AI-300 Practice Questions — Page 3

An organization operates a generative AI application in production by using Microsoft Foundry. The application serves live user traffic and is updated by a data scientist team regularly as prompts and models evolve.

The application intermittently times out during production use, which requires ongoing visibility into runtime behavior.

The team must also validate model quality and safety before releasing new updates to avoid introducing regressions.

You need to apply the correct mechanisms for continuous runtime monitoring and for release time validation.

Which mechanisms should you use for each requirement? To answer, move the appropriate mechanisms to the correct requirements. You may use each mechanism 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.

An organization is deploying several generative AI workloads by using Microsoft Foundry. Each workload must meet different requirements related to data governance, task specialization, and operational cost control.

The organization requires models that meet the following requirements:

Model behavior aligns with the task being performed.

Data handling aligns with internal governance policies.

Operational complexity and cost are justified by workload needs.

You need to select the foundation model options that meet the requirements.

Which three models can you select? Each correct answer presents a complete solution. Choose three.

NOTE: Each correct selection is worth one point.

A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.

The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.

You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.

What should you configure for each requirement? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

A company is creating an internal tool that summarizes long meeting transcripts and extracts action items.

The model must:

Process text inputs up to 200k tokens long.

Generate concise summaries in seconds.

Support interactive testing before integration into the app.

You need to select, deploy, and test a model that supports summarization with low latency.

How should you complete the configuration plan? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You are reviewing a dataset that will be used for an advanced fine-tuning job in Microsoft Foundry.

The fine-tuning job uses preference comparison data.

You review the following dataset excerpt.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.