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| Section | Objectives |
|---|---|
| Topic 1: Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Topic 2: Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
| Topic 3: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 4: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 5: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
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NEW QUESTION # 133
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 # 134
You are building an app by using the Semantic Kernel.
You need to include complex objects in the prompt templates of the app. The solution must support objects that contain subproperties.
Which two prompt templates can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: D,E
Explanation:
Semantic Kernel provides support for the following template formats:
semantic-kernel - Built-in Semantic Kernel format.
handlebars - Handlebars template format.
liquid - Liquid template format
The Semantic Kernel prompt template language is a simple way to define and compose AI functions using plain text. You can use it to create natural language prompts, generate responses, extract information, invoke other prompts or perform any other task that can be expressed with text.
Reference:
https://learn.microsoft.com/en-us/semantic-kernel/concepts/prompts/prompt-template-syntax
NEW QUESTION # 135
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: B
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 # 136
You are deploying a support agent that enables users to upload photos.
You need to automatically classify uploaded images for harmful content. The solution must block content based on severity levels.
What should you do?
Answer: A
Explanation:
The best solution in this scenario is to implement image moderation.
Azure AI Content Safety provides dedicated image moderation capabilities that automatically detect, classify, and score harmful visual content (such as violence, hate speech, sexual content, and self-harm). It natively allows you to configure threshold blocklists based on four severity levels (Low, Medium, High), perfectly matching the requirements.
Incorrect:
[Not A]
Optical Character Recognition (OCR) only extracts text embedded within an image. If a user uploads a harmful photo that contains no text (e.g., a violent image), OCR and keyword scanning will completely fail to detect it.
[Not B]
The Prompt shields feature is designed to protect generative AI models from text-based attacks, such as jailbreaking or prompt injection. It does not analyze or classify the visual pixels of an uploaded photo for harmful content.
[Not C]
While Azure AI Content Safety uses blocklists, standard text blocklists match specific exact terms or regex patterns. They are used for text moderation, not for evaluating the severity levels of raw visual imagery.
Reference:
https://supportzebra.com/blog/what-is-ai-image-moderation
NEW QUESTION # 137
You are building an app that will share user images. You need to configure the app to:
* Categorize each image as a photograph or drawing.
* Generate a caption for the image.
* Minimize development effort.
Which two services should you include?
Answer: C,D
Explanation:
Use image type detection to distinguish drawings from ordinary photographic content. Azure Vision's ImageType visual feature returns information including lineDrawingType, which indicates whether the submitted image is a line drawing, and clipArtType, which estimates whether it is clip art. The application can use these pretrained results to implement the required photograph-versus-drawing categorization without collecting labeled images or training a custom classifier.
Use image descriptions to generate the caption. Azure Vision Image Analysis provides the Caption visual feature, which generates a concise, one-sentence description of the overall image. Dense Captions can additionally describe individual regions, but the standard Caption feature directly satisfies the stated requirement.
Object detection identifies and locates individual objects by using bounding boxes; it does not determine whether the entire image is a photograph or drawing. Content tags produce descriptive keywords rather than a natural-language caption. Azure Custom Vision classification could be trained for the image-type distinction, but it would require image collection, labeling, training, evaluation, and deployment, increasing development effort unnecessarily.
Study Guide alignment: configure applications to produce captions and implement solutions that identify visual characteristics within images .
NEW QUESTION # 138
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