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You will need to pass the Developing AI Apps and Agents on Azure (AI-103) exam to achieve the Developing AI Apps and Agents on Azure (AI-103) certification. Due to extremely high competition, passing the Microsoft AI-103 exam is not easy; however, possible. You can use EduDump products to pass the AI-103 Exam on the first attempt. The Developing AI Apps and Agents on Azure (AI-103) practice exam gives you confidence and helps you understand the criteria of the testing authority and pass the Microsoft AI-103 exam on the first attempt.
| Section | Objectives |
|---|---|
| Implement Natural Language Processing Solutions | - Translation and multilingual support - Language understanding and intent recognition - Text analytics and summarization |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Azure AI resource provisioning and configuration - Model selection and lifecycle management |
| Knowledge Mining and Information Retrieval | - Azure AI Search configuration - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns |
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NEW QUESTION # 88
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?
Answer: C
Explanation:
You should implement an evaluation and retry loop in your application code.
You must wrap the agent call inside a conditional code loop that evaluates the output against required criteria (such as a checklist of regulatory clauses), and programmatically forces a retry if information is omitted.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/ai-search
NEW QUESTION # 89
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: A
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 # 90
You have a Microsoft Foundry project that contains an agent.
The agent uses Azure AI Search for Retrieval Augmented Generation (RAG).
You plan to ingest and index PDF product manuals.
You need to build a solution that supports semantic similarity matching. The solution must ensure that the agent retrieves relevant data when user questions use different wording than the product manuals.
Which indexing approach should you use?
Answer: C
Explanation:
Vector search is the correct indexing and retrieval type to use for this solution.
Vector search versus semantic ranking
While both features help bridge the gap between different wordings, vector search is specifically designed at the indexing and retrieval layer to handle semantic similarity matching by converting text into mathematical vectors (embeddings) based on conceptual meaning. Semantic ranking is a secondary re-ranking layer applied after initial retrieval to improve precision, but it cannot function as the primary indexing method on its own.
Reference:
https://www.scribd.com/document/866453317/Agentforce-Specialist
NEW QUESTION # 91
You need to configure personalized user interactions for Agent1 based on the business requirements. What should you include in the solution?
Answer: B
Explanation:
Agent1 requires memory because the business requirement explicitly states that the agent must retain conversation context and recall relevant information during future interactions. Microsoft Foundry Agent Service memory provides persistent knowledge that can be retained and retrieved across separate conversations, sessions, devices, and workflows. It can store meaningful information extracted from prior interactions, including user preferences, relevant facts, and summarized context, and then retrieve that information when personalizing later responses.
Short-term memory maintains context within the current conversation. Long-term memory supports continuity across future sessions, which directly addresses Contoso's requirement. Memory scopes should be associated with individual user identities so that one customer's stored information cannot be retrieved during another customer's interaction.
Guardrails enforce safety or behavioral restrictions but do not provide conversation recall. Tools enable the agent to access external systems, while instructions define the agent's role and operating boundaries. Neither tools nor instructions persist user-specific information across sessions.
The AI-103 Study Guide maps this requirement to choose appropriate memory, tool, and knowledge integration services and build agents that integrate retrieval, function calling, and conversation memory .
NEW QUESTION # 92
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 a prompt shield for user prompts.
Does this meet the goal?
Answer: B
Explanation:
Correct:
* You configure a prompt shield for documents.
Prompt Shield for Documents: Highly Effective (Critical Defense)
How it helps: This shield specifically scans untrusted, third-party data inputs (like external documents or text extracted from uploaded images).
Mechanism: It evaluates the extracted image text before it is sent to the LLM to identify hidden jail
* You configure a prompt shield for user prompts.
Prompt Shield for User Prompts: Partially Effective (Defense in Depth)
How it helps: This shield targets direct jailbreak attempts written manually by the user in the text prompt field accompanying the upload.
Mechanism: It prevents the user from typing supporting instructions that prime the model to execute the hidden instructions found within the image.
* You configure image moderation to block unsafe content before processing the images.
Implementing rigorous image moderation is one of the most effective ways to secure multimodal AI systems against these threats. Moderation acts as a necessary gatekeeper, preventing malicious inputs from ever reaching the generative model.
Incorrect:
* You configure protected material detection.
Protected Material Detection: Ineffective for this Threat
Why it does not help: This feature is designed to scan model outputs to prevent the generation of copyrighted text, proprietary source code, or licensed imagery.
Limitation: It does not scan inputs for adversarial instructions and will not prevent a user from manipulating the model's logic.
Reference:
https://www.upgrad.com/blog/what-is-multimodal-ai/
https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/jailbreak-detection
NEW QUESTION # 93
......
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