P.S. Free & New AI-103 dumps are available on Google Drive shared by ExamcollectionPass: https://drive.google.com/open?id=16hTlLr2OpeVQvSp-pPDgxYep6jv4xHFM
The ExamcollectionPass is a trusted and reliable platform that has been helping the Developing AI Apps and Agents on Azure (AI-103) certification exam candidates for many years. Over this long time period, the AI-103 Exam Practice questions have helped the AI-103 exam candidates in their preparation and enabled them to pass the challenging exam on the first attempt.
| Section | Weight | Objectives |
|---|---|---|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
>> AI-103 Test Collection Pdf <<
We respect private information of our customers, and if you purchase AI-103 exam dumps from us, your personal information such as name and email address will be protected well. Once the order finishes, your information will be concealed. We won’t send junk email to you. Besides, AI-103 exam braindumps of us offer you free update for you, and we recommend you to have a try before buying, therefore you can have a better understanding of what you are going to buy. We have online service stuff, and if you have any questions about AI-103 Exam Dumps, just contact us.
NEW QUESTION # 36
Hotspot Question
You have a Microsoft Founcy project that contains a Retrieval Augmented Generation (RAG) solution.
You need to run a pre-production evaluation by using labeled CSV dataset that contains the query, context, response and ground truth. The evaluation must measure the following:
- Whether responses address the user query
- Whether responses are supported by the provided context
- Whether responses contain sensitive or proprietary information
Which AI quality evaluation metrics should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Groundedness and Relevance
The two best AI quality evaluation metrics for your pre-production evaluation in Microsoft Foundry are Relevance and Groundedness.
Microsoft Foundry provides built-in, AI-assisted quality evaluators specifically designed to measure these two critical dimensions of a Retrieval-Augmented Generation (RAG) system:
Relevance: This metric directly addresses your first requirement: whether responses address the user query. It evaluates how well the gens.nerated response aligns with and answers the initial user query.
Groundedness: This metric directly addresses your second requirement: whether responses are supported by the provided context. It evaluates the degree to which the generated response is factually supported by and derived strictly from the retrieved context, which helps capture model hallucinatio Box 2: Protected material The best AI quality evaluation metric for this scenario is Protected material.
Direct alignment: The Protected material metric specifically checks whether an AI model outputs copyrighted text, proprietary data, or sensitive information that matches its training data or internal context.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/observability
https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/langchain-middleware
NEW QUESTION # 37
You are building an app by using the Semantic Kernel.
You need to include complex objects in the prompt templates of the app. The solution must support objects that contain subproperties.
Which two prompt templates can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,E
Explanation:
Semantic Kernel provides support for the following template formats:
semantic-kernel - Built-in Semantic Kernel format.
handlebars - Handlebars template format.
liquid - Liquid template format
The Semantic Kernel prompt template language is a simple way to define and compose AI functions using plain text. You can use it to create natural language prompts, generate responses, extract information, invoke other prompts or perform any other task that can be expressed with text.
Reference:
https://learn.microsoft.com/en-us/semantic-kernel/concepts/prompts/prompt-template-syntax
NEW QUESTION # 38
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: A,B
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 # 39
You have a Microsoft Foundry project that contains an agent.
The agent uses Azure Content Understanding in Foundry Too to process vendor onboarding packets. The packs include digital PDFs that contain tables and hyperlinks.
The extracted content is indexed for search and provided to a downstream agent in the Markdown format.
You need to generate a Markdown output that has a layout and a semantic structure optimized for Retrieval Augmented Generation (RAG) workflows.
Which built-in analyzer should you use?
Answer: C
Explanation:
The appropriate built-in analyzer to use is prebuilt-documentSearch.
RAG Optimization: The prebuilt-documentSearch analyzer is part of the dedicated Retrieval- Augmented Generation (RAG) analyzer suite within Azure Content Understanding. It is specifically built to perform semantic analysis and extract documents in structures optimized for chunking, indexing, and vector workflows.
Native Markdown Layout: It extracts layout structures (such as paragraphs, complex multi-page tables, and hierarchical sections) directly into clean Markdown format. This structural preservation is ideal for downstream consumption by large language models (LLMs) or other AI agents.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/concepts/prebuilt-analyzers
NEW QUESTION # 40
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 # 41
......
ExamcollectionPass has a huge team of IT experts, who continue to use their knowledge and experience to study a lot of IT certification examination papers of past few years. Their findings of the research is now the product of ExamcollectionPass, therefore ExamcollectionPass's Microsoft AI-103 Practice Questions are very similar with the real exam, which can help a lot of people to realize their dreams. ExamcollectionPass can ensure you to successfully pass the exam, and you can boldly Add ExamcollectionPass's products to your shopping cart. With ExamcollectionPass your dreams can be achieved immediately.
Dumps AI-103 Collection: https://www.examcollectionpass.com/Microsoft/AI-103-practice-exam-dumps.html
DOWNLOAD the newest ExamcollectionPass AI-103 PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=16hTlLr2OpeVQvSp-pPDgxYep6jv4xHFM