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One of the advantages of taking the Braindumpsqa Developing AI Apps and Agents on Azure (AI-103) practice exam (desktop and web-based) is that it helps applicants to focus on their weak areas. It also helps applicants to track their progress and make improvements. Microsoft AI-103 Practice Exams are particularly helpful in identifying areas where one needs more practice.
| Section | Weight | Objectives |
|---|---|---|
| Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement agentic solutions | 20-25% | - Manage agent operations
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
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NEW QUESTION # 148
You have a Microsoft Foundry project that contains a high-traffic agent.
After a recent update, operational costs increase significantly.
Monitoring confirms that the volume of user traffic to the agent remains unchanged.
You suspect that changes to the request or response characteristics are causing the increase.
You need to identify whether the additional costs are driven by the model input size, the model output size, or expanded tool usage.
Which observability capability should you use?
Answer: A
Explanation:
The correct capability is token usage . In Microsoft Foundry observability, token consumption is the primary signal for diagnosing model-cost changes when request volume is unchanged. Token usage lets you distinguish whether costs increased because prompts became larger, retrieved or tool-provided context expanded, responses became longer, or agent execution added more model calls. Microsoft Foundry monitoring dashboards track operational metrics such as token consumption, latency, error rates, and quality scores, and the agent monitoring dashboard is specifically intended to help analyze token usage, latency, success rates, and evaluation outcomes for production traffic.
This directly matches the scenario because the issue is not more traffic, but changed request or response characteristics. Input tokens reveal whether the prompt, chat history, grounding data, or tool outputs being sent to the model increased. Output tokens reveal whether the model is generating longer completions.
Expanded tool usage can also increase cost indirectly by adding more tool results, intermediate calls, and context into subsequent model requests; Foundry tracing and observability capture tool usage and token consumption for agent runs.
Evaluation metrics assess response quality and safety, not cost drivers. Latency identifies performance delays, and run success rate measures reliability. Reference topics: Microsoft Foundry observability, agent monitoring dashboard, token consumption, cost analysis, tool usage, and production monitoring.
NEW QUESTION # 149
You plan to configure an evaluation in Microsoft Foundry for a Retrieval Augmented Generation (RAG) chat app.
You need to provide scores for groundedness, relevance, and harmful content categories.
Which two evaluation categories can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: C,E
Explanation:
The two evaluation categories that would be of use are AI quality (AI assisted) metrics and risk and safety metrics Risk and safety metrics: Scoring harmful content categories (such as violence, sexual content, hate speech, and self-harm) is handled directly by the specialized safety evaluators contained within this category.
AI quality (AI assisted) metrics: In Microsoft Foundry, evaluating RAG-specific criteria like groundedness and relevance requires large language models (LLMs) acting as a judge to assess semantic context. These are explicitly categorized under AI-assisted quality metrics rather than standard natural language processing (NLP) rules.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/evaluation-evaluators/rag-evaluators
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/develop/evaluate-sdk
NEW QUESTION # 150
You are building a web app named App1 that generates responses by using a model deployed to a Microsoft Foundry project named Project1.
Before sending the prompts to the model, App1 must retrieve documents by using Azure AI Search.
You need to integrate Project1 and App1. The solution must meet the following requirements:
- Multiple client applications must use the same search configuration.
- A security policy must prevent key-based authentication.
- Administrative effort must be minimized.
What should you do?
Answer: C
Explanation:
To meet your security and architecture requirements, you must add the Azure AI Search instance as a Connection within your Azure AI Foundry project and configure Managed Identities for role- based access control (RBAC).
To securely unify your search configuration without API keys, add the Azure AI Search instance as a shared Connection in your Azure AI Foundry project, disable key authentication on the search service, and authorize your applications using Azure RBAC and Managed Identities.
Note:
*-> 1. Create a Project Connection
Connect Azure AI Search directly inside the Azure AI Foundry hub or project.
*-> Share the same search service configuration across all connected client applications automatically.
Centralize your search endpoint details to reduce administrative overhead.
2. Disable Key Authentication
3. Enable Managed Identities
4. Update the Web App Code
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/tutorials/copilot-sdk-create-resources
NEW QUESTION # 151
You successfully run an HTTP request to:
.../regenerateKey?...
with:
{ " keyName " : " Key2 " }
What is the result?
Answer: B
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The request invokes the Azure Cognitive Services/Azure AI resource regenerateKey management operation and explicitly specifies " keyName " : " Key2 " .
Azure AI service resources traditionally expose two interchangeable access keys: Key1 and Key2. Key1 is treated as the primary key and Key2 as the secondary key. Regenerating Key2 invalidates the previous Key2 value and produces a new value for that secondary credential.
Only the selected key is regenerated. Key1 remains unchanged, so option D is incorrect.
The request is unrelated to Azure AI Search query keys, which are separate read-only Search credentials. It also does not cause the newly generated key to be written automatically to Azure Key Vault. Such storage would require separate Key Vault configuration or deployment automation.
Maintaining two resource keys enables zero- or low-downtime key rotation: applications can temporarily use one key while the other is regenerated, then switch and rotate the remaining key.
The AI-103 Study Guide includes securing and managing Azure AI systems, including credentials, authentication, least privilege, and operational resource management. Microsoft Learn
NEW QUESTION # 152
Hotspot Question
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 AI 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:
NEW QUESTION # 153
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