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| Section | Weight | Objectives |
|---|---|---|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Implement agentic solutions | 20-25% | - Build AI agents
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
By reviewing these results, you will be able to know and remove your mistakes. These AI-103 practice exams are created as per the pattern of the AI-103 real examination. Therefore, Developing AI Apps and Agents on Azure (AI-103) mock exam takers will experience the real exam environment. It will calm down their nerves so they can appear in the Microsoft AI-103 final test without anxiety or fear.
NEW QUESTION # 80
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: A
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 # 81
You have a Microsoft Foundry project that contains an agent named Agent1. Agent1 runs successfully, but Foundry Control Plane does NOT display values for error rates, runs, and token usage, and the Traces tab is empty. You need to ensure that Foundry Control Plane displays the appropriate values for Agent1.
Answer: D
Explanation:
Foundry Control Plane obtains agent observability information from the Application Insights resource connected to the Foundry project that hosts the agent. When the telemetry is available, Control Plane uses it to calculate run counts and error rates, report usage metrics such as token consumption and cost, and display execution traces. Microsoft's guidance explicitly directs administrators to configure Application Insights when these values are absent.
Application Insights must be connected to the project, and the agent or application must emit the applicable OpenTelemetry data. Traces are stored in Application Insights and surfaced through both the Foundry portal and Azure Monitor. Microsoft's tracing procedure requires connecting an Application Insights resource to the Foundry project before trace records can be displayed.
A Log Analytics workspace can provide the underlying storage for a workspace-based Application Insights resource, but assigning a workspace alone does not configure the project's agent telemetry connection.
Restarting Agent1 does not add instrumentation or a telemetry destination. Creating a new agent version also does not resolve the missing observability configuration.
Study Guide alignment: Integrate monitoring into deployed agents and set up observability by implementing tracing, token analytics, safety signals, latency analysis, and error investigation.
NEW QUESTION # 82
You need to measure the public perception of your brand on social media by using natural language processing. Which Azure service should you use?
Answer: A
Explanation:
Azure Language in Foundry Tools provides sentiment analysis and opinion mining , which are the appropriate natural language processing capabilities for evaluating public perception. Sentiment analysis processes unstructured social-media text and assigns classifications such as positive, neutral, or negative, together with confidence scores. Opinion mining extends this analysis by associating expressed sentiment with particular aspects, attributes, products, or services mentioned in the text. This allows the application to determine not only the overall attitude toward the brand but also which specific brand characteristics are receiving favorable or unfavorable reactions.
Azure Document Intelligence is intended primarily for extracting text, fields, tables, and structures from documents. Content Safety detects potentially harmful material rather than measuring consumer opinion.
Azure Vision processes visual content and is not the primary service for sentiment classification of written social-media posts.
Azure Language can be accessed through REST APIs, client libraries, or supported containers. The application submits the social-media text and receives document-level and sentence-level sentiment results that can be aggregated into brand-perception metrics, dashboards, or alerts.
Study Guide alignment: Implement text analysis solutions - perform sentiment analysis and opinion mining, process unstructured text, and interpret confidence scores and sentiment classifications.
NEW QUESTION # 83
You have a Microsoft Foundry project that contains a deployed ticket-triage agent.
You discover that sometimes the agent responds without calling any tools, even when a tool is required.
You need to ensure that the agent calls a tool during execution.
How should you complete the Python code? To answer, drag the appropriate values to the correct targets.
Each value 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:
" tool_choice " : " required "
The correct completion is " tool_choice " : " required " . In Microsoft Foundry Agent Service, tool_choice controls whether the model can answer directly or must invoke a tool during a run. The official Foundry tool guidance states that tool_choice provides deterministic control over tool calling: auto allows the model to decide whether to call tools, none prevents tool use, and required forces the model to call one or more tools.
This directly addresses the issue where the ticket-triage agent sometimes responds without invoking a required tool.
The completed payload should therefore add the tool_choice property beside assistant_id, with the value " required " . The value " auto " is incorrect because it preserves the current nondeterministic behavior. The values " tools " and " type " do not force execution-time tool invocation in this payload; they are used for tool definitions or typed objects in other contexts. response_format controls output formatting, not tool execution.
Reference topics: Microsoft Foundry Agent Service, tool calling reliability, run payload configuration, tool_choice, agent execution, and deterministic tool invocation.
NEW QUESTION # 84
You have an Azure subscription that contains an Azure OpenAI resource.
You plan to build an agent by using the Azure AI Agent Service. The agent will perform the following actions:
- Interpret written and spoken questions from users.
- Generate answers to the questions.
- Output the answers as speech.
You need to create the project for the agent.
What should you use?
Answer: C
Explanation:
Azure AI Foundry is a platform for designing, customizing, managing, and supporting AI applications and agents. It acts as an AI app factory, providing a unified environment with tools, models, and deployment pipelines for various AI tasks. It enables teams to build and operate AI solutions, including those powered by generative AI, while ensuring security, governance, and cost-efficiency.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-azure-ai-foundry
NEW QUESTION # 85
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