Model approval workflows in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🔀 Workflow

intermediate

Identify the correct sequence in a model approval workflow

Which of the following sequences correctly represents the typical steps in a model approval workflow?

A 1,2,3,4

B 2,1,3,4

C 1,3,2,4

D 3,1,2,4

💡 Hint

Think about the logical order from building to deploying a model.

The model is first trained and evaluated, then validated by experts, followed by an approval decision, and finally deployed.

🧠 Conceptual

intermediate

Purpose of model approval in MLOps

What is the main purpose of having a model approval step in an MLOps pipeline?

A To reduce the size of the model for faster inference

B To speed up the deployment process by skipping testing

C To ensure the model meets quality and compliance standards before deployment

D To automatically retrain the model without human intervention

💡 Hint

Think about why we want to check a model before using it in real life.

Model approval ensures the model is reliable, safe, and meets required standards before it is used in production.

❓ Troubleshoot

advanced

Diagnose failure in automated model approval pipeline

An automated model approval pipeline fails to trigger the approval step after model validation. Which is the most likely cause?

A The validation results were not saved in the expected storage location

B The model training step completed successfully

C The deployment environment is offline

D The model size exceeds the maximum allowed limit

💡 Hint

Consider what the approval step depends on to start.

If validation results are missing or misplaced, the approval step cannot proceed because it lacks required input.

💻 Command Output

advanced

Output of model approval status check command

What is the output of the command `mlflow models get-approval-status --model-id 123` if the model is approved?

MLOps

mlflow models get-approval-status --model-id 123

A {"model_id": 123, "status": "pending", "approved_by": null}

B {"model_id": 123, "status": "approved", "approved_by": "team_lead"}

C Error: Model ID 123 not found

D {"model_id": 123, "status": "rejected", "approved_by": "qa_team"}

💡 Hint

Look for the status that indicates approval.

The output shows the model ID, status as approved, and who approved it.

✅ Best Practice

expert

Best practice for integrating human approval in CI/CD for models

Which approach best integrates a human approval step into an automated CI/CD pipeline for model deployment?

A Approve models based on training accuracy alone without human review

B Automatically deploy all models without any approval to speed up delivery

C Skip validation and rely on monitoring after deployment to catch issues

D Use a manual approval gate in the pipeline that pauses deployment until a designated person approves

💡 Hint

Think about how to combine automation with necessary human checks.

A manual approval gate pauses the pipeline and requires explicit human confirmation before continuing, ensuring safety and compliance.

Practice

1. What is the main purpose of a model approval workflow in MLOps?

easy

A. To delete old models from storage

B. To train models faster using more data

C. To deploy models automatically without any checks

D. To check and approve machine learning models before they are used in production

Solution

1. Step 1: Understand the role of model approval workflows

   Model approval workflows are designed to ensure models are reviewed and tested before use.

2. Step 2: Identify the correct purpose

   The main goal is to check and approve models to keep production safe and reliable.

3. Final Answer:

   To check and approve machine learning models before they are used in production -> Option D

4. Quick Check:

   Model approval = safety check before use [OK]

Hint: Approval means checking before use, not training or deleting [OK]

Common Mistakes:

• Confusing approval with training or deployment
• Thinking approval deletes models
• Assuming approval skips checks

2. Which of the following is the correct syntax to add a manual approval step in a typical MLOps pipeline YAML?

easy

A. steps:
- approval_step:
users: ['team_lead']

B. steps:
- name: manual_approval
type: approval
inputs:
approvers: ['team_lead']

C. steps:
- manual_approval:
approvers: 'team_lead'

D. steps:
- name: approval
type: manual
approvers: team_lead

Solution

1. Step 1: Review typical YAML structure for approval steps

   Approval steps usually have a name, type, and inputs including approvers as a list.

2. Step 2: Match syntax with correct YAML format

   steps: - name: manual_approval type: approval inputs: approvers: ['team_lead'] correctly uses 'name', 'type', and 'inputs' with a list of approvers.

3. Final Answer:

   steps: - name: manual_approval type: approval inputs: approvers: ['team_lead'] -> Option B

4. Quick Check:

   Correct YAML keys and list format = steps: - name: manual_approval type: approval inputs: approvers: ['team_lead'] [OK]

Hint: Look for 'name', 'type', and list of approvers in YAML [OK]

Common Mistakes:

• Using wrong keys like 'users' instead of 'approvers'
• Not using list format for approvers
• Incorrect indentation or missing 'type'

3. Given this simplified MLOps pipeline snippet, what will be the output status after running the approval step if the approver rejects the model?

steps:
  - name: train_model
    type: training
  - name: approve_model
    type: approval
    inputs:
      approvers: ['alice']
    on_reject: fail_pipeline

medium

A. Pipeline skips approval and deploys

B. Pipeline continues to deploy the model

C. Pipeline fails and stops immediately

D. Pipeline retries training automatically

Solution

1. Step 1: Understand the approval step behavior

   The approval step waits for 'alice' to approve or reject the model.

2. Step 2: Analyze the 'on_reject' action

   If rejected, the pipeline is set to 'fail_pipeline', which stops the process immediately.

3. Final Answer:

   Pipeline fails and stops immediately -> Option C

4. Quick Check:

   Reject triggers fail_pipeline = stop [OK]

Hint: 'on_reject: fail_pipeline' means stop on rejection [OK]

Common Mistakes:

• Assuming pipeline continues after rejection
• Thinking approval is skipped
• Believing training retries automatically

4. You have this approval step configuration but the pipeline never pauses for approval. What is the likely error?

steps:
  - name: approve_model
    type: approval
    inputs:
      approvers: 'bob'

medium

A. Approvers should be a list, not a string

B. Missing 'type' field for approval step

C. Approval step name must be 'manual_approval'

D. Pipeline needs a trigger to pause

Solution

1. Step 1: Check the 'approvers' format

   The 'approvers' field must be a list, e.g., ['bob'], not a plain string.

2. Step 2: Understand impact of wrong format

   Using a string causes the pipeline to ignore the approval step or not pause.

3. Final Answer:

   Approvers should be a list, not a string -> Option A

4. Quick Check:

   Approvers list format required = Approvers should be a list, not a string [OK]

Hint: Approvers must be a list, even if one person [OK]

Common Mistakes:

• Using string instead of list for approvers
• Changing step name unnecessarily
• Assuming pipeline triggers control pause

5. You want to create a model approval workflow that automatically approves models with accuracy above 90%, but requires manual approval otherwise. Which workflow design achieves this?

hard

A. Add an automatic test step checking accuracy, then a conditional approval step that auto-approves if accuracy > 90%, else manual approval

B. Only use manual approval for all models regardless of accuracy

C. Skip approval if accuracy is above 90%, else reject the model automatically

D. Deploy all models first, then ask for approval later

Solution

1. Step 1: Define automatic test for accuracy

   Use a test step to check if model accuracy is above 90% automatically.

2. Step 2: Set conditional approval based on test result

   If accuracy > 90%, auto-approve; otherwise, require manual approval to ensure safety.

3. Final Answer:

   Add an automatic test step checking accuracy, then a conditional approval step that auto-approves if accuracy > 90%, else manual approval -> Option A

4. Quick Check:

   Auto test + conditional approval = Add an automatic test step checking accuracy, then a conditional approval step that auto-approves if accuracy > 90%, else manual approval [OK]

Hint: Combine auto test with conditional manual approval [OK]

Common Mistakes:

• Skipping manual approval for low accuracy models
• Approving all models without checks
• Approving after deployment instead of before