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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage an Azure AI solution | 20-25% | - Plan solutions aligned with responsible AI principles - Select suitable AI models - Monitor, optimize, and secure AI solutions - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Create and configure Azure AI resources - Select appropriate Microsoft Foundry Services |
| Topic 2: Implement computer vision solutions | 10-15% | - Build and deploy custom vision models - Extract text and handwriting from images - Process and index video content - Integrate vision capabilities into applications - Analyze images and detect objects/features |
| Topic 3: Implement an agentic solution | 5-10% | - Develop multi-agent workflows and orchestration - Understand agent use cases and types - Test, deploy, and optimize agents - Build agents with Microsoft Foundry Agent Service |
| Topic 4: Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Apply prompt engineering and fine-tuning - Orchestrate multiple models and containers - Deploy and manage generative models - Integrate Azure OpenAI and other generative models |
| Topic 5: Implement natural language processing solutions | 15-20% | - Implement translation and summarization - Build conversational AI and chatbots - Customize and deploy NLP models - Perform text analysis, sentiment detection, and language detection |
| Topic 6: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes - Implement intelligent search and retrieval |
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NEW QUESTION # 80
You need to create a visualization of running sales totals per quarter as shown in the following exhibit.
What should you create in Cower Bl Desktop;1
Answer: A
NEW QUESTION # 81
You are building an app that will scan confidential documents and use the Language service to analyze the contents.
You provision an Azure Cognitive Services resource.
You need to ensure that the app can make requests to the Language service endpoint. The solution must ensure that confidential documents remain on-premises.
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.
Answer:
Explanation:
1 - Provision an on-premises Kubernetes cluster that is isolated from the internet.
2 - Pull an image from the Microsoft Container Registry (MCR).
3 - Run the container and specify an API key and the Endpoint URL of the Cognitive Services resource.
NEW QUESTION # 82
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 build a language model by using a Language Understanding service. The language model is used to search for information on a contact list by using an intent named FindContact.
A conversational expert provides you with the following list of phrases to use for training.
Find contacts in London. Who do I know in Seattle?
Search for contacts in Ukraine.
You need to implement the phrase list in Language Understanding.
Solution: You create a new pattern in the FindContact intent.
Does this meet the goal?
Answer: B
Explanation:
Scenario Recap:
* Intent: FindContact
* Training phrases include cities (London, Seattle, Ukraine).
* Requirement: Implement phrase list properly.
* Solution: Create a new pattern in the FindContact intent.
Analysis:
* Patterns in LUIS/CLU are useful for handling specific templates like "Book a flight from {origin} to
{destination}" .
* They are not the correct way to capture locations like London/Seattle in this scenario.
* Instead, locations should be modeled as entities (prebuilt GeographyV2 or custom Location entity).
NEW QUESTION # 83
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation
NEW QUESTION # 84
You are building a retail chatbot that will use a QnA Maker service.
You upload an internal support document to train the model. The document contains the following question:
"What is your warranty period?"
Users report that the chatbot returns the default QnA Maker answer when they ask the following question:
"How long is the warranty coverage?"
The chatbot returns the correct answer when the users ask the following question: 'What is your warranty period?" Both questions should return the same answer.
You need to increase the accuracy of the chatbot responses.
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. (Choose three.)
Answer:
Explanation:
Explanation:
Step 1: Add alternative phrasing to the question and answer (QnA) pair.
Add alternate questions to an existing QnA pair to improve the likelihood of a match to a user query.
Step 2: Retrain the model.
Periodically select Save and train after making edits to avoid losing changes.
Step 3: Republish the model
Note: A knowledge base consists of question and answer (QnA) pairs. Each pair has one answer and a pair contains all the information associated with that answer.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/edit-knowledge-base
NEW QUESTION # 85
......
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