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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Topic 2: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 4: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 5: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
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AI-103 is so flexible that you can easily change the timings, types of questions, and topics for each mock exam. DumpExam's Developing AI Apps and Agents on Azure practice test contains all the important questions that will appear in the actual AI-103 Exam. We design and update our Microsoft AI-103 exam questions after receiving precious feedback. You can try a demo and sample of AI-103 exam questions before purchasing.
NEW QUESTION # 34
You have an app named App1 that uses a Microsoft Foundry multimodal model deployment.
App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context.
Some uploaded images contain embedded text.
You need to prevent potentially malicious instructions from being processed by the model.
What should you use?
Answer: A
Explanation:
To prevent potentially malicious instructions embedded within untrusted external sources (such as text extracted from uploaded images via OCR) from being processed by the model, you should use Prompt Shields for Documents (specifically designed for Indirect Prompt Attacks or Cross- Domain Prompt Injections).
Key Features to Implement
Prompt Shields for Documents: This specific component of Azure AI Content Safety / Prompt Shields analyzes external data--like text pulled from files, emails, or OCR outputs--appended to the prompt. It identifies and neutralizes hidden adversarial instructions trying to trick the model into overriding its system protocols.
Spotlighting: A native capability within Microsoft Foundry's Prompt Shields, Spotlighting uses advanced text-formatting techniques to separate valid user commands from the untrusted document text. This prevents the model from mistaking malicious instructions embedded inside the OCR output for instructions from the developer or end-user.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/azure-ai-announces-prompt-shields-ga/4236033
NEW QUESTION # 35
You use Azure Custom Vision to build a classifier. After training is complete, which two metrics are available for review?
Answer: B,C
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Azure Custom Vision exposes precision and recall as primary metrics for evaluating trained model iterations.
Precision measures how many positive predictions are correct:
Precision = TP / (TP + FP)
A high precision value means that when the model predicts a class, it generates relatively few false positives.
Recall measures how many actual positive examples the model successfully identifies:
Recall = TP / (TP + FN)
A high recall value means that relatively few relevant examples are missed.
These metrics must be considered together. Increasing a prediction threshold can make the classifier more conservative, potentially improving precision while decreasing recall. Lowering the threshold often increases recall at the cost of additional false positives.
F-score can be calculated from precision and recall in general machine-learning practice, but it is not one of the two Custom Vision review metrics requested by the choices. Area under the curve and weighted accuracy likewise are not the standard selections represented in this question.
The AI-103 Study Guide requires understanding of computer-vision model implementation and operational evaluation when creating object/component-identification solutions. Microsoft Learn
NEW QUESTION # 36
You need a model that generates vector representations of internal policy-document text and user queries.
Answer: B
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use an embedding model . Embedding models convert text into numerical vector representations whose geometric relationships encode semantic similarity.
During indexing, policy documents are normally divided into chunks. Each chunk is submitted to the embedding model and the resulting vector is stored in a vector field in Azure AI Search or another vector store.
At query time, the user ' s natural-language question is submitted to the same embedding model. The resulting query vector can then be compared with the stored document vectors by using a nearest-neighbor similarity algorithm. Semantically similar passages can therefore be retrieved even when the query and policy use different wording.
A large language model or small language model can subsequently use the retrieved text to generate an answer, but the LLM/SLM is not the component specifically intended to create search vectors. An image- generation model creates visual content and is unrelated to textual vector retrieval.
Embedding models are therefore foundational to vector search and many RAG ingestion and retrieval architectures.
Study Guide alignment: configure semantic, hybrid, and vector search for grounding and build retrieval pipelines for internal documents . Microsoft Learn
NEW QUESTION # 37
You are building a voice agent for a pharmacy. It must accept spoken questions, reply with synthesised speech, and accurately recognise specialised medicine names that a general speech model often mishears. Which capability should you configure?
Answer: A
Explanation:
A custom speech model improves recognition of specialised vocabulary, such as medicine names, by adapting speech-to-text (STT) to your domain, and AI-103 covers integrating speech, including custom speech models, as an agent modality. This directly addresses the accuracy problem in the scenario.
NEW QUESTION # 38
You need to ensure that Agent1Dev Team can access Agent1. The solution must meet the security and compliance requirements.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
credential = DefaultAzureCredential()
agent = project_client.agents.get(agent_name=myAgent)
The correct authentication option is DefaultAzureCredential() because the case study states that API keys must not be used to access Foundry-deployed models and that Contoso developers must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication. It also states that access to Project1 must be assigned to Agent1Dev Team by using the security group SC_Agent1_Dev . Microsoft Foundry authentication guidance recommends Microsoft Entra ID for production workloads because it supports least- privilege RBAC, per-principal auditing, and keyless authentication. AzureKeyCredential() would violate the no-API-key requirement, and None would not provide a valid credential.
The correct agent operation is get because the task is to access an existing agent named Agent1, not create a new version or retrieve a specific published version. Microsoft Foundry SDK examples show AIProjectClient created with DefaultAzureCredential() and then using project agent operations to create, retrieve, or interact with agents by name. To meet the compliance requirement, the group SC_Agent1_Dev must also be granted the appropriate project-scoped Foundry role, such as Foundry User, for Project1. Reference topics: Microsoft Entra authentication, Foundry RBAC, AIProjectClient, and project agent access.
NEW QUESTION # 39
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