uncertain extractions must be validated by a person before finalising.
✗
Increase the number of processors
Why it's wrong here
Scaling processor count raises throughput for parallel document volumes, but it cannot route low-confidence extractions to a reviewer. Human review requires a confidence threshold that triggers assessment, which Document AI provides through human-in-the-loop configuration. Adding processors would be the right choice when ingestion volume, not accuracy assurance, is the bottleneck.
✓
Human-in-the-Loop (HITL)
Why this is correct
Human-in-the-Loop routes low-confidence extractions to human reviewers before decisions finalise, directly satisfying the requirement that uncertain documents receive manual review. Document AI assigns confidence scores per field, and HITL triggers review when those scores fall below your configured threshold, ensuring no unreliable output reaches the loan decision.

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PMLE Architecting Low-Code ML Solutions Practice Question

Why each option matters

Option-by-option breakdown

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