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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. Hotspot Question
You are creating an enrichment pipeline that will use Azure AI Search. The knowledge store contains unstructured JSON data and the text from scanned PDF documents.
Which projection type should you use for each data type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

2. 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) Fluency
C) Groundedness Pro
D) Relevance

3. A team wants to deploy a model from the Foundry catalogue with the fastest possible setup, pay only for the tokens they consume, and avoid provisioning any virtual machine (VM) quota. Which deployment option fits?

A) Managed compute (managed endpoint) deployment
B) Serverless API deployment, also called models as a service (MaaS)
C) A local container running on a developer workstation
D) Self-hosted deployment on Azure Kubernetes Service

4. A multimodal agent processes user-uploaded images. An attacker embeds hidden text instructions inside an image, hoping the agent will read and act on them. Which Azure AI Content Safety capability is designed to detect this kind of attack?

A) Raising the image resolution threshold on the analyser
B) Disabling the agent's file search tool
C) Prompt Shields, which detects document and indirect prompt injection
D) A content filter set to High severity for the Violence category

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

Solutions: