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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models - Orchestrate multiple models and containers |
| Topic 2: Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Process and index video content - Extract text and handwriting from images - Integrate vision capabilities into applications - Build and deploy custom vision models |
| Topic 3: 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 4: Implement knowledge mining and information extraction solutions | 15-20% | - Ingest and process structured/unstructured data - Implement intelligent search and retrieval - Extract entities, relationships, and key phrases - Build knowledge bases and search indexes |
| Topic 5: Implement an agentic solution | 5-10% | - Understand agent use cases and types - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration - Build agents with Microsoft Foundry Agent Service |
| Topic 6: Plan and manage an Azure AI solution | 20-25% | - Monitor, optimize, and secure AI solutions - Plan solutions aligned with responsible AI principles - Select suitable AI models - Select appropriate Microsoft Foundry Services - Create and configure Azure AI resources - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining |
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NEW QUESTION # 187
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:
Explanation:
Box 1: Object projection
Object projections are JSON representations of the enrichment tree that can be sourced from any node.
Box 2: File projection
File projections are similar to object projections and only act on the normalized_images collection.
Reference:
https://docs.microsoft.com/en-us/azure/search/knowledge-store-projection-overview
NEW QUESTION # 188
Which Azure Storage service implements the key/value model?
Answer: D
Explanation:
Azure Storage provides multiple services, each with different data models:
* Azure Files
* Provides fully managed file shares accessible via SMB/NFS.
* Works like a traditional file system (folders and files).
* Not key/value.
* Azure Blob Storage
* Object storage for unstructured data like images, videos, backups, logs.
* Identified by container + blob name.
* Not strictly a key/value store (though blob name acts as a key).
* Azure Table Storage #
* A NoSQL key/value store for structured, non-relational data.
* Data is stored in tables as entities (rows), with properties (columns).
* Entities are indexed by PartitionKey + RowKey, forming a unique key/value pair.
* Specifically designed for scalable key/value lookups.
* Azure Queue Storage
* Provides message queueing for communication between components.
* Stores ordered messages, not key/value pairs.
C). Azure Table
* Azure Table Storage overview
* Azure Storage services comparison
Microsoft References
NEW QUESTION # 189
You Build a bot in JavaScript.
From the Azure Command-Line interface (CLI), you run the following command.
az bot prepare-deploy
You need to deploy the bot to Azure.
Which three Azure CLl commands should you run in sequence? To answer, move the appropriate commands from the list of commands to the answer area and arrange them in the client order.
Answer:
Explanation:
Explanation
To deploy the bot to Azure, you should run the following three Azure CLI commands in sequence:
az deployment group create This command will create the Azure resources for your bot using an ARM template and a parameters file. You need to specify the resource group name, the template file path, and the parameters file path. For example:
az deployment group create --resource-group myResourceGroup --template-file
"deploymentTemplates\template-with-preexisting-rg.json" --parameters
"deploymentTemplates\parameters-for-template-BotApp-with-rg.json"
This command will also output the app ID and password of your bot, which you will need for the next command1.
az webapp deployment source config-zip This command will deploy your bot code to the app service that you created in the previous step. You need to specify the resource group name, the app service name, and the zip file path of your bot code. For example:
az webapp deployment source config-zip --resource-group myResourceGroup --name myBotAppService --src
"code.zip"
This command will also output the URL of your bot endpoint, which you will need for the next command2.
az ad app update This command will update your bot registration with the endpoint URL of your bot.
You need to specify the app ID of your bot and the endpoint URL. For example:
az ad app update --id myBotAppId --set
replyUrls="https://myBotAppService.azurewebsites.net/api/messages"
This command will complete the deployment process and make your bot ready to be tested3.
NEW QUESTION # 190
You are designing a conversational interface for an app that will be used to make vacation requests. The interface must gather the following data:
- The start date of a vacation
- The end date of a vacation
- The amount of required paid time off
The solution must minimize dialog complexity.
Which type of dialog should you use?
Answer: B
Explanation:
Waterfall dialog is used to manage linear and more complex conversation flows.
https://learn.microsoft.com/en-us/azure/bot-service/bot-builder-dialog-manage-conversation- flow?view=azure-bot-service-4.0&tabs=csharp Component dialog is used to create independent dialogs to handle specific scenarios, breaking a large dialog set into more manageable pieces.
https://learn.microsoft.com/en-us/azure/bot-service/bot-builder-compositcontrol?view=azure-bot- service-4.0&tabs=csharp
NEW QUESTION # 191
You are examining the Language service output of an application.
The text analyzed is: Our tour guide took us up the Space Needle during our trip to Seattle last week.
The response contains the data shown in the following table.
Which Language service API is used 10 analyze the Text?
Answer: C
Explanation:
The given text:
" Our tour guide took us up the Space Needle during our trip to Seattle last week. " The Language service output shows:
Text
Category
ConfidenceScore
Tour guide
PersonType
0.45
Space Needle
Location
0.38
Trip
Event
0.78
Seattle
Location
0.78
Last week
DateTime
0.80
Why the correct answer is Named Entity Recognition (NER):
NER identifies named entities (people, places, dates, events, organizations, etc.) in text and classifies them into categories with confidence scores.
In the table, we see categories such as PersonType, Location, Event, DateTime # these are classic outputs of NER.
Why not the others:
A). Entity Linking # Identifies entities and links them to a knowledge base (like Wikipedia). Output would include links/IDs rather than just categories.
C). Key Phrase Extraction # Returns important phrases ("tour guide", "Space Needle", "trip", "Seattle", "last week") but does not assign categories like PersonType, Location, DateTime.
D). Sentiment Analysis # Returns sentiment scores (positive/neutral/negative), not entity categories.
The answer:
B). Named Entity Recognition
Microsoft References
Named Entity Recognition in Azure AI Language
Entity Linking vs Named Entity Recognition
NEW QUESTION # 192
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