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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have a chat app in a Microsoft Foundry project and an Azure AI Search vectorized index.
You need to connect to the index to meet the following requirements:
- Complex questions must retrieve information from multiple chunks.
- Multi-turn conversations must influence retrieval planning.
- Retrievals must run in parallel to reduce latency.
Which retrieval approach should you use?

A) agentic Retrieval Augmented Generation (RAG)
B) classic Retrieval Augmented Generation (RAG)
C) chain of thought
D) iterative retrieval

2. Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.
Does this meet the goal?

A) Yes
B) No

3. You have an application that uses a prebuilt Azure Language model.
You need to generate a summary that meets the following requirements:
- Summarizes documents that contain multiple sentences
- Preserves the original sentence order
- Returns exactly three sentences
Which Language service feature should you use?

A) sentiment analysis
B) key phrase extraction
C) abstractive summarization
D) extractive summarization

4. You have a Microsoft Foundry project that contains a prompt agent used by a customer support web app.
The agent is invoked from a Python service that does NOT run in the Foundry portal.
You need to implement end-to-end tracing to capture latency breakdowns and exceptions across agent runs.
Which two components can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A) the Azure Monitor Agent
B) Application Insights
C) OpenTelemetry
D) Microsoft Sentinel
E) a Log Analytics workspace

5. You have an invoice-processing application named App1 that uses Azure Constant Understanding in Foundry Tools.
You are building a new Content Understanding pipeline named Pipeline1 that must meet the following requirements:
- Compare an invoice to its related purchase order
- Validate the voice against static vendor contact documents
- Return a single structured output that includes discrepancy findings
You need to configure Pipeline1 and expose the pipeline as a single analyzer endpoint. What should you configure?

A) a multiple-file task in pro mode that uses the vendor contract files as reference data
B) a single-file task in standard mode that uses the vendor contract provided as an additional document during analysis.
C) a single-file task in standard mode that uses confidence scores enabled for the extracted fields.
D) a multi-file task in standard mode that uses the invoice and purchase order as input to the analyzer

Solutions: