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| Section | Objectives |
|---|---|
| Implement Knowledge Mining and Document Intelligence | - Build Azure AI Search solutions - Implement document intelligence solutions - Enrich and index data for search scenarios |
| Implement Natural Language Processing Solutions | - Translate and interpret language using Azure AI services - Implement question answering and conversational AI - Analyze text using Azure AI Language |
| Plan and Manage an Azure AI Solution | - Select appropriate Azure AI services - Manage security, compliance, and responsible AI - Plan solution requirements and architecture |
| Implement Computer Vision Solutions | - Extract information using OCR and form recognition - Implement custom vision models - Analyze images and videos using Azure AI Vision |
| Implement Generative AI Solutions | - Implement Azure OpenAI Service solutions - Integrate prompt engineering techniques - Build retrieval-augmented generation (RAG) solutions |
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NEW QUESTION # 199
You are building a chatbot.
You need to use the Content Moderator service to identify messages that contain sexually explicit language.
Which section in the response from the service will contain the category score, and which category will be assigned to the message? To answer, select the appropriate options in the answer area, NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Comprehensive Detailed ExplanationThe scenario:
* You are using Azure Content Moderator to filter chatbot messages.
* You must detect sexually explicit language.
* The question asks: which section of the response provides the category score, and which category number corresponds to sexual content.
Step 1 - Understand Content Moderator response structure
Content Moderator returns several sections:
* Classification # Provides scores for categories of potentially unsafe or inappropriate content.
* PII # Identifies personally identifiable information (phone numbers, email, addresses, etc.).
* Terms # Identifies terms from a custom or built-in blocklist.
Since the requirement is sexually explicit language detection, the correct section is Classification.
Step 2 - Identify the correct Category
Content Moderator's Classification section returns three default categories:
* Category 1: Potentially sexually explicit or adult content.
* Category 2: Potentially sexually suggestive content.
* Category 3: Offensive or derogatory language.
Since the question specifies sexually explicit language, the correct category is 1.
Correct Answer Mapping:
* Section: Classification
* Category: 1
* Content Moderator - Text moderation (classification categories explained)
* Content Moderator overview and response schema
Microsoft References
NEW QUESTION # 200
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You create a web app named app1 that runs on an Azure virtual machine named vm1. Vm1 is on an Azure virtual network named vnet1.
You plan to create a new Azure Cognitive Search service named service1.
You need to ensure that app1 can connect directly to service1 without routing traffic over the public internet.
Solution: You deploy service1 and a public endpoint to a new virtual network, and you configure Azure Private Link.
Does this meet the goal?
Answer: B
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/private-link/private-link-overview
NEW QUESTION # 201
You have a training dataset that contains 10,000 PDF documents. The documents contain scanned books, comics, and magazines.
You are building a solution that will use Azure Al and a custom model.
You need to train the model by using Language Studio. The solution must meet the following requirements:
* Tag each item as a book, comic, or magazine.
* Minimize development effort.
What should you use?
Answer: B
Explanation:
To tag each document as "book," "comic," or "magazine" with minimal development effort in Language Studio, you should use Custom Text Classification. In Language Studio, custom text classification supports two modes:
* Single-label (multi-class) - one class per document.
* Multi-label - multiple classes per document.
Since the categories appear mutually exclusive, in practice you would configure the project as single-label inside the custom text classification experience. Among the provided options, the only correct Language Studio text-classification choice is "a multi label classification project," which represents the Custom Text Classification project type (the other options are for different tasks). This approach lets you upload or connect your dataset, label samples in the browser, train, evaluate, and deploy a model with minimal code.
Why the other options are not suitable:
* Custom extraction model (Document Intelligence/Form Recognizer) is for field/key-value extraction, not assigning a document to a category.
* Custom NER project is for extracting entities (names, numbers, etc.) from text, not whole-document classification. Microsoft Learn
* Multi-label image classification model belongs to vision tooling (e.g., Custom Vision), not Language Studio, and your requirement is text/document categorization.
Microsoft References
* Custom Text Classification overview (supports single-label and multi-label projects). Microsoft Learn
* Quickstart: Create and train a custom text classification project in Language Studio. Microsoft Learn
* Data formats and labeling guidance for custom text classification. Microsoft Learn
* Custom Named Entity Recognition (for contrast; not used for document tagging). Microsoft Learn
NEW QUESTION # 202
You are building a chatbot by using the Microsoft Bot Framework SDK.
You use an object named UserProfile to store user profile information and an object named ConversationData to store information related to a conversation.
You create the following state accessors to store both objects in state.
var userStateAccessors = _userState.CreateProperty<UserProfile>(nameof(UserProfile)); var conversationStateAccessors =
_conversationState.CreateProperty<ConversationData>(nameof(ConversationData)); The state storage mechanism is set to Memory Storage.
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:
Box 1: Yes
You create property accessors using the CreateProperty method that provides a handle to the BotState object.
Each state property accessor allows you to get or set the value of the associated state property.
Box 2: Yes
Box 3: No
Before you exit the turn handler, you use the state management objects' SaveChangesAsync() method to write all state changes back to storage.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-howto-v4-state
NEW QUESTION # 203
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a chatbot that uses question answering in Azure Cognitive Service for Language Users report that the responses of the chatbot lark formality when answering spurious questions You need to ensure that the chatbot provides formal responses to spurious questions.
Solution: From Language Studio, you change the chitchat source to qna_chitchit_friindly.tsv. and then retrain and republish the model.
Does this meet the goal?
Answer: A
Explanation:
Comprehensive Detailed ExplanationScenario:
* You have a chatbot using Question Answering in Azure Cognitive Service for Language.
* Users report that the chatbot's responses to spurious questions (nonsense or off-topic input) lack formality.
* The goal is to ensure the chatbot provides formal responses.
Proposed Solution:
* Change the chitchat source to qna_chitchat_friendly.tsv in Language Studio, retrain, and republish.
Analysis:
* Azure provides prebuilt chitchat datasets for handling spurious/off-topic inputs.
* These datasets come in three styles:
* Professional # formal tone
* Friendly # casual/informal tone
* Witty # humorous tone
* If you want formal responses, you must use the professional chitchat dataset ( qna_chitchat_professional.tsv).
* The proposed solution uses qna_chitchat_friendly.tsv, which produces informal/casual responses, not formal ones.
Therefore, the solution does not meet the goal.
The answer: B. No
* Add chit-chat to your QnA Maker knowledge base
* Question Answering with chit-chat styles
Microsoft References
NEW QUESTION # 204
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