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| Section | Weight | Objectives |
|---|---|---|
| Implement agentic solutions | 20-25% | - Build AI agents
|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
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NEW QUESTION # 141
You are building a text-to-speech solution that uses Azure Speech in Foundry Tools to read instructions from the script in a text file.
You discover that the solution often pronounces technical terms incorrectly.
You need to prevent the incorrect pronunciations. The solution must minimize development effort.
What should you do?
Answer: D
Explanation:
Using Speech Synthesis Markup Language (SSML) with the <phoneme> element is the ideal way to fix mispronunciations for technical terms.
The <phoneme> tag lets you override the default text-to-speech model by explicitly defining the sounds using the International Phonetic Alphabet (IPA).
Reference:
https://learn.microsoft.com/en-us/answers/questions/5729867/pronunciation-issue-when-generating-audio-from-ssm
NEW QUESTION # 142
You need to configure Agent1 to answer customer questions about only the Contoso products. The solution must meet the business requirements.
What should you do?
Answer: C
Explanation:
The correct answer is B. Modify the system message instructions . The case study states that Agent1 answers general questions about Contoso products and that a business requirement is for Agent1 to answer questions only about products sold by Contoso. This requirement defines the agent's allowed domain and refusal boundary, so it must be expressed in the agent's system-level instructions. Microsoft Foundry guidance states that system messages steer Azure OpenAI chat model behavior and are used to define the assistant's role, boundaries, output format, and safety or quality constraints.
The system message should instruct Agent1 to answer only Contoso-product questions, use Contoso product documentation when available, and decline questions about non-Contoso products. This directly enforces the intended business scope at the highest instruction level. Few-shot examples can reinforce desired behavior but are not the primary control for defining mandatory operating boundaries. Top-p sampling and temperature are decoding controls; they influence randomness and diversity, not whether the agent restricts answers to a specific product domain. Increasing temperature would likely reduce consistency. Reference topics: Microsoft Foundry agent instructions, system message design, prompt engineering, response boundaries, and grounded generative AI behavior.
NEW QUESTION # 143
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 # 144
You have a chat app in a Microsoft Foundry project and an Azure AI Search vectorized index.
You need to connect to the index to meet the following requirements:
* Complex questions must retrieve information from multiple chunks.
* Multi-turn conversations must influence retrieval planning.
* Retrievals must run in parallel to reduce latency.
Which retrieval approach should you use?
Answer: C
Explanation:
The correct answer is agentic Retrieval Augmented Generation (RAG) because the requirements describe the agentic retrieval pipeline in Azure AI Search. Agentic retrieval is designed for chat and copilot scenarios where a user's request can be complex, conversational, and dependent on prior turns. Azure AI Search agentic retrieval uses an LLM-assisted planning stage to break a complex request into focused subqueries, allowing the system to retrieve grounding information from multiple chunks rather than relying on a single query path.
Microsoft's Azure AI Search guidance describes agentic retrieval as a multi-query pipeline for complex questions in chat and agent workflows, with subqueries that can include chat history for additional context.
This also satisfies the latency requirement because agentic retrieval runs the generated subqueries in parallel and then merges and reranks the best results for use by the generative model. Classic RAG is simpler and typically sends a single query to search, making it less suitable for multi-hop or conversational retrieval planning. Chain of thought is a reasoning technique, not an Azure AI Search retrieval approach, and iterative retrieval does not specifically provide the built-in query planning, conversation-aware retrieval, and parallel execution described here. Reference topics: Azure AI Search agentic retrieval, RAG with Azure AI Search, knowledge bases, query planning, and generative AI grounding.
NEW QUESTION # 145
Drag and Drop Question
You have a web app that uses Azure AI Search.
When reviewing activity you see greater than expected search query volumes. You suspect that the query key is compromised.
You need to prevent unauthorized access to the search endpoint and ensure that users only have read only access to the documents collection. The solution must minimize app downtime.
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:
Enforces Read-Only Permissions: Query keys are specifically designed to provide read-only access to the documents collection of an index. Admin keys provide full read-write administrative privileges and should never be distributed to consumer-facing applications.
Zero Downtime: Azure AI Search lets you generate up to 50 individual query keys. Creating a new one allows the app to stay online throughout the entire key rotation process Reference:
https://learn.microsoft.com/en-us/azure/search/search-security-api-keys
NEW QUESTION # 146
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