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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. Hotspot Question
A team is provisioning a new Azure Machine Learning workspace for a production project.
The workspace must support secure secret storage and operational monitoring. The team requires the workspace to be created with the correct dependent resources to meet security and monitoring requirements.
You need to configure the required dependencies when the team creates the workspace.
Which resources should you associate with the workspace? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

2. A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?

A) Set minimum instance count
B) Switch to batch endpoint
C) Increase timeout settings
D) Reduce logging

3. An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?

A) Publish the asset as a pipeline component.
B) Publish the asset to an Azure Machine Learning registry.
C) Create a shared Azure Machine Learning environment that includes the asset.
D) Enable workspace-level Git integration and sync assets between repositories.

4. A team is validating a generative AI assistant for a company. The assistant generates responses by using internal knowledge sources.
The company requires assurance that responses are accurate, supported by sources, and related to the user prompts before enabling production access.
You need to implement quality metrics that confirm the assistant produces reliable and meaningful responses.
Which two evaluation metrics should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) Groundedness
B) Tone
C) Relevance
D) Harmfulness
E) Fairness

5. You manage a Microsoft Foundry project. You build a multi-turn chatbot application.
You plan to filter your traces to identify issues while observing how the application is responding.
The solution must not use an external knowledge base.
You need to select an evaluation metric.
Which built-in evaluator should you use?

A) RelevanceEvaluator
B) SimilarityEvaluator
C) QAEvaluator
D) CoherenceEvaluator

Solutions: