Purchase Microsoft : AI-103 Exam Materials and then pass exam easily
Updated: Oct 16, 2026
No. of Questions: 159 Questions & Answers with Testing Engine
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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 |
| Real Exam Qty: | 50–60 |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate (retiring June 30, 2026) |
| Certificate Validity Period: | 1 year (renewable annually via free online assessment) |
| Passing Score: | 700 / 1000 |
| Exam Price: | $165 USD (varies by country/region) |
| Exam Duration: | 100 minutes |
| Available Languages: | Spanish, Korean, Portuguese (Brazil), English, German, Chinese (Simplified), French, Japanese |
| Exam Format: | Multiple choice, Scenario-based, Case study, Performance-based items |
| 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 information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
Microsoft Developing AI Apps and Agents on Azure Sample Questions:
You have an Azure subscription that contains an Azure OpenAI resource named AI1.
You build a chatbot that uses AI1 to provide generative answers to specific questions.
You need to ensure that questions intended to circumvent built-in safety features are blocked.
Which Azure AI Content Safety feature should you implement?
- A. Prompt Shields
- B. Moderate text content
- C. Protected material text detection
- D. Monitor online activity
Correct Answer: A 🗳️
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You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?
- A. Add a retry evaluation before the responses are returned.
- B. Replace the model with a smaller deployment.
- C. Decrease the value of the temperature parameter.
- D. Increase the value of the presence_penalty parameter
Correct Answer: A 🗳️
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You have an Azure Speech in Foundry Tools resource that hosts a custom speech to text model deployed to a custom endpoint. An agent uses the endpoint to perform real-time speech recognition.
You are approaching the expiration date of the custom speech to text model.
What is the expected behavior when the model expires?
- A. Speech recognition requests will continue to use the expired custom model until the model is removed manually.
- B. Speech recognition requests will return a 4xx error until a new custom model is deployed.
- C. The custom model will be deleted automatically when the model expires.
- D. Speech recognition requests will fall back to the most recent base model for the same locale.
Correct Answer: D 🗳️
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Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent uses a stored access key to retrieve secrets from an Azure key vault, which violates a keyless-credentials requirement.
You need to ensure that the agent can retrieve the secrets. The solution must follow the principle of least privilege.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: Enable a system-assigned managed identity at the project level
The correct configuration is to enable a system-assigned managed identity at the project level.
This is the correct approach. Azure AI Foundry (formerly Azure Machine Learning) allows you to enable a system-assigned managed identity directly at the project level. This identity acts as a keyless credential native to Azure. To follow the principle of least privilege, you then assign this project-level identity a specific role (such as Key Vault Secrets User) restricted only to the required Azure Key Vault. This ensures that the agent can retrieve secrets securely without exposing credentials to other projects or resources Box 2: Assign the Key Vault Secrets User role to the managed identity To comply with the keyless-credentials requirement and follow the principle of least privilege, you should configure the Key Vault authorization method to assign the Key Vault Secrets User role to the managed identity using Azure Role-Based Access Control (Azure RBAC).
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/ai-gateway
https://learn.microsoft.com/en-us/azure/app-service/app-service-key-vault-references
Hotspot Question
You have a Python application that redacts sensitive information before sending prompt text to a language model. The application has the following code:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: No
The audit list will not include entity records for "Contact" and "SSN" because neither word is a target PII entity type defined in the configuration.
The code limits scanning to Person and PhoneNumber via the piiCategories list.
"Contact" Definition: This is a regular English verb/noun, not a person's name or a telephone number.
"SSN" Definition: While "SSN" is a text label that points to sensitive data, it is a descriptor rather than an actual Social Security Number.
Box 2: Yes
For the given sample_text, text_for_model will include both or [email protected] and 859-
98-0987 completely unredacted.
The payload explicitly restricts the Personally Identifiable Information (PII) recognition to only two specific categories by setting piiCategories: ["Person", "PhoneNumber"].
Email Address Excluded: Because the Email category is omitted from the configuration list, the service bypasses the email address ([email protected]) and leaves it fully intact.
SSN Excluded: Similarly, because the USSSN (or equivalent Social Security Number) category is not specified in the piiCategories array, the SSN (859-98-0987) is completely ignored by the redaction policy and remains visible.
Box 3: Yes
text_for_model will contain entity type masks for "John Doe" and "312-555-1234", but only for those two specific items.
The piiCategories array explicitly requests Person and PhoneNumber.
Applied Redaction: The Azure AI Language service will replace "John Doe" and "312-555-1234" with masks like * or entity labels (depending on exact policy settings).
Skipped Data: The email ([email protected]) and the SSN (859-98-0987) will not be redacted because their categories (Email and USSSN) were omitted from your piiCategories list.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/personally-identifiable-information/concepts/conversations-entity-categories
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