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| Section | Weight | Objectives |
|---|---|---|
| Implement Decision Support Solutions | 10-15% | - Design and implement decision automation - Integrate Azure AI services into applications - Monitor and optimize decision support solutions |
| Implement Computer Vision Solutions | 15-20% | - Analyze images using Azure AI Vision - Implement Azure AI Face service - Process video and generate insights - Read and process documents using Azure AI Document Intelligence - Implement object detection and image classification |
| Implement Natural Language Processing Solutions | 20-25% | - Translate text and speech - Create conversational AI solutions with Azure AI Bot Service - Implement speech capabilities using Azure AI Speech - Analyze text using Azure AI Language - Build question answering solutions |
| Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Implement Azure AI Search solutions - Design document processing pipelines - Implement semantic and vector search - Create custom skills for Azure AI Search |
| Implement Generative AI Solutions | 10-15% | - Use Azure OpenAI Service for generative AI - Integrate Azure OpenAI models into applications - Apply responsible AI practices - Implement prompt engineering techniques |
| Plan and Manage an Azure AI Solution | 15-20% | - Implement governance and compliance for AI solutions - Select appropriate Azure AI services and technologies - Manage AI costs and resource allocation - Plan and configure secure AI solutions |
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NEW QUESTION # 372
You have a text-based chatbot.
You need to enable content moderation by using the Text Moderation API of Content Moderator. Which two service responses should you use? Each correct answer presents part of the solution NOTE: Each correct selection is worth one point.
Answer: B,D
NEW QUESTION # 373
You build a bot by using the Microsoft Bot Framework SDK.
You need to test the bot interactively on a local machine.
Which three 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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
Explanation
NEW QUESTION # 374
You are creating an enrichment pipeline that will use Azure Cognitive Search. The knowledge store contains unstructured JSON data and scanned PDF documents that contain text.
Which projection type should you use for each data type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/search/knowledge-store-projection-overview
NEW QUESTION # 375
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: B
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 # 376
Hotspot Question
You are developing a service that records lectures given in English (United Kingdom).
You have a method named AppendToTranscriptFile that takes translated text and a language identifier.
You need to develop code that will provide transcripts of the lectures to attendees in their respective language. The supported languages are English, French, Spanish, and German.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: {"fr", "de", "es"}
A common task of speech translation is to specify target translation languages, at least one is required but multiples are supported. The following code snippet sets both French and German as translation language targets.
static async Task TranslateSpeechAsync()
{
var translationConfig =
SpeechTranslationConfig.FromSubscription(SPEECH__SUBSCRIPTION__KEY,
SPEECH__SERVICE__REGION);
translationConfig.SpeechRecognitionLanguage = "it-IT";
// Translate to languages. See, https://aka.ms/speech/sttt-languages
translationConfig.AddTargetLanguage("fr");
translationConfig.AddTargetLanguage("de");
}
Box 2: TranslationRecognizer
After you've created a SpeechTranslationConfig, the next step is to initialize a TranslationRecognizer.
Example code:
static async Task TranslateSpeechAsync()
{
var translationConfig =
SpeechTranslationConfig.FromSubscription(SPEECH__SUBSCRIPTION__KEY,
SPEECH__SERVICE__REGION);
var fromLanguage = "en-US";
var toLanguages = new List<string> { "it", "fr", "de" };
translationConfig.SpeechRecognitionLanguage = fromLanguage;
toLanguages.ForEach(translationConfig.AddTargetLanguage);
using var recognizer = new TranslationRecognizer(translationConfig); }
NEW QUESTION # 377
......
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