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| Section | Objectives |
|---|---|
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Azure AI resource provisioning and configuration - Model selection and lifecycle management |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
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NEW QUESTION # 28
You have a Microsoft Foundry project that contains an agent named Agent1.
Agent runs successful, but Foundry Control Plane does NOT display values for error rates, runs, and token usage, and the Traces tab is empty.
You need to ensure that Found Control Plane displays the appropriate values for Agent1.
What should you do?
Answer: A
Explanation:
To resolve this issue, you must connect and configure an Azure Application Insights resource for your Microsoft Foundry project.
The Foundry Control Plane, its Agent Monitoring Dashboard, and the Traces tab rely directly on telemetry data stored within the connected Application Insights instance. If this resource is missing, unlinked, or improperly configured, the dashboard cannot display runs, error rates, token usage, or transaction spans.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/monitoring--observability-in-microsoft-foundry/4517250
NEW QUESTION # 29
You have a factory that contains a production line.
You need to develop a solution that will analyze a video feed to detect and track the movement of employees as they work on the production line.
Which service should you include in the solution?
Answer: B
Explanation:
Azure Vision in Foundry Tools (Spatial Analysis) can be used to analyze a video feed to detect and track the movement of employees as they work on the production line. This technology processes live or recorded video to understand spatial relationships and human activity.
Key capabilities for this application include:
Person Tracking & Movement: Spatial Analysis models identify human presence by detecting a body bounding box and tracking it in real time, enabling monitoring of movement patterns throughout a facility.
Zone & Line Monitoring: You can configure the system to detect when a person enters or exits a designated zone (e.g., a specific workstation) or crosses a line.
Dwell Time Calculation: The system can measure how long an employee spends in a specific zone, which can be useful for identifying bottlenecks.
Safety and Compliance: It can monitor for safety infractions, such as workers entering prohibited areas or failing to wear protective equipment (PPE).
Efficiency Analysis: By integrating these insights into Palantir Foundry's ontology, manufacturers can track assembly processes, optimize workflows, and enhance productivity.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/whats-new
NEW QUESTION # 30
You need to configure the model deployment for Agent1 to meet the technical requirements.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Deployment type: Standard
Version update policy: Once the current version expires
The correct deployment type is Standard . The case study specifies that Project1 is deployed in an EU Azure region and that model-processed data must remain within the EU. It also requires scalable, high-throughput generative AI workloads that dynamically handle variable customer support traffic without reserved throughput capacity. In Microsoft Foundry Models, Standard is a pay-per-token deployment type that processes data in a single Azure region, while Global Standard can process requests across regions and Global Provisioned uses reserved provisioned throughput. Microsoft's deployment-type guidance identifies Standard as single-region, pay-per-token, whereas Global Provisioned is cross-region with reserved capacity.
The correct version update policy is Once the current version expires . This keeps Agent1 on the selected model version during its supported lifecycle, which supports stable and consistent responses, but still preserves continuity by automatically moving to a supported replacement when the current version is retired.
Microsoft's model versioning guidance states that this policy updates only when the current model version expires, while upgrading when a new default is available changes the deployment sooner and opting out can cause the deployment to stop working after retirement. Reference topics: deployment types, regional data processing, model versioning, throughput capacity, and stable production deployments.
NEW QUESTION # 31
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 multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure protected material detection.
Does this meet the goal?
Answer: A
Explanation:
Correct:
* You configure a prompt shield for documents.
Prompt Shield for Documents: Highly Effective (Critical Defense)
How it helps: This shield specifically scans untrusted, third-party data inputs (like external documents or text extracted from uploaded images).
Mechanism: It evaluates the extracted image text before it is sent to the LLM to identify hidden jail
* You configure a prompt shield for user prompts.
Prompt Shield for User Prompts: Partially Effective (Defense in Depth)
How it helps: This shield targets direct jailbreak attempts written manually by the user in the text prompt field accompanying the upload.
Mechanism: It prevents the user from typing supporting instructions that prime the model to execute the hidden instructions found within the image.
* You configure image moderation to block unsafe content before processing the images.
Implementing rigorous image moderation is one of the most effective ways to secure multimodal AI systems against these threats. Moderation acts as a necessary gatekeeper, preventing malicious inputs from ever reaching the generative model.
Incorrect:
* You configure protected material detection.
Protected Material Detection: Ineffective for this Threat
Why it does not help: This feature is designed to scan model outputs to prevent the generation of copyrighted text, proprietary source code, or licensed imagery.
Limitation: It does not scan inputs for adversarial instructions and will not prevent a user from manipulating the model's logic.
Reference:
https://www.upgrad.com/blog/what-is-multimodal-ai/
https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/jailbreak-detection
NEW QUESTION # 32
You are planning a Microsoft Foundry project named Project1 that will contain multiple agents. Each agent will access the same Azure Al Search resource.
You need to recommend a solution to centrally manage the Azure Al Search credentials within Project1. The solution must be implemented across all the agents.
What should you recommend?
Answer: B
NEW QUESTION # 33
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