AI-103 Exam Preparation Material | Developing AI Apps and Agents on Azure

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Prepare for the AI-103 Developing AI Apps and Agents on Azure exam with CertQueen's independently developed study resources. Review important concepts, practice scenario-based questions, and use clear explanations to identify areas that require further study.

Question#1

You are building a speech processing solution in Microsoft Foundry for a customer support platform.
The platform will transcribe live phone calls, so that supervisors at your company can view call transcripts and detect issues while the calls are in progress. The call audio will arrive as a continuous stream from the telephony system. You need to ensure that the call transcripts appear within only a few seconds of the audio stream.
What should you do?

A. Use text to speech by using a custom neural voice.

B. Use speech translation to generate the transcripts into multiple languages.

C. Run a batch transcription job on recorded audio files.

D. Use real-time speech to text to process streaming audio input.

Question#2

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. indexer status and failure history

B. latency breakdown traces

C. prediction drift metrics

D. groundedness evaluation metrics

Question#3

HOTSPOT -
You have a Microsoft Foundry project that contains an agent.
You use a GitHub Actions workflow for CI/CD.
You need to configure the workflow to automatically evaluate the agent when a pull request (PR) is created and prevent branches from merging if the evaluation results do NOT meet the defined thresholds.
How should you configure the workflow? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Question#4

You need to recommend a solution to support the planned changes and technical requirements for Agent1 to use the product information stored in storage1.
What should you include in the recommendation?

A. Azure Al Search

B. Azure Translator in Foundry Tools

C. Azure Document Intelligence in Foundry Tools

D. Grounding with Bing Search

Explanation:
The correct recommendation is Azure AI Search. The case study states that the product detail sheets are stored as PDFs in storage1, and that Agent1 must be enabled to retrieve and use detailed product information from those sheets. It also specifies that the indexing pipeline must enable semantic and vector search, and that Agent1 must answer natural language questions about product details by using the product sheet information. Azure AI Search is the Azure service designed to ingest content from sources such as Azure Blob Storage, create searchable indexes, and support keyword, semantic, hybrid, and vector retrieval for Retrieval Augmented Generation (RAG) solutions.
Microsoft’s Azure AI Search guidance states that integrated vectorization can chunk content and generate embeddings during indexing, enabling vector search over source documents. It also states that Azure AI Search supports text and vector queries and can improve raw content for search-related scenarios through enrichment pipelines. Azure Translator is unrelated to retrieval. Document Intelligence can extract document structure, but it is not the retrieval index for Agent1. Grounding with Bing Search retrieves public web content, not Contoso’s private PDFs in storage1.
Reference topics: Azure AI Search, RAG, semantic search, vector search, Azure Blob Storage indexing, and agent grounding.

Question#5

You have a Microsoft Foundry project that contains a model deployment.
You have an application that calls the deployment by using the Azure OpenAI v1 API and DefaultAzureCredential.
The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login.
You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege.
Which role-based access control (RBAC) role should you assign to the developers?

A. Cognitive Services User

B. Cognitive Services OpenAI User

C. Contributor

D. Cognitive Services Data Reader

Exam Code: AI-103
Q & A: 187 Q&As Updated: Nov 12,2026

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What This AI-103 Study Resource Helps You Do

Review Key Concepts

Review the technologies, products, processes, and practical skills covered by the current AI-103 exam objectives.

Practice Scenario-Based Questions

Work through independently developed questions designed to strengthen your understanding of technical scenarios and decision-making.

Identify Knowledge Gaps

Use your results and the provided explanations to find weaker areas and focus your study more effectively.

How to Use This AI-103 Preparation Material

Review the Exam Scope

Start by reviewing the topics covered by the AI-103 exam. Compare them with the official exam objectives to understand the required technologies, operational tasks, and practical skills, then identify the areas that deserve the most attention.

Practice Independently

Complete a focused set of practice questions for each topic. On your first attempt, avoid referring to notes, answers, or other study resources so that you can evaluate your current understanding more accurately.

Study the Explanations

Review the answers and explanations after completing each practice session. Understand why the correct option is appropriate for the given scenario and why the other options may be incorrect or less suitable.

Close Knowledge Gaps

Keep track of incorrect answers, unfamiliar concepts, and weaker knowledge areas. Review these topics using official documentation and practical experience, then answer the related questions again to reinforce your understanding and monitor your progress.

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Exam Code: AI-103
Q & A: 187 Q&As
Updated: Nov 12,2026