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Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 2: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 3: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 4: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 5: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
1. You need to add an automated grounding check to a RAG application's continuous evaluation.
The check must return a simple pass or fail result and must not require you to deploy a separate judge model. Which evaluator should you use?
A) Groundedness, which returns a 1 to 5 score
B) Relevance
C) Groundedness Pro
D) Fluency
2. You are building a chatbot.
You need to ensure that the chatbot can classify user input into separate categories. The categories must be dynamic and defined at the time of inference.
Which service should you use to classify the input?
A) Azure OpenAI text summarization
B) Azure AI Language custom text classification
C) Azure OpenAI text classification
D) Azure AI Language custom named entity recognition (NER)
3. You have a Microsoft Foundry project that contains an agent and an image generation model deployment.
The agent generates original images from user-supplied product photos.
You need to ensure that the generated images maintain the product identity and visual characteristics of the provided photo.
What should you do?
A) Decrease the value of the temperature parameter.
B) Include a prompt and input image in the request.
C) Apply a groundedness detection filter.
D) Set the input_fidelity parameter to high.
4. You have an application that processes scanned PDF invoices. The invoices have varied layouts and include multipage tables.
You have a pipeline that uses optical character recognition (OCR) and extracts totals and invoice numbers. The results are often incorrect because the document structure is ignored.
You need to implement a solution that provides OCR, layout analysis, and template-generalizing field extraction. The solution must NOT require training a custom model. The solution must minimize administrative effort.
What should you include in the solution?
A) Azure Content Understanding in Foundry Tools
B) an Azure Machine Learning model
C) Azure Language in Foundry Tools
5. You have an Azure subscription that contains an Azure App Service app named App1.
You provision a Microsoft Foundry Service resource named CSAccount1.
You need to configure App1 to access CSAccount1. The solution must minimize administrative effort.
What should you use to configure App1?
A) the endpoint URI and an OAuth token
B) the endpoint URI and a shared access signature (SAS) token
C) the endpoint URI and subscription key
D) a system assigned managed identity and an X.509 certificate
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: C |








