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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A financial services company is deploying Microsoft Foundry to host generative AI workloads that process regulated customer data. The Microsoft Foundry environment must prevent any public network exposure while still allowing services managed by Microsoft Foundry to communicate with dependent Azure resources.
Security auditors require that all traffic to and from the Microsoft Foundry resource remain on private networks, with no public endpoints available.
You need to configure the Microsoft Foundry environment so that network access is restricted while maintaining full platform functionality.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

A) Disable public network access to the Microsoft Foundry resource.
B) Disable all inbound network access.
C) Use API key authentication for all model endpoints.
D) Configure a managed virtual network for the Microsoft Foundry resource.
E) Deploy the Microsoft Foundry resource in a separate Azure subscription.

2. You need to run large-scale inference jobs on millions of records periodically. Jobs are not latency-sensitive but must be cost-efficient and scalable. Which deployment option is MOST appropriate?

A) Local endpoint
B) Batch endpoint
C) Managed online endpoint
D) Notebook execution

3. Drag and Drop Question
A team maintains Infrastructure as Code (IaC) templates to provision Azure Machine Learning resources.
Provisioning must be triggered by changes in the templates and executed without manual intervention.
You need to automate resource provisioning.
Which action should you take for each requirement? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

4. A Retrieval-Augmented Generation (RAG) solution returns incomplete answers because relevant content is inconsistently retrieved from the knowledge source.
You need to improve RAG accuracy without changing the embedding model currently in use. You need to achieve this goal while minimizing operational costs.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

A) Tune chunk size and overlap to match content structure.
B) Implement an optimized re-ranker.
C) Increase token limits for all requests.
D) Optimize the length of embedding vectors.

5. A team manages an Azure Machine Learning workspace and deploys a model to an endpoint.
A deployed online endpoint shows inconsistent response times during periods of high traffic.
You need to identify potential performance degradation.
Which three metrics should you monitor? Each correct answer presents part of the solution.
Choose three.
NOTE: Each correct selection is worth one point.

A) Feature count
B) Dataset size
C) Requests per minute
D) Connections active
E) Request latency

Solutions: