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| Section | Weight | Objectives |
|---|---|---|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
>> AI-103 Reliable Learning Materials <<
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NEW QUESTION # 99
You have a Microsoft Foundry project.
You need to deploy a model from the model catalog to support a search solution for internal policy documents. The model must generate vector representations of the text in the documents and of user queries.
Which type of model should you use?
Answer: C
Explanation:
To fulfill this requirement, you need an embeddings model from the Microsoft Foundry AI Model Catalog that captures semantic meaning and outputs numerical vectors for text chunks.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/openai/concepts/understand-embeddings
NEW QUESTION # 100
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: A
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 # 101
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 image moderation to block unsafe content before processing the images.
Does this meet the goal?
Answer: B
Explanation:
The solution does not fully meet the goal. Image moderation is appropriate for one part of the risk: blocking unsafe image content before the image is processed. Azure AI Content Safety provides image APIs that detect harmful content, and its harm categories and severity levels can be used to classify and block objectionable image content. This addresses unsafe photos, but it does not address hidden instructions embedded in images.
The second risk is prompt manipulation through extracted image text. After OCR extracts text from the uploaded image, that text becomes untrusted third-party content supplied to a generative model. Microsoft defines document attacks as malicious instructions embedded in third-party content, where the objective is to cause the model to execute unintended commands or alter intended behavior. Prompt Shields are the control designed to detect user prompt attacks and document attacks, including indirect attacks that come from uploaded or referenced content.
Therefore, image moderation alone is incomplete. A complete mitigation would combine image moderation for harmful visual content with Prompt Shields for document attacks, and optionally Spotlighting, so extracted or embedded text is treated as lower trust. Reference topics: Azure AI Content Safety, image moderation, Prompt Shields, document attacks, indirect prompt injection, and multimodal safety.
NEW QUESTION # 102
You need to recommend a solution to assess the responses generated by Agent1 when the agent uses the product information stored in storage1. The solution must meet the technical requirements.
Answer: B
Explanation:
A Retrieval Augmented Generation evaluator is required because Agent1 will retrieve product-sheet content and use it as grounding context when generating answers. Foundry RAG evaluators assess both retrieval quality and the quality of the final grounded response. This directly supports Contoso's requirement that answers based on the product sheets be relevant, complete, and accurate.
The appropriate evaluation suite can include groundedness , which determines whether the answer is supported by the retrieved product information without fabrication; relevance , which evaluates whether the answer accurately and completely addresses the customer's question; and response completeness , which measures whether critical expected information is missing. Retrieval evaluators can additionally determine whether the indexing and search pipeline returned context chunks relevant to the query.
A coherence evaluator assesses readability and logical organization but does not establish that claims are supported by storage1. A custom guardrail controls or blocks behavior rather than measuring RAG quality.
Fine-tuning changes model behavior and does not evaluate whether retrieved documentation was selected and used correctly.
The AI-103 Study Guide includes evaluating models and applications for fabrications, relevance, quality, and safety , as well as monitoring grounding quality and retrieval relevance.
NEW QUESTION # 103
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:
To achieve full, multi-turn, and cross-session continuity with complete historical context (including user messages, agent messages, tool calls, and tool outputs), you must leverage the durable Conversation lifecycle provided by the Microsoft Foundry Agent Service runtime.
Because the underlying Agent Service can be stateless at the inference tier, managing continuity across days requires linking your agent interactions to a persistent, server-side conversation ID.
Here is exactly what must be done to meet your requirements:
*-> 1. Maintain a Durable Conversation ID
Instead of creating a brand-new conversation session every time a user returns, your application must persist and map the unique support case to a Microsoft Foundry Conversation ID (typically prefixed with resp_* or conv_*) in your external database.
New Case: Create a new session and capture the generated ConversationId.
Returning Case: Retrieve the previously saved ConversationId associated with that customer's support case from your database.
2. Resume Sessions via the SDK
When the customer returns days later, initialize the agent's session by passing the existing Conversation ID into the session creation method. This forces the Microsoft Foundry Agent Service to automatically reload the complete, un-summarized thread history on the backend before processing the next turn.
Reference:
https://learn.microsoft.com/en-us/agent-framework/agents/conversations/session
NEW QUESTION # 104
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