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| Section | Objectives |
|---|---|
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Text analytics and summarization - Translation and multilingual support |
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
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NEW QUESTION # 120
You have an Azure subscription
You need to create a new resource that will generate fictional stories in response to user prompts. The solution must ensure that the resource uses a customer-managed key to protect data.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The solution requires a generative text model, so the Cognitive Services account must be created with the OpenAI kind from the values supplied in the exhibit.
The JSON object shown underneath contains Key Vault-based encryption settings such as the key source, key vault URI, key name, and key version. That JSON therefore belongs to the --encryption parameter.
Customer-managed key encryption lets the organization control the key used to protect supported stored data.
This provides additional control over rotation, revocation, key governance, and separation of duties compared with relying solely on Microsoft-managed keys.
The Azure AI resource also needs permission to use the Key Vault key. In an operational deployment, this generally involves configuring a managed identity and granting the required Key Vault permissions.
--api-properties configures service-specific API properties rather than encryption. --assign-identity enables the account identity but does not itself supply the customer-managed encryption configuration shown in the question.
Therefore, the correct selections are OpenAI and --encryption .
Study Guide alignment: design Azure infrastructure, configure model resources, and secure AI systems through identity and resource-security controls . Microsoft Learn
NEW QUESTION # 121
You have a configurable guardrail in a Microsoft Foundry project.
You have a Python application. A text blocklist named Conf identialTerns already contains terms that must be detected in user messages. The application stores each incoming user message in a variable named inpot_text.
You plan to moderate input_text before the text is sent to a language model.
You need to analyze input_text by using the existing blocklist.
Answer:
Explanation:
Explanation:
AnalyzeTextOptions
request = AnalyzeTextOptions(
text=input_text,
blocklist_names=[blocklist_name],
halt_on_blocklist_hit=True
)
response = client.analyze_text(request)
AnalyzeTextOptions is the request model used by the Azure AI Content Safety Python SDK to submit text for moderation. Its text property contains the user message, while blocklist_names identifies one or more existing custom blocklists that must be applied during analysis. The value must be supplied as a list, so the correct assignment is [blocklist_name]. The request is then passed to ContentSafetyClient.analyze_text(), which returns an AnalyzeTextResult containing any blocklist matches and applicable harm-category analysis.
Setting halt_on_blocklist_hit=True stops additional harm-category processing when a blocklisted term is detected. This is appropriate when a blocklist match alone is sufficient to prevent the message from reaching the language model.
AddOrUpdateTextBlocklistItemsOptions manages terms within a blocklist; it does not analyze input.
AnalyzeImageOptions is intended for image moderation, and TextBlocklist represents blocklist metadata rather than a text-analysis request.
Study Guide alignment: configure safety filters, guardrails, blocklists, risk detection, and content moderation for Azure AI applications.
NEW QUESTION # 122
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:
Explanation:
Metrics to enable: Model Availability Rate and Provisioned Utilization
Diagnostic log to collect: trace
The correct metrics are Model Availability Rate and Provisioned Utilization . Model Availability Rate identifies whether failures are caused by service-side model unavailability because it is calculated from total calls minus server errors. Provisioned Utilization identifies resource-limit conditions because Microsoft states that when utilization reaches or exceeds 100%, calls are throttled and HTTP 429 errors are returned. This directly maps to the reported peak-hour rate-limit errors.
The correct diagnostic log is trace . For an internal Q & A scenario, trace logging is required to analyze question-answering behavior, including unanswered questions. Microsoft's custom question answering analytics guidance states that diagnostic logging stores telemetry and chat logs and instructs enabling Trace in addition to Audit, RequestResponse, and AllMetrics. The sample Kusto queries for unanswered questions inspect the answer, question, score, and knowledge base ID, and filter unanswered results by a score of zero.
RequestResponse is useful for request status and latency, and Audit is for administrative operations, but neither is the best diagnostic category for analyzing Q & A inference behavior such as "No relevant information found." Reference topics: Foundry monitoring, model availability, provisioned utilization, diagnostic logging, custom question answering analytics, and trace logs.
NEW QUESTION # 123
Hotspot Question
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 use 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:
NEW QUESTION # 124
You are creating an image-editing workflow in a Microsoft Foundry project.
The workflow must meet the following requirements:
- Ensure that background objects can be removed by applying a mask-
based inpainting edit.
- Preserve the original lighting and style of the edited images.
- Use the built-in image editing controls, NOT a custom model.
You need to ensure that image edits apply exclusively inside the masked area.
How should you configure the workflow?
Answer: B
NEW QUESTION # 125
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