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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A team accidentally deploys an outdated model version due to incorrect tagging. You need to enforce strict deployment governance and version control. What should you implement?

A) Increase logging
B) Use version pinning and approval workflows
C) Store models locally
D) Rename models

2. Your model requires access to external APIs using sensitive credentials during inference. You must ensure credentials are not exposed in code, logs, or environment variables. What should you implement?

A) Encrypt credentials locally
B) Store in config files
C) Hardcode credentials
D) Use Azure Key Vault with managed identity

3. You manage an Azure Machine learning workspace. You develop a machine learning model.
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?

A) Azure Container Instances (ACI)
B) Local deployment
C) Azure Machine Learning compute clusters
D) Azure Kubernetes Service (AKS)

4. An Azure Machine Learning workspace processes sensitive training data.
The workspace must NOT be accessible from the public internet.
You need to restrict network access.
Which configuration should you implement?

A) Network security groups
B) Azure Firewall rules
C) Service endpoints
D) Private endpoints

5. Hotspot Question
A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions: