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| Section | Objectives |
|---|---|
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search - Azure AI Search configuration |
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Translation and multilingual support - Text analytics and summarization |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
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NEW QUESTION # 109
You have a Microsoft Foundry project that contains an agent and uses a GitHub repository. The repository contains a YAM file named File1 that defines the evaluation settings of the agent. You need to create a GitHub Actions workflow that runs the evaluation defined in File1 when a pull request (PR) is opened. How should you configure the workflow?
Answer: C
Explanation:
The correct configuration choice is to set project-endpoint to the endpoint of the project.
azure-ai-project-endpoint: This parameter is required and must be set to the URL endpoint of your Microsoft Foundry project. This aligns with setting project-endpoint.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/evaluation-github-action
NEW QUESTION # 110
An application must translate a high volume of standard product descriptions into 20 languages quickly, consistently, and at the lowest cost. The text is straightforward, with no domain-specific nuance. Which approach best fits?
Answer: D
Explanation:
Azure Translator in Foundry Tools provides deterministic, broad-language translation at scale and at lower cost, which suits high-volume, straightforward content.
NEW QUESTION # 111
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 Al 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 a prompt shield for documents.
Does this meet the goal?
Answer: A
Explanation:
The solution does not fully meet the goal. A prompt shield for documents is the correct control for the embedded-instruction portion of the scenario. Microsoft defines Prompt Shields as protection against prompt manipulation, including attacks embedded in third-party or document-like content that is supplied to a generative model. OCR-extracted text from uploaded images is untrusted contextual content, so document attack protection is appropriate for detecting hidden instructions that attempt to override the model's intended behavior.
However, the scenario contains two separate risks: unsafe image uploads and hidden instructions embedded in images. Prompt Shields for documents address indirect prompt injection, but they do not classify or block harmful visual content in the uploaded image itself. Azure AI Content Safety image moderation is the control that scans images for harmful content categories such as sexual content, violence, hate, and self-harm with severity levels that can be used for blocking decisions.
Therefore, document prompt shielding alone is incomplete. A complete mitigation would combine image moderation for unsafe images with Prompt Shields for document attacks, and optionally Spotlighting for lower-trust third-party content. Reference topics: Content Safety image moderation, Prompt Shields, document attacks, indirect prompt injection, and multimodal safety.
NEW QUESTION # 112
You plan to deploy a containerized version of an Azure Al Language service that will be used for sentiment analysis.
You configure https: //contoso.cognitiveservices.azure, cow as the endpoint URI for the service.
You need to run the container on an Azure virtual machine by using Docker.
How should you complete the command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
First selection:
mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment
Second selection:
https://contoso.cognitiveservices.azure.com
Completed command:
docker run --rm -it -p 5000:5000 --memory 8g --cpus 1 \
mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment \
Eula=accept \
Billing=https://contoso.cognitiveservices.azure.com \
ApiKey=xxx
The image mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment is the official Microsoft Container Registry image for Azure AI Language sentiment analysis. The Billing argument must reference the endpoint of the provisioned Azure AI Language resource associated with the supplied API key. It is not the address used by applications to invoke the locally running container.
Azure
AI containers require three licensing and metering parameters: Eula=accept, a valid Billing endpoint, and ApiKey. Without all three values, the container does not start. The command maps host port 5000 to container port 5000, allocates 8 GB of memory and one CPU, creates an interactive terminal, and automatically removes the stopped container. Applications subsequently invoke the locally hosted service through an address such as http://
< vm-address > :5000. The Azure endpoint remains responsible for metering and authentication; analyzed customer text is processed by the container rather than sent to the billing endpoint.
Study Guide alignment: Implement text analysis solutions - perform sentiment analysis and opinion mining, deploy Azure Language containers, and configure container billing parameters.
NEW QUESTION # 113
You have a custom named entity recognition (NER) project in Azure Language in Foundry Tools for support tickets. The schema for the project contains an entity type named ContactInfo.
In tagged training files, ContactInfo is used for phone numbers, email addresses, and social media handles.
Model evaluation shows low precision for ContactInfo, including false positives in which nearby text is extracted as ContactInfo.
You need to improve the precision of the project.
What should you do before retraining the model?
Answer: A
Explanation:
To improve the precision of the project before retraining the model, you should replace ContactInfo by using Phone, Email, and SocialMedia entities, and then relabel every matching span.
Reduces Model Confusion: Combining distinct types of data (like phone numbers, emails, and usernames) into a single generic entity (ContactInfo) creates conflicting patterns for the model.
Splitting them allows the model to learn the specific, unique syntax rules of each type.
Eliminates Over-extraction: When an entity is too broad, the model struggles to identify where the entity begins and ends, leading to false positives by grabbing adjacent text. Distinct entities provide sharper, cleaner boundaries.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/custom-named-entity-recognition/overview
NEW QUESTION # 114
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