MLA-C01 Deployment and Orchestration of ML Workflows Practice Question

A team is building a SageMaker Pipeline that trains a model and then registers it in the SageMaker Model Registry. They want the pipeline to automatically deploy the model to a real-time endpoint only after a human approves the model package. Which two actions should the team take to implement this approval-gated deployment? (Choose two.)

⚠ Common exam trap

The trap here is assuming a pipeline step can wait for human approval, when pipelines are finite executions and approval events must be handled by an external event-driven mechanism.

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

✓

Add a RegisterModel step to the pipeline that creates a model package with a PendingManualApproval status.

Manual approval gating relies on the model package lifecycle: RegisterModel creates a package in PendingManualApproval, and a reviewer later approves it. Because the pipeline execution does not pause for review, an EventBridge rule watching for the Approved state change is what triggers the downstream deployment automation, keeping humans in the loop.

Answer analysis

Option-by-option breakdown

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

✗
Set the model package status to Approved directly in the RegisterModel step configuration so the endpoint deploys without human intervention.
Why it's wrong here
Setting the package to Approved programmatically bypasses the human review the team requires. It also defeats the purpose of the approval gate, since deployment would occur automatically rather than after a person evaluates the model, contradicting the stated requirement.
✓
Add a RegisterModel step to the pipeline that creates a model package with a PendingManualApproval status.
Why this is correct
RegisterModel creates a model package group entry and sets the package status to PendingManualApproval when manual approval is configured. This produces the artifact that a reviewer evaluates and approves, which is the trigger point the deployment automation watches for.
✗
Use a SageMaker Clarify processing step to approve the model package when bias metrics fall within thresholds.
Why it's wrong here
Clarify computes bias and explainability metrics; it does not manage model package approval states. While its reports can inform a reviewer's decision, the service cannot transition a package to Approved, so it cannot serve as the approval mechanism for gated deployment.
✗
Add a ConditionStep to the pipeline that checks the model package status immediately after RegisterModel and branches to a deployment step.
Why it's wrong here
A ConditionStep evaluates within the running pipeline execution, but the pipeline has already finished or moved past the registration step long before a human approves. The pipeline cannot block indefinitely waiting for manual review, so this approach cannot gate deployment on a later human decision.
✓
Configure an Amazon EventBridge rule that matches the model package state change to Approved and invokes a target that starts the deployment.
Why this is correct
When a reviewer approves the model package, the status changes to Approved and EventBridge emits a matching event. An EventBridge rule can route that event to a target such as a Lambda function or Step Functions state machine that creates the endpoint, achieving deployment only after approval.

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MLA-C01 Deployment and Orchestration of ML Workflows Practice Question

Why each option matters

Option-by-option breakdown

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