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| Section | Weight | Objectives |
|---|---|---|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
>> Microsoft AI-103 Exam Vce <<
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NEW QUESTION # 21
You have a Microsoft Foundry project.
You plan to build a customer support solution that contains an agent. The solution must meet the following requirements:
* Provide accurate, context-aware responses grounded in internal product documentation stored in Azure AI Search.
* Require deep, multi-step reasoning across long contexts.
* Generate detailed natural language responses.
Which type of model should you use to power the agent?
Answer: C
Explanation:
The correct model type is a large language model (LLM) . The scenario requires an agent that can reason over retrieved documentation, synthesize context-aware answers, and generate detailed natural language responses.
Microsoft Foundry RAG guidance defines Retrieval Augmented Generation as a pattern that combines search with large language models so responses are grounded in organizational data, which directly matches the use of Azure AI Search for internal product documentation.
An LLM is also the correct fit for deep, multi-step reasoning across long contexts. Azure AI Search agentic retrieval guidance states that complex chat and agent scenarios use an LLM to break a user query into smaller focused subqueries for better coverage over indexed content. Microsoft also describes Azure AI Search as a way to ground agents and chatbots in proprietary enterprise data for accurate, context-aware responses.
A multimodal model is unnecessary because the requirements are text-focused, not image, audio, or video based. A key phrase extraction model performs narrow text analytics and cannot generate detailed answers.
An SLM may reduce cost and latency, but the stated need for deep reasoning and long-context synthesis favors an LLM. Reference topics: Microsoft Foundry RAG, Azure AI Search grounding, agentic retrieval, and model selection for generative AI agents.
NEW QUESTION # 22
You have a Microsoft Foundry project that contains an agent used by the financial analysts at your company.
You need to optimize the agent workflow by providing additional data access and processing capabilities. The solution must meet the following requirements:
* Ensure that the agent can perform calculations during conversations
* Ensure that the agent can access up-to-date information from public websites.
* Ensure that the agent can retrieve information from documents uploaded directly to the agent.
What should you use for each requirement? To answer, drag the appropriate tools to the correct requirements.
Each tool may be
used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Access up-to-date information from public websites: Grounding with Bing Search Perform calculations during conversations: Code interpreter Retrieve information from documents uploaded directly to the agent: File search The correct tool for public, current web information is Grounding with Bing Search . Microsoft Foundry Agent Service identifies Grounding with Bing Search as the built-in tool that enables an agent to access and return information from the internet, which fits the requirement for up-to-date public website data. ( learn.
microsoft.com )
For calculations during conversations, use Code interpreter . Microsoft's Foundry guidance states that Code Interpreter enables an agent to run Python code in a sandboxed execution environment and solve data analysis and math tasks iteratively. This is the correct fit for financial analysts who need calculations, analysis, and potentially chart generation during the conversation.
For documents uploaded directly to the agent, use File search . Microsoft describes File Search as the tool that enables Foundry agents to search through documents, retrieve relevant information, and augment model responses with knowledge from uploaded files such as PDFs, Word documents, and proprietary content.
Computer use is for interacting with graphical applications, not calculation or document retrieval. Microsoft Fabric is for enterprise data and analytics integration, not direct uploaded document retrieval. Reference topics: Foundry Agent Service tools, Code Interpreter, File Search, and Grounding with Bing Search.
NEW QUESTION # 23
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 # 24
You have a customer support agent that uses the Microsoft Foundry Agent Service.
Sometimes, customers return to a session days later to continue the same support case, and the agent must resume with the full historical context. The agent must provide the following:
* Multi-turn continuity within the session
* Cross-session continuity for the same case
* Access to the full interaction history, including user messages, agent messages, tool calls, and tool outputs You need to ensure that the agent automatically reloads the complete history on each new turn.
What should you do?
Answer: A
Explanation:
The correct approach is to create and reuse a conversation by storing the conversation's ID and supplying that ID on subsequent requests . In Microsoft Foundry Agent Service, conversations are durable objects with unique identifiers that can be reused across sessions. The official runtime guidance states that conversations store items, including messages, tool calls, tool outputs, and other data, and are intended for multi-turn continuity, cross-session continuity, and inspection of what happened over time. This directly satisfies the requirement to resume the same support case days later with the full historical context.
Persisting only the final model response is insufficient because it loses the full interaction chain, especially tool calls and tool outputs that may be essential to case state. Memory summarization is also not the best fit because the requirement asks for the complete history, not a compressed representation that may omit details.
Reusing the conversation ID allows Foundry to maintain the conversation server-side so the next turn can reuse prior context without the client manually rebuilding prompts. Reference topics: Foundry Agent Service runtime components, conversations, conversation items, multi-turn continuity, cross-session continuity, and tool output history.
NEW QUESTION # 25
You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.
Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.
Sometimes, mixed-language segments result in incomplete or incorrect translations.
You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.
What should you do before sending the segments to Translator?
Answer: D
Explanation:
The correct answer is C. Split the mixed-language segments into single-language segments and translate each segment separately . Azure Translator has a known limitation for text that contains mixed-language input within the same sentence or segment. Microsoft's Translator known-issues guidance states that the Text translation API does not support translating sentences that contain mixed-language text and that translations can be incorrect or incomplete when a single sentence includes multiple languages. The documented workaround is to specify the intended source language, remove the mixed-language sentence, or split the text into single-language segments.
In this scenario, specifying English for all segments would fail when Spanish content is present. Automatic language detection can detect a dominant language, but it does not reliably solve a segment that contains both English and Spanish. Document translation is also not the right fix because the issue is mixed-language content inside transcript segments, and Microsoft notes similar incomplete results for mixed-source-language documents. Splitting the transcript into language-homogeneous segments ensures each translation request has a clear source language and lets the entire transcript be translated reliably. Reference topics: Azure Translator in Foundry Tools, text translation, language detection, mixed-language input, and transcript preprocessing.
NEW QUESTION # 26
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