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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 2: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Topic 3: Implement agentic solutions | 20-25% | - Build AI agents
|
| Topic 4: Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Topic 5: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
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NEW QUESTION # 82
Drag and Drop Question
You have a Microsoft Foundry project that contains an agent. The agent uses threads and file uploads and calls an Azure OpenAI model deployment.
During load testing, calls intermittently fall and return an HTTP 429 rate limit exceeded error.
Some user uploads fail and generate an HTTP 400 file size exceeded error.
You need to mitigate the errors and reduce call failures. The solution must remain within the service and model limits.
What should you do to resolve each error? To answer, drag the appropriate actions to the correct errors. Each action may be used once, more than once or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each comet selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Implement exponential backoff and jitter in the retry logic
To remedy intermittent HTTP 429 rate limit errors while staying within service limits, you must implement a retry policy that uses exponential backoff and jitter.
Because the agent uses threads and file uploads, load testing triggers short-window concurrency spikes (bursting). Azure OpenAI evaluates rate limits in small slices (like 1- to 10-second windows), meaning overlapping file processing or concurrent thread steps will trigger a 429 even if your total Tokens-Per-Minute (TPM) or Requests-Per-Minute (RPM) look safe overall.
Box 2: Split content into smaller files before uploading the files.
Instead of uploading a large document in a single standard files.create request, utilize the Chunked Uploads REST API path. This splits the file payload on your client application layer before streaming it to Azure.
Note: To resolve the HTTP 400 file size exceeded error in your Microsoft Foundry agent, you need to bypass the strict payload constraints of standard single-request uploads. In Azure OpenAI and Microsoft Foundry Agent architectures, a multipart standard upload has a rigid request body limit (typically 30 MB or 50 MB.
Reference:
https://learn.microsoft.com/en-us/answers/questions/2265002/getting-error-after-deployed-a-model-in-azure-ai-f
https://learn.microsoft.com/en-us/answers/questions/5521436/getting-400-error(request-body-too-large)-for-batc
NEW QUESTION # 83
You are building an image sharing app that will use Azure AI to prevent users from sharing sexually explicit images.
You need to ensure that inappropriate images are identified correctly. The solution must minimize development effort.
What should you use?
Answer: C
Explanation:
To prevent users from sharing sexually explicit images, you need a tool that can handle content moderation specifically designed for inappropriate content such as adult or sexually explicit material. Azure AI Content Safety Studio is a service designed for this purpose, providing pre- built AI models that can detect inappropriate content like adult images, violence, and other sensitive material. It minimizes development effort by offering an easy-to-use, pre-configured service for content safety without the need to build custom models.
NEW QUESTION # 84
A customer support agent must answer from an internal knowledge base that is updated weekly with new product documentation, and its answers must always reflect the latest content. Which approach should you implement?
Answer: B
Explanation:
RAG injects fresh, frequently changing knowledge at inference time by retrieving relevant content from an index and grounding the model's answer in it, which is exactly the "chat with your data" pattern this scenario describes. Microsoft's guidance is direct on the boundary between the two techniques.
Use fine-tuning when you need to change model behavior, style, or task performance, rather than add fresh knowledge.
NEW QUESTION # 85
Hotspot Question
You plan to create a Microsoft Foundry project named Project1 that will contain an agent and use an Azure key vault named KV1.
You need to configure a connection from Project1 to KV1.
How should you complete the Bicep code? To answer, select the appropriate options in the answer area?
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: AzureKeyVault
To configure a connection from a Microsoft Foundry project to an Azure Key Vault, you must use the AzureKeyVault connection category.
Box 2: AccountManagedIdentity
To connect a Microsoft Foundry project to an Azure Key Vault, you must use AccountManagedIdentity as the authType.
Microsoft Foundry secures its outbound connections to core Azure storage and security infrastructure using Microsoft Entra ID role-based access control (RBAC). Using AccountManagedIdentity completely eliminates the need to handle or rotate explicit credentials or api keys within your configuration.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/set-up-key-vault-connection
NEW QUESTION # 86
You have a Microsoft Foundry project that contains a customer support agent built by using the Foundry Agent Service.
The agent uploads user-provided screenshots to Azure Storage through a ticketing tool and receives a blob URL for additional reasoning.
You need to use image moderation during agent runs and prevent harmful content from being returned during runs. Azure Al Content Safety must access the images by using the blob URL. The solution must follow the principle of least privilege.
What should you configure for Content Safety? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Guardrails: Select User input, Output, Tool response, and Tool call and set Action to Block.
Storage access: A system-assigned managed identity that is assigned the Storage Blob Data Contributor role The guardrail must be applied to User input, Output, Tool response, and Tool call with the action set to Block . Microsoft Foundry guardrails support four intervention points: user input, tool call, tool response, and output. This scenario includes user-provided screenshots, a ticketing tool that uploads images and returns blob URLs, and final agent responses. Applying blocking controls at all four points ensures harmful image-related content is inspected throughout the agent run and prevented from continuing or being returned to the user.
Microsoft's guardrails guidance also states that tool call and tool response controls are specifically required when harmful content can pass through agent tools.
For storage, configure the Azure AI Content Safety resource with a system-assigned managed identity and grant it Storage Blob Data Contributor on the storage account or container. The Content Safety image moderation quickstart states that images can be supplied by blob storage URL and that the Content Safety resource must be given storage access by enabling its system-assigned managed identity and assigning Storage Blob Data Contributor or Owner; Contributor is the least-privileged valid option shown. Reference topics: Foundry guardrails, agent intervention points, image moderation, managed identity, and Azure Storage RBAC.
NEW QUESTION # 87
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