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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Topic 2: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Topic 3: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 4: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 5: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
>> Reliable AI-103 Dumps Sheet <<
You will need to pass the Developing AI Apps and Agents on Azure (AI-103) exam to achieve the Microsoft AI-103 certification. Due to extremely high competition, passing the Microsoft AI-103 exam is not easy; however, possible. You can use ActualPDF products to pass the AI-103 Exam on the first attempt. The Microsoft practice exam gives you confidence and helps you understand the criteria of the testing authority and pass the Developing AI Apps and Agents on Azure (AI-103) exam on the first attempt.
NEW QUESTION # 52
You are developing an application that will use the Azure vision in Foundry Tools client library. The application has the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The first statement is No because the code requests only the tags and description visual features. No face- related visual capability is requested. Additionally, facial identification/recognition involves specialized Face capabilities and is not implied merely by calling general Image Analysis.
The second statement is Yes . The code explicitly iterates through image_analysis.tags and prints tag.name together with tag.confidence. The output therefore includes every returned tag and its associated confidence value.
The third statement is also Yes . The statement:
with open(local_image, " rb " ) as image_stream:
opens a path referenced by local_image from the application ' s local file system and provides the resulting binary stream to analyze_image_in_stream. This is different from submitting an externally hosted image URL.
The requested visual features determine which analysis results are generated, while the method used to supply the image determines its source. Here, the input source is a local file and the requested results are image descriptions and semantic tags.
Study Guide alignment: build solutions that analyze visual context and configure applications to produce captions and visual characteristics .
NEW QUESTION # 53
A finance team must extract named fields, such as invoice number and total, from invoices, and they need a per-field confidence score so that low-confidence values can be routed to a human reviewer. Which Azure Content Understanding mode should they use?
Answer: B
Explanation:
Standard mode in Azure Content Understanding supports extract fields and returns confidence scores and grounding, which is exactly what a human-in-the-loop review workflow needs. Pro mode is built for multi-step reasoning across documents and deliberately omits confidence scoring.
Content Understanding pro mode currently doesn't offer confidence scores or grounding. It supports classify and generate fields, but it doesn't support extract fields.
NEW QUESTION # 54
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 image moderation to block unsafe content before processing the images.
Does this meet the goal?
Answer: A
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 # 55
You have a Microsoft Foundry resource named Al1 that hosts three deployments of the GPT 3.5 model. Each deployment is optimized for a unique workload.
You plan to deploy three apps. Each app will access AM by using the REST API and will use the deployment that was optimized for the apps intended workload.
You need to provide each app with access to All and the appropriate deployment. The solution must ensure that only the apps can access AH.
What should you use to provide access to AI1. and what should each app use to connect to its appropriate deployment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Use Microsoft Entra ID authentication and pass an OAuth 2.0 bearer token in the REST request's Authorization header. Assign the required inference role, such as Cognitive Services User , only to each application's managed identity or service principal. This authenticates the applications as distinct security principals and supports resource-scoped RBAC, auditing, token expiration, and immediate access revocation.
Microsoft recommends Entra ID for production workloads because API keys are static, resource-wide secrets that cannot identify individual callers or provide granular authorization.
Each application must specify its appropriate deployment name when making an inference request. Foundry deployments assign a unique name and configuration to each deployed model. In the deployment-level REST API, that name appears in the request route:
/openai/deployments/{deployment-name}/chat/completions
The resource endpoint and authentication mechanism can remain common across all three applications, while the deployment name directs each request to the GPT-3.5 deployment optimized for that application's workload. A deployment type describes capacity or provisioning characteristics and does not select a particular deployed model.
Study Guide alignment: configure authentication, managed identities, RBAC, model deployments, and REST-based model consumption.
NEW QUESTION # 56
Hotspot Question
You have a Microsoft Foundry project that contains an internal Q&A agent.
Users report the following issues when they ask the agent questions:
- An increase in the following response: "No relevant information
found"
- Periodic HTTP 429 rate limit exceeded errors during peak hours
You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 57
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