Prepare with Microsoft AI-103 exam torrent, pass for sure
Updated: Nov 08, 2026
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Microsoft AI-103 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI Apps and Agents on Azure |
| Exam Number: | AI-103 |
| Passing Score: | 700 / 1000 |
| Exam Duration: | 100 minutes |
| Real Exam Qty: | 50–60 |
| Exam Format: | Case study, Multiple choice, Performance-based items, Scenario-based |
| Exam Price: | $165 USD (varies by country/region) |
| Certificate Validity Period: | 1 year (renewable annually via free online assessment) |
| Available Languages: | English, German, Portuguese (Brazil), Chinese (Simplified), Spanish, Japanese, Korean, French |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate (retiring June 30, 2026) |
| Recommended Training: | Microsoft Learn: Develop AI Apps and Agents on Azure Azure AI Foundry Documentation |
| Exam Registration: | Pearson VUE Registration Microsoft Certification Dashboard |
| Sample Questions: | Microsoft AI-103 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended experience: 6+ months building AI applications, proficiency in Python or C#, familiarity with Azure services and AI concepts |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-103 |
Microsoft AI-103 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
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. an Azure Machine Learning model
- B. Azure Language in Foundry Tools
- C. Azure Content Understanding in Foundry Tools
Correct Answer: C 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
You need to ensure that the marketing department can generate videos by using the model deployed to Project2. now should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Call client.videos.create() to submit the prompt and start a video-generation job against the model deployment. Video generation is asynchronous: the initial response represents a job and contains properties such as its unique ID and current status rather than immediately returning the completed video. Microsoft's official Python example creates the job by using client.videos.create(model=..., prompt=...).
The application must then poll the job. After waiting between requests, client.videos.retrieve(video.id) obtains the latest state of that specific job. Polling continues until the status becomes completed, failed, or cancelled.
list would enumerate jobs rather than refresh the selected job, while download_content is used only after successful completion to retrieve the generated video file.
To comply with Contoso's prohibition on API keys, the client should use a Microsoft Entra bearer-token provider. The OpenAI Python client accepts this provider through its api_key constructor parameter even though no static API key is used.
Study Guide alignment: design and implement video-generation solutions and integrate generative workflows through Foundry SDKs.
You have a Microsoft Foundry project that contains an internal Q & A agent.
Users report the following issues when they ask the agent questions:
* An increase in the following response: "No relevant information found"
* Periodic HTTP 429 rate limit exceeded errors during peak hours
You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Metrics to enable: Model Availability Rate and Provisioned Utilization
Diagnostic log to collect: trace
The correct metrics are Model Availability Rate and Provisioned Utilization . Model Availability Rate identifies whether failures are caused by service-side model unavailability because it is calculated from total calls minus server errors. Provisioned Utilization identifies resource-limit conditions because Microsoft states that when utilization reaches or exceeds 100%, calls are throttled and HTTP 429 errors are returned. This directly maps to the reported peak-hour rate-limit errors.
The correct diagnostic log is trace . For an internal Q & A scenario, trace logging is required to analyze question-answering behavior, including unanswered questions. Microsoft's custom question answering analytics guidance states that diagnostic logging stores telemetry and chat logs and instructs enabling Trace in addition to Audit, RequestResponse, and AllMetrics. The sample Kusto queries for unanswered questions inspect the answer, question, score, and knowledge base ID, and filter unanswered results by a score of zero.
RequestResponse is useful for request status and latency, and Audit is for administrative operations, but neither is the best diagnostic category for analyzing Q & A inference behavior such as "No relevant information found." Reference topics: Foundry monitoring, model availability, provisioned utilization, diagnostic logging, custom question answering analytics, and trace logs.
You need to configure an indexing pipeline for Agent1 to retrieve the relevant product information in storage1. The solution must meet the technical requirement.
Which two built-in skills should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Text Split
- B. Merge
- C. Entity Recognition
- D. Language Detection
- E. Azure OpenAl Embedding
- F. key phrase extraction
Correct Answer: A,E 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
You need to configure Agent1 to answer customer questions about only the Contoso products. The solution must meet the business requirements.
What should you do?
- A. Apply top-p sampling.
- B. Modify the system message instructions.
- C. Add few-shot examples.
- D. Increase the value of the temperature parameter.
Correct Answer: B 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
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