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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Implement intelligent search and retrieval - Build knowledge bases and search indexes - Extract entities, relationships, and key phrases |
| Topic 2: Implement generative AI solutions | 15-20% | - Orchestrate multiple models and containers - Apply prompt engineering and fine-tuning - Deploy and manage generative models - Integrate Azure OpenAI and other generative models - Implement model monitoring and feedback |
| Topic 3: Implement an agentic solution | 5-10% | - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Understand agent use cases and types - Build agents with Microsoft Foundry Agent Service |
| Topic 4: Plan and manage an Azure AI solution | 20-25% | - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources - Monitor, optimize, and secure AI solutions - Select suitable AI models - Plan solutions aligned with responsible AI principles |
| Topic 5: Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Customize and deploy NLP models - Implement translation and summarization |
| Topic 6: Implement computer vision solutions | 10-15% | - Process and index video content - Integrate vision capabilities into applications - Analyze images and detect objects/features - Build and deploy custom vision models - Extract text and handwriting from images |
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NEW QUESTION # 400
You have an Azure Cognitive Search solution and an enrichment pipeline that performs Sentiment Analysis on social media posts.
You need to define a knowledge store that will include the social media posts and the Sentiment Analysis results.
Which two fields should you include in the definition? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
In Azure Cognitive Search, a knowledge store is an optional capability that allows you to persist the enriched data from a skillset (enrichment pipeline) into an Azure Storage account. This is often used when you want to further analyze or visualize enriched content, e.g., sentiment analysis results from social media posts.
When defining a knowledge store, you need:
storageConnectionString
This is required so the enrichment pipeline can connect to your Azure Storage account.
Without it, the knowledge store cannot persist any data.
storageContainer
This specifies the container in Blob Storage where enriched documents or projections will be stored.
This allows you to persist the original documents and enriched outputs such as sentiment analysis scores.
Other options (not correct in this case):
tables # This is a projection type used within a knowledge store to structure output in Table Storage, but it's not required in the definition itself.
files # Another projection type for storing enriched documents as files in Blob Storage, but again, not a required field in the definition itself.
objects # A projection type used for representing enriched documents as JSON objects in Blob Storage.
Thus, while tables, files, and objects are used inside projections within a knowledge store, the definition itself always requires a storage connection string and storage container.
A). storageContainer
C). storageConnectionString
Microsoft References
Knowledge store in Azure Cognitive Search
How to define a knowledge store
NEW QUESTION # 401
You are building a Language Understanding model for purchasing tickets.
You have the following utterance for an intent named PurchaseAndSendTickets.
Purchase [2 audit business] tickets to [Paris] [next Monday] and send tickets to [email@domain.com] You need to select the entity types. The solution must use built-in entity types to minimize training data whenever possible.
Which entity type should you use for each label? To answer, drag the appropriate entity types to the correct labels. Each entity type may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
Answer:
Explanation:
Explanation
Graphical user interface, application Description automatically generated
Box 1: GeographyV2
The prebuilt geographyV2 entity detects places. Because this entity is already trained, you do not need to add example utterances containing GeographyV2 to the application intents.
Box 2: Email
Email prebuilt entity for a LUIS app: Email extraction includes the entire email address from an utterance.
Because this entity is already trained, you do not need to add example utterances containing email to the application intents.
Box 3: Machine learned
The machine-learning entity is the preferred entity for building LUIS applications.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-geographyv2
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-email
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-machine-learned-entity
NEW QUESTION # 402
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:
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-howto-v4-state
NEW QUESTION # 403
You need to upload speech samples to a Speech Studio project. How should you upload the samples?
Answer: C
Explanation:
Explanation
To upload your data, navigate to the Speech Studio . From the portal, click Upload data to launch the wizard and create your first dataset. You'll be asked to select a speech data type for your dataset, before allowing you to upload your data.
The default audio streaming format is WAV
Use this table to ensure that your audio files are formatted correctly for use with Custom Speech:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/how-to-custom-speech-test-and-train
NEW QUESTION # 404
You are developing an application that will recognize faults in components produced on a factory production line. The components are specific to your business.
You need to use the Custom Vision API to help detect common faults.
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:
Explanation:
Comprehensive Detailed Explanation
You are tasked with building a solution using Custom Vision to detect faults in factory components. Custom Vision follows a defined workflow:
Create a project
Before anything else, you must create a Custom Vision project in the Azure AI portal.
This project specifies the type of model you are building: classification (classifying entire images) or object detection (locating items within images).
Since the question is about detecting faults in specific components (a business-specific scenario), it aligns with classification unless explicitly stated otherwise.
Upload and tag images
You must provide training data.
Upload a set of images showing both faulty and non-faulty components.
Tag each image correctly so that the model can learn from labeled examples.
Train the classifier model
Once images are tagged, you run training on the dataset to generate the classifier model.
The model can then be published and used for inference via API calls.
Why not the other options?
Initialize the training dataset: This is not a direct step in Custom Vision; instead, uploading and tagging images inherently prepares the training dataset.
Train the object detection model: Object detection is used if you need bounding boxes around items within an image. Since the question is about recognizing faults in a component (faulty vs non-faulty), a classifier model is appropriate.
Correct Sequence:
Create a project
Upload and tag images
Train the classifier model
Microsoft References
Quickstart: Build an image classification project with the Custom Vision portal Custom Vision overview Train and test a Custom Vision model
NEW QUESTION # 405
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