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| Section | Weight | Objectives |
|---|---|---|
| Implement agentic solutions | 20-25% | - Build AI agents
|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
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NEW QUESTION # 117
You have an Azure Al Search resource named Search1 that is used by multiple apps hosted in Azure. You need to secure Search1. The solution must meet the following requirements:
* Prevent access to Search1 from the internet.
* Limit the access of each app to query specific indexes.
What should you do? To answer, select the appropriate options in the answer area. NOTE: Each correct answer is worth one point.
Answer:
Explanation:
Explanation:
* To prevent access from the internet: Create a private endpoint
* To limit access to query specific indexes: Use Azure roles
Create an Azure Private Link private endpoint for Search1 and disable public network access. The private endpoint assigns Search1 a private IP address within an Azure virtual network, allowing the hosted applications to reach the search service over the Microsoft backbone instead of through its public endpoint.
Disabling public network access ensures that requests originating from the public internet are rejected. An IP firewall would still expose the public endpoint to approved public IP addresses and therefore would not provide complete internet isolation.
For index-level authorization, enable Microsoft Entra ID role-based access control and assign Azure roles to each application's managed identity or service principal. The Search Index Data Reader role permits query and retrieval operations without allowing index modification. Azure AI Search supports scoping Search Index Data Reader or Search Index Data Contributor permissions to an individual index; custom role definitions can also be used when more precise permissions are required.
Query-key authentication is unsuitable because API keys normally provide service-level access and cannot reliably isolate applications to designated indexes.
Study Guide alignment: secure Azure AI services by configuring private endpoints, network isolation, managed identities, Microsoft Entra authentication, and role-based access control.
NEW QUESTION # 118
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You increase the value of the max_tokens parameter.
Does this meet the goal?
Answer: B
Explanation:
The solution does not meet the goal. Increasing max_tokens only raises the maximum number of tokens the model is allowed to generate. Microsoft's Azure OpenAI reference defines max_tokens as the maximum number of tokens allowed for the generated answer, and the quota guidance notes that increasing it can help when responses are being truncated.
In this scenario, the problem is not described as output truncation. The required regulatory clauses are already present in the retrieved policy documents, but the agent omits them during summarization. That is a response completeness issue: Microsoft Foundry RAG evaluator guidance defines response completeness as the recall aspect of the response, meaning the response should not miss critical information compared with expected content or ground truth.
A larger token budget might permit a longer answer, but it does not force the model to identify, verify, or include each mandatory clause. It can also increase cost and latency. The appropriate control is a reflection or completeness verification pass that checks the draft against the retrieved policy clauses and regenerates or revises the response when required content is missing. Reference topics: RAG response completeness, model output limits, max_tokens, reflection, and response validation.
NEW QUESTION # 119
You have a Microsoft Foundry project that contains two agents named PolicyWriter and RiskReviewer.
PolicyWriter generates draft updates for customer policies, and RiskReviewer reviews the drafts. In the visual builder, you need to create a workflow that meets the following requirements:
* Finalizes low-risk updates without manual intervention
* Ensures predictable execution across the agents
Answer:
Explanation:
Explanation:
* Orchestration pattern: The sequential template that passes outputs node-by-node
* Approval checkpoints: Add a condition statement
Use the sequential orchestration template because the agents must execute in a fixed, predictable order.
PolicyWriter first generates the proposed policy update, and its output is then passed directly to RiskReviewer. Microsoft defines the sequential pattern as passing the result from one agent to the next in a defined order, making it appropriate for deterministic, multistage processing. The group-chat pattern would allow control to move dynamically between agents, which would reduce execution predictability.
After RiskReviewer returns a structured risk classification, add a condition statement that evaluates the result. The low-risk branch can proceed directly to the finalization action without requesting human input. A separate higher-risk branch can route to an approval step, such as an Ask a question node. Adding an Ask a question node without conditional branching would pause every workflow execution, including low-risk updates, and therefore would not satisfy the automation requirement. Microsoft Foundry workflows support if
/else branching and condition expressions for selecting the next action.
Study Guide alignment: implement orchestrated multi-agent solutions and build autonomous or semiautonomous workflows with safeguards and approval-flow controls .
NEW QUESTION # 120
You successfully run an HTTP request to:
.../regenerateKey?...
with:
{ " keyName " : " Key2 " }
What is the result?
Answer: B
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The request invokes the Azure Cognitive Services/Azure AI resource regenerateKey management operation and explicitly specifies " keyName " : " Key2 " .
Azure AI service resources traditionally expose two interchangeable access keys: Key1 and Key2. Key1 is treated as the primary key and Key2 as the secondary key. Regenerating Key2 invalidates the previous Key2 value and produces a new value for that secondary credential.
Only the selected key is regenerated. Key1 remains unchanged, so option D is incorrect.
The request is unrelated to Azure AI Search query keys, which are separate read-only Search credentials. It also does not cause the newly generated key to be written automatically to Azure Key Vault. Such storage would require separate Key Vault configuration or deployment automation.
Maintaining two resource keys enables zero- or low-downtime key rotation: applications can temporarily use one key while the other is regenerated, then switch and rotate the remaining key.
The AI-103 Study Guide includes securing and managing Azure AI systems, including credentials, authentication, least privilege, and operational resource management. Microsoft Learn
NEW QUESTION # 121
You have a Microsoft Foundry project that generates product marketing images from text prompts.
After publishing several images, the legal team at your company identifies a competitor's logo on a sign in the background of an image.
You need to remove only the logo, while preserving the rest of the image.
What should you do?
Answer: D
Explanation:
To remove a logo from a generated image while preserving the rest of the content, you should use AI-powered inpainting tools or standard object removal features in professional image editing software.
Microsoft Designer or Azure AI (In-Engine Fixes)
If your Foundry project is built on top of Azure OpenAI Service or Dall-E, you can often handle this programmatically or through related first-party tools.
Generative Erase / Inpainting: Use Microsoft Designer's "Erase" tool. Brush over the logo, and the AI will replace it by seamlessly blending the background.
API Inpainting: If you have access to the underlying image generation API (like DALL-E 3 or Stable Diffusion), use the Inpainting API. Pass the original image along with a black-and-white mask highlighting the logo, and prompt it to "fill the background naturally." Reference:
https://starryai.com/en/blog/mai-image-1
NEW QUESTION # 122
......
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