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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 2: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 3: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| 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% | - Build generative AI applications
|
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NEW QUESTION # 50
You use Azure Custom Vision to build a classifier. After training is complete, which two metrics are available for review?
Answer: B,E
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 # 51
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 # 52
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 Al 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:
Explanation:
Guardrails: Select User input, Output, Tool response, and Tool call and set Action to Block.
Storage access: A system-assigned managed identity that is assigned the Storage Blob Data Contributor role The guardrail must be applied to User input, Output, Tool response, and Tool call with the action set to Block
. Microsoft Foundry guardrails support four intervention points: user input, tool call, tool response, and output. This scenario includes user-provided screenshots, a ticketing tool that uploads images and returns blob URLs, and final agent responses. Applying blocking controls at all four points ensures harmful image-related content is inspected throughout the agent run and prevented from continuing or being returned to the user.
Microsoft's guardrails guidance also states that tool call and tool response controls are specifically required when harmful content can pass through agent tools.
For storage, configure the Azure AI Content Safety resource with a system-assigned managed identity and grant it Storage Blob Data Contributor on the storage account or container. The Content Safety image moderation quickstart states that images can be supplied by blob storage URL and that the Content Safety resource must be given storage access by enabling its system-assigned managed identity and assigning Storage Blob Data Contributor or Owner; Contributor is the least-privileged valid option shown. Reference topics: Foundry guardrails, agent intervention points, image moderation, managed identity, and Azure Storage RBAC.
NEW QUESTION # 53
You are building an Azure AI Search indexing pipeline named Pipeline1 that ingests invoices stored in Azure Blob Storage. The invoices are stored as scanned images.
You need to enable users to search invoice data across the invoice fields.
Which built-in skill should you add to the skillset of Pipeline1?
Answer: D
Explanation:
You should add the Azure Machine Learning OCR skill (historically known as the Microsoft.Skills.Vision.OcrSkill) to your skillset pipeline.
Text Extraction: Scanned images contain unsearchable pixels rather than text.
Data Ingestion: The OCR skill extracts the printed text and layout information from the images.
Downstream Processing: It feeds raw text into subsequent skills (like Entity Recognition) or directly into your search index fields.
Reference:
https://techcommunity.microsoft.com/blog/azurearchitectureblog/from-large-semi-structured-docs-to-actionable-data-reusable-pipelines-with-adi-a/4474054
NEW QUESTION # 54
You have an app that uses Azure Al Custom Vision and a custom trained classifier to identify products in images. You need to add new products to the classifier. The solution must meet the following requirements:
* Minimize how long it takes to add the products.
* Minimize development effort.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Because the existing classifier is an Azure AI Custom Vision project, the lowest-effort workflow is performed directly in the Custom Vision portal , not Azure Machine Learning studio or Vision Studio. Open the existing project so that the new product classes can be incorporated into its current training dataset.
Next, upload representative sample images of each new product. The images should include sufficient variation in viewing angle, lighting, background, size, and presentation to support reliable classification.
Label each image with the appropriate new product tag. Custom Vision uses these tags as the target classes during supervised training. Microsoft's portal workflow explicitly supports uploading and manually tagging images without requiring application code.
Retrain the classifier after the labeled images have been added. Training creates a new iteration based on all current training images and tags, including the newly introduced product categories.
Finally, publish the trained iteration to the prediction resource. Publishing makes that iteration available through the Custom Vision Prediction API used by the application.
Study Guide alignment: create, train, evaluate, retrain, publish, and consume custom image-classification models .
NEW QUESTION # 55
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