Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) - AI-102日本語 Exam Practice Test

Explanation:
In user want to change to deploy offline model
1. Change model domain to compact model
2. Retrain compact model
3. Export model
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/export-your-model

Explanation:

Comprehensive Detailed Explanation
The 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
Microsoft References
Content Moderator - Text moderation (classification categories explained) Content Moderator overview and response schema

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Explanation:

The requirement:
You are using Azure AI Anomaly Detector.
You need to deploy it on Server1, which has intermittent internet access.
This means you need to run the service locally using containers (Docker).
Step 1 - Install Docker Engine
Before pulling or running any containers, Docker must be installed on Server1.
Without Docker Engine, no container can be deployed.
Step 2 - Pull the container image
Use docker pull to download the Anomaly Detector container image from Microsoft Container Registry.
This requires internet access, so it should be done when connectivity is available.
Step 3 - Run the container
After pulling the image, use docker run to start the Anomaly Detector container.
This exposes a local REST API endpoint on Server1.
Step 4 - Query the local endpoint
Once the container is running, you can query the prediction endpoint on Server1.
This allows local inference without requiring continuous internet access.
Correct Sequence:
Install the Docker Engine on Server1.
From Server1, run the docker pull command.
From Server1, run the docker run command.
Query the prediction endpoint on Server1.
Microsoft References
Use Azure AI Anomaly Detector on containers
Deploy Azure AI services in Docker containers
Anomaly Detector container setup

Explanation:

To let an Azure AI Agent access publicly available, recent web content, you add the Bing grounding tool. In the SDK, you first define a ToolConnectionList with the Bing connection ID (here, " bingConnectionId " ), then construct a BingGroundingToolDefinition(connectionList) so the agent can call Bing Search with recency filters (e.g., last 90 days) when answering questions.
When creating the agent, tool definitions are passed via the tools: parameter of CreateAgentAsync(...) as a List < ToolDefinition > . The toolResources parameter is used when you must also supply resource descriptors (for example, an Azure AI Search index configuration), which is not needed here because the Bing grounding tool already references the connection through the ToolConnectionList .
Therefore:
Use BingGroundingToolDefinition to ground the agent on current public web data.
Provide that tool in tools: new List < ToolDefinition > { grounding } when calling CreateAgentAsync(...) .
Microsoft Azure AI References (titles only)
Azure AI Agent Service - Bing web grounding tool (tool definitions and connections) Azure AI Agent Service SDK - Agent creation ( CreateAgentAsync parameters: tools vs toolResources ) Azure AI Agent Service - Tool connections and ToolConnectionList usage

Explanation:

Comprehensive Detailed Explanation
You're creating a resource that will handle sentiment analysis (Language) and OCR (Vision) with a single key and endpoint. That is a Cognitive Services multi-service (Azure AI services) account.
When creating any Azure resource via ARM (HTTP/REST), the URL must be scoped to a subscription, not a tenant, and use the correct resource provider.
Scope segment: Use
https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{rg}/providers/...
Therefore, choose subscriptions/8d3591aa-96b8-4737-ad09-00f9b1ed35ad (not the tenant scope).
Resource provider: Multi-service cognitive account is under Microsoft.CognitiveServices.
The full path to create would be like:
/providers/Microsoft.CognitiveServices/accounts/{accountName}?api-version=2021-10-01 This creates a single Cognitive Services (Azure AI services) resource that provides one endpoint and one key across multiple services (e.g., Language for sentiment analysis and Vision for OCR).
Microsoft Azure AI Solution References
Azure Resource Manager: Create Cognitive Services account (Microsoft.CognitiveServices/accounts) using REST and API version 2021-10-01.
Azure AI services (Cognitive Services) multi-service resource overview-single key/endpoint across services.

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Explanation:

This scenario is about improving the accuracy of QnA Maker responses when user phrasing is different but semantically the same.
Problem
Uploaded document contains: "What is your warranty period?"
Users ask: "How long is the warranty coverage?"
QnA Maker fails because it does not automatically equate these two phrasings.
Both must map to the same answer.
Correct Sequence of Actions
Add alternative phrasing to the question and answer (QnA) pair.
In QnA Maker, you can add synonyms or paraphrases for a question.
Example: Add "How long is the warranty coverage?" as an alternative phrasing to the existing QnA pair.
This ensures the system treats both phrasings as the same question.
Retrain the model.
After modifying the knowledge base with new alternatives, you must retrain the model.
This step incorporates the updates into the QnA Maker service.
Republish the model.
To make the changes live and accessible by the chatbot endpoint, the retrained model must be republished.
Without republishing, the chatbot will still use the old knowledge base.
Actions That Are Not Required
Add a new QnA pair # Not needed, because we want both questions to map to the same existing answer, not duplicate answers.
Add additional questions to the document # That's not required when QnA Maker already supports adding alternative phrasing within the portal or JSON editor.
Final Answer (Sequence)
Add alternative phrasing to the question and answer (QnA) pair.
Retrain the model.
Republish the model.
Microsoft References
QnA Maker - Add alternative phrasing
Train and publish a QnA Maker knowledge base