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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 2: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 3: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 4: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Topic 5: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
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NEW QUESTION # 64
You have a web app named App1 that sends requests to a multimodal chat model deployment in a Microsoft Foundry project.
User messages can contain both text and images.
Currently, App1 includes image URL: as plain text inside the message content so the model cannot recognize them as images.
Traces show that the requests contain a single text message instead of a multimodal content array.
You need to send the message as a structured array that includes both the text portion and the image reference to ensure that the model can process the image correctly.
What should you do?
Answer: C
Explanation:
To fix this, you must change your request payload structure from a single string to a structured content array. Multimodal models in Azure AI Foundry expect an array of distinct objects for text and images.
Reference:
https://towardsdatascience.com/multimodal-ai-search-for-business-applications-65356d011009/
NEW QUESTION # 65
You have a Microsoft Foundry project that contains an agent.
The agent uses tools to retrieve internal content and call external APIs. The agent is configured to let the model decide when to call the tools.
You need to publish the agent for a compliance workflow. The solution must meet the following requirements:
* Each workflow run must include a retrieval step before generating a response.
* Tool calls must authenticate by using the published agent's own identity.
* Tool access must use an identity isolated from other project resources.
* Tool access must support audit tracing.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Set tool_choice to: required
Configure the tool to authenticate by: Using a distinct agent identity bound to the client application Set tool_choice to required because the compliance workflow must deterministically include a tool-based retrieval step before the agent generates a response. Microsoft Foundry Agent Service guidance states that tool_choice provides the most deterministic control over tool use: auto lets the model decide, none prevents tool calls, and required forces the model to call one or more tools. This directly corrects the current nondeterministic behavior where the model decides whether to call tools.
For authentication, use a distinct agent identity bound to the client application . Microsoft Foundry creates a shared identity for unpublished or in-development agents, but publishing an agent automatically creates a dedicated agent identity blueprint and agent identity associated with the agent application resource. Published agents authenticate tool calls by using that unique agent identity, and RBAC permissions must be assigned to the new identity. This provides isolation from the broader shared project identity and supports independent audit trails for compliance workflows.
Storing API keys in prompts violates security guidance and prevents robust audit attribution. The shared project agent identity is easier for development, but it has a broader blast radius and does not meet the isolation requirement. Reference topics: Foundry Agent Service tool choice, tool authentication, published agent identities, RBAC, and auditability.
NEW QUESTION # 66
Hotspot Question
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.
Answer:
Explanation:
NEW QUESTION # 67
You have a Docker host named Host! that contains a container base image.
You have an Azure subscription that contains a custom speech-to-text model named model1.
You need to run model 1 on Host1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
* Request approval to run the container.
* Export model1 to Host1.
* Run the container.
Approval is the first step because custom Speech container scenarios that operate with locally stored models, particularly disconnected deployments, are gated. Microsoft requires an access request to be submitted and approved before the applicable Speech container can be used in a disconnected environment. The Azure Speech resources used for the container must be associated with the approved subscription.
After approval, export or download the trained custom speech-to-text model to Host1. The model files must be available through a Docker volume mounted to the container's model directory. The custom model determines the recognition locale and supplies the specialized acoustic or language behavior required by the container.
Finally, run the container and provide the model location or model identifier, volume mount, license and billing configuration, API key, and required compute allocation. Microsoft documents that the custom Speech container loads the model from the mounted volume and then exposes the speech-to-text service through its configured port.
Retraining is unnecessary because model1 already exists. Disk logging is optional operational configuration and is not required to deploy the model.
Study Guide alignment: implement custom speech solutions and deploy Azure AI services in containers .
NEW QUESTION # 68
You have an Azure subscription that contains an Azure OpenAI resource.
You need to build an app that will suggest product names from a given product description.
Which model should you use?
Answer: A
Explanation:
To suggest product names from a given product description, the ideal model is GPT-4. GPT-4 is a powerful language model that can understand and generate human-like text based on given inputs, making it well-suited for tasks such as generating creative suggestions like product names from descriptions.
Whisper is used for speech-to-text conversion, not for generating text suggestions.
DALL-E is designed for generating images from text prompts, not for text generation tasks like naming products.
Embeddings are used for tasks involving similarity search and semantic understanding but not for generating new text like product names.
NEW QUESTION # 69
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