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| Section | Objectives |
|---|---|
| Topic 1: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 2: Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Model selection and lifecycle management - Responsible AI principles and governance |
| Topic 3: Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Topic 4: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 5: Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
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NEW QUESTION # 32
You have a customer support agent built by using the Microsoft Foundry Agent Service. The agent calls an Azure OpenAl model deployment.
During load testing, calls intermittently fail and return an HTTP 429 rate limit exceeded error.
You need to handle throttling to reduce call failures and improve reliability under load. The solution must remain within the service and model limits.
What should you do?
Answer: B
Explanation:
The correct answer is A. Implement a retry policy that uses exponential backoff and jitter . HTTP 429 indicates that the request rate or token rate has exceeded the configured service limits for the model deployment. Microsoft Foundry Agent Service limits guidance specifically recommends implementing exponential backoff with jitter in application retry logic when agents receive rate-limit 429 errors. It also recommends reviewing Azure OpenAI quotas and token-per-minute and request-per-minute limits for the deployment.
This approach improves reliability while remaining within service limits because retries are delayed progressively instead of immediately adding more pressure to an already throttled deployment. Microsoft Foundry Models quota guidance also states that unsuccessful requests still count toward per-minute rate limits and that continuously resending requests without backing off makes throttling worse. It recommends retry logic with exponential backoff and using the Retry-After header when available.
Creating a new thread and retrying immediately does not change the deployment's rate limits and can worsen throttling. Reducing registered tools may simplify orchestration but does not directly address model RPM or TPM limits. Splitting uploaded content into smaller files may help ingestion scenarios, but it is not the correct throttling control for intermittent HTTP 429 model calls. Reference topics: Foundry Agent Service limits, Azure OpenAI quota management, throttling, retry policies, and production reliability.
NEW QUESTION # 33
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 # 34
You have a Microsoft Foundry project that contains a deployed chat model.
You have a Python service that sends API requests to the model. The service is integrated with an automated validation system that compares generated outputs against approved response patterns.
Stakeholders report that small wording differences are causing validation mismatches.
You need to update the request parameters to improve output stability. The solution must maximize reasoning quality.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
temperature = 0
output_config = { " effort " : " high " }
The correct configuration is temperature = 0 and output_config = { " effort " : " high " }. The requirement is to reduce small wording variations that are breaking automated validation. In chat completion requests, temperature controls sampling randomness. Microsoft's Azure OpenAI reference states that temperature ranges between 0 and 2, and that higher values make output more random while lower values make output more focused and deterministic. Therefore, the most stable setting from the available choices is 0, because it minimizes randomness and improves repeatability for validation-sensitive response patterns.
The solution must also maximize reasoning quality. The code already enables thinking with thinking={ " type
" : " enabled " }, so the remaining reasoning-quality control is the effort setting. Microsoft Foundry model guidance states that the effort parameter controls the quality/cost tradeoff and supports low, medium, and high effort levels. Selecting " high " maximizes reasoning quality among the available options.
Using temperature values of 1 or 2 would increase variability and make validation mismatches more likely.
Selecting low or medium effort would not meet the requirement to maximize reasoning quality. Reference topics: Microsoft Foundry model inference, chat model parameters, temperature, thinking, effort, and output stability.
NEW QUESTION # 35
You have an app named App1 that uses a Microsoft Foundry multimodal model deployment.
App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context.
Some uploaded images contain embedded text.
You need to prevent potentially malicious instructions from being processed by the model.
What should you use?
Answer: C
Explanation:
The correct answer is D. prompt shields for documents . The OCR text is extracted from uploaded images and appended as additional context, so it is third-party content rather than a direct trusted user instruction.
Microsoft Foundry Prompt Shields distinguish between user prompt attacks , which are malicious instructions directly supplied as user prompts, and document attacks , which are hidden or embedded instructions in third-party content such as documents, emails, webpages, or grounded data. Microsoft also notes that Prompt Shields analyze indirect attacks embedded in input documents or images.
This scenario is an indirect prompt injection pattern: an uploaded screenshot may contain text that says to ignore the system prompt or perform unauthorized actions. Because the OCR output is being passed to the multimodal model as contextual content, the appropriate protection is Prompt Shields for documents, which scans externally sourced context for malicious embedded instructions before generation. Protected material text detects known copyrighted text in model outputs, not prompt injection. Image moderation detects harmful visual content categories, not malicious instructions in extracted context. Prompt Shields for user prompts are less precise here because the risk comes from document-like content derived from the uploaded image.
Reference topics: Prompt Shields, document attacks, indirect prompt injection, multimodal safety, OCR- derived context, and Foundry guardrails.
NEW QUESTION # 36
You have 10,000 ASCII files and need to identify files containing specific phrases by using cosine similarity.
Answer: A
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
text-embedding-ada-002 is an embedding model . Embedding models transform text into dense numerical vectors representing semantic characteristics of the input.
The application can generate an embedding for each relevant file or text chunk and store those vectors. When a user supplies a phrase, the application generates an embedding for that phrase by using the same embedding model. It can then calculate cosine similarity between the query vector and stored vectors.
Cosine similarity measures the angular similarity between vectors. Values representing semantically similar text will generally be closer even when the exact wording differs. This makes embeddings appropriate for semantic file retrieval, clustering, similarity search, and RAG.
GPT-4, GPT-35 Turbo, and GPT-4-32k are generative language models rather than dedicated vector- embedding models. They can reason about text but are not the appropriate options for producing the vector representations required by the question.
Although newer embedding-model generations exist in current Azure model catalogs, among the choices presented, text-embedding-ada-002 is the correct model family and answer.
Study Guide alignment: Build retrieval and grounding pipelines, configure vector search, and generate representations used for semantic retrieval . Microsoft Learn
NEW QUESTION # 37
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