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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 2: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 3: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 4: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Topic 5: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
Question 1
You plan to configure an evaluation in Microsoft Foundry for a Retrieval Augmented Generation (RAG) chat app.
You need to provide scores for groundedness, relevance, and harmful content categories.
Which two evaluation categories can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. risk and safety metrics
B. fluency evaluator
C. similarity evaluators
D. AI quality (AI assisted) metrics
E. AI quality (NLP) metrics
Question 2
You have a Microsoft Foundry project that contains an agent.
The agent uses Azure AI Search as the retriever.
You plan to ingest PDF into an Azure AI Search index to ensure that the agent can ground responses in texts in both documents and embedded images.
Users require citations that link to the source files.
You need to ensure that during indexing, the images are extracted into a structure that can be used as input for the built-in optical character recognition (OCR) skill.
Which indexing approach should you use?
A. the outputFieldMappings parameter to write image data to a searchable field
B. a Shaper skill to restructure the OCR input
C. a skillset to run the OCR skill directly against the content field of the index
D. an indexer to extract image data into a normalized_images collection
Question 3
A legal team must extract clauses and inferred fields from unstructured contracts. One required field is the contract end date, which is not stated explicitly and must be derived from the start date plus the term. The team has no labelled training data. Which tool best fits?
A. Azure Translator in Foundry Tools
B. An Azure AI Document Intelligence custom model trained on labelled samples
C. The Azure AI Search semantic ranker
D. Azure Content Understanding using a prebuilt-contract or zero-shot custom analyser
Question 4
You have a custom agent named Agent1.
You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
What should you do first?
A. Provision an Application Insights resource.
B. Add Agent1 to the Microsoft Foundry playground.
C. Add Agent1to a Microsoft Foundry project.
D. Create a Microsoft Foundry project.
Question 5
You have a Microsoft Foundry project that contains a customer support agent built on a deployed chat model.
The agent responses are validated by using an automated testing system that compares generated answers to stored expected outputs. Identical prompts must return consistent response to prevent automated test failures.
You need to reduce response variability, without modifying the prompt or reducing factual accuracy.
What should you do for the model?
A. Increase the temperature parameter.
B. Decrease the temperature parameter.
C. Increase the max_tokens parameter.
D. Remove stop sequences from the requests.
Solutions:
| Question 1 Answer: A,D | Question 2 Answer: D | Question 3 Answer: D | Question 4 Answer: D | Question 5 Answer: B |
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