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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

Question 1

You plan to build an agent that will combine and process multiple files uploaded by users.
You are evaluating whether to use the Azure AI Agent Service to develop the agent.
What is the maximum size of each file that can be uploaded to the service?

A. 1 GB
B. 512 MB
C. 256 MB
D. 10 MB

Question 2

You have a Microsoft Foundry project that contains an agent.
The knowledge source for the agent is a set of scanned PDF troubleshooting guides stored in Azure Blob Storage. The guide pages contain two-column layouts and tables.
You use Azure Content Understanding in Foundry Tools to process the PDFs.
You plan to ingest the processed content into an index for Retrieval Augmented Generation (RAG) and store extracted fields for downstream automation.
Stakeholders must be able to verify where each extracted field value came from in the original PDF and route low-reliability extractions for manual review.
You need to ensure that the Content Understanding document analyzer output includes a per- field confidence score and source grounding to locations within the source document.
What should you do?

A. Set enableSegment to true.
B. Enable estimateFieldSourceAndConfidence.
C. Configure the analyzer to use generative extraction for all fields.
D. Provide labeled samples.

Question 3

You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?

A. Add a retry evaluation before the responses are returned.
B. Switch to Retrieval Augmented Generation (RAG).
C. Replace the model with a smaller deployment.
D. Decrease the value of the max_tokens parameter.

Question 4

You have a Microsoft Foundry project that uses Azure AI Search to ground an agent in internal documentation.
After a recent content update, users report that the agent's answers have become less accurate.
You need to identify whether the retrieved content is negatively influencing the model's generated responses.
Which observability signal should you review?

A. groundedness evaluation metrics
B. indexer status and failure history
C. prediction drift metrics
D. latency breakdown traces

Question 5

You are building a voice agent for a pharmacy. It must accept spoken questions, reply with synthesised speech, and accurately recognise specialised medicine names that a general speech model often mishears. Which capability should you configure?

A. Azure AI Document Intelligence on the audio stream
B. A custom speech model for speech-to-text within the agent
C. Only the default text-to-speech voice
D. Azure Translator to convert the audio

Solutions: