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| Section | Objectives |
|---|---|
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
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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: B
Explanation:
The solution does not fully meet the goal. Image moderation is appropriate for one part of the risk: blocking unsafe image content before the image is processed. Azure AI Content Safety provides image APIs that detect harmful content, and its harm categories and severity levels can be used to classify and block objectionable image content. This addresses unsafe photos, but it does not address hidden instructions embedded in images.
The second risk is prompt manipulation through extracted image text. After OCR extracts text from the uploaded image, that text becomes untrusted third-party content supplied to a generative model. Microsoft defines document attacks as malicious instructions embedded in third-party content, where the objective is to cause the model to execute unintended commands or alter intended behavior. Prompt Shields are the control designed to detect user prompt attacks and document attacks, including indirect attacks that come from uploaded or referenced content.
Therefore, image moderation alone is incomplete. A complete mitigation would combine image moderation for harmful visual content with Prompt Shields for document attacks, and optionally Spotlighting, so extracted or embedded text is treated as lower trust. Reference topics: Azure AI Content Safety, image moderation, Prompt Shields, document attacks, indirect prompt injection, and multimodal safety.
NEW QUESTION # 55
Hotspot Question
You need to recommend a plan to create a customer support agent by using the Microsoft Foundry Agent Service. The agent must meet the following requirements:
- Retain user preferences across multiple conversations.
- Enable users to provide contextual grounding by directly uploading
documents during a chat.
Which Foundry capability should you recommend for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 56
You are developing an app that will perform a sentiment analysis of social media posts by using the Azure AI Language service.
You perform a test on a sample post.
You need to quantify the results of the test.
Which JSON property should you review?
Answer: A
Explanation:
Sentiment analysis
The sentiment analysis feature assigns sentiment labels, such as "negative," "neutral," and
"positive." The service determines these labels using the highest confidence score. Sentiment is evaluated at both the sentence level and the document level. This feature also returns confidence scores between 0 and 1 for each document & sentences within it for positive, neutral, and negative sentiment.
In Azure AI Language's sentiment analysis, confidenceScores are numerical values between 0 and 1 that represent the probability that the text belongs to a specific sentiment (positive, neutral, or negative). A score closer to 1 indicates a higher confidence from the service that the text exhibits that sentiment, while a lower score signifies less confidence in that particular label. The service calculates these scores for both individual sentences and the entire document, providing a granular understanding of sentiment.
How to interpret confidenceScores:
High Score (close to 1): The model is very sure about the assigned sentiment. For example, a positive score of 0.95 means the model is 95% confident the text is positive.
Low Score (close to 0): The model is not very sure about the assigned sentiment.
Scores for each sentiment: For any given piece of text, the service returns a score for positive, neutral, and negative sentiment. The sentiment label that receives the highest score is assigned as the overall sentiment for that text.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/sentiment-opinion-mining/overview
NEW QUESTION # 57
You have an Azure Al Search resource named Search1 that is used by multiple apps hosted in Azure. You need to secure Search1. The solution must meet the following requirements:
* Prevent access to Search1 from the internet.
* Limit the access of each app to query specific indexes.
What should you do? To answer, select the appropriate options in the answer area. NOTE: Each correct answer is worth one point.
Answer:
Explanation:
Explanation:
* To prevent access from the internet: Create a private endpoint
* To limit access to query specific indexes: Use Azure roles
Create an Azure Private Link private endpoint for Search1 and disable public network access. The private endpoint assigns Search1 a private IP address within an Azure virtual network, allowing the hosted applications to reach the search service over the Microsoft backbone instead of through its public endpoint.
Disabling public network access ensures that requests originating from the public internet are rejected. An IP firewall would still expose the public endpoint to approved public IP addresses and therefore would not provide complete internet isolation.
For index-level authorization, enable Microsoft Entra ID role-based access control and assign Azure roles to each application's managed identity or service principal. The Search Index Data Reader role permits query and retrieval operations without allowing index modification. Azure AI Search supports scoping Search Index Data Reader or Search Index Data Contributor permissions to an individual index; custom role definitions can also be used when more precise permissions are required.
Query-key authentication is unsuitable because API keys normally provide service-level access and cannot reliably isolate applications to designated indexes.
Study Guide alignment: secure Azure AI services by configuring private endpoints, network isolation, managed identities, Microsoft Entra authentication, and role-based access control.
NEW QUESTION # 58
You have a web app named App1 that sends requests to a multimodal chat model deployment in a Microsoft Foundry project. User messages can contain both text and images. Currently, App1 includes image URLs as plain text inside the message content, so the model cannot recognize them as images. You need to send the message as a structured array that includes both the text portion and the image reference.
Answer: C
NEW QUESTION # 59
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