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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Microsoft Azure AI Solution |
| Exam Number: | AI-102 |
| Exam Price: | USD 165 (varies by country/region) |
| Available Languages: | Korean, Portuguese (Brazil), Spanish (Latin America), Simplified Chinese, French, English, German, Japanese |
| Exam Duration: | 100-120 |
| Passing Score: | 700 (out of 1000) |
| Certificate Validity Period: | 1 year (renewable annually via Microsoft certification renewal assessment) |
| Real Exam Qty: | 40-60 (approximate range) |
| Exam Format: | Multiple choice, Build/review solution scenarios, Drag and drop, Case studies, Multiple response |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals (AI-900) |
| Recommended Training: | Microsoft Learn AI-102 Learning Path Azure AI Engineer Associate Study Resources |
| Exam Registration: | Microsoft Certification Exam Registration (Pearson VUE) Microsoft Learn AI-102 Certification Page |
| Sample Questions: | Microsoft AI-102 Sample Questions |
| Exam Way: | Online proctored or onsite test center (Pearson VUE) |
| Pre Condition: | No formal prerequisites required, but familiarity with Azure fundamentals and AI/ML concepts is recommended |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-engineer/ |
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NEW QUESTION # 65
You use the Custom Vision service to build a classifier.
After training is complete, you need to evaluate the classifier.
Which two metrics are available for review? Each correct answer presents a complete solution. (Choose two.) NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
Explanation
Custom Vision provides three metrics regarding the performance of your model: precision, recall, and AP.
Reference:
https://www.tallan.com/blog/2020/05/19/azure-custom-vision/
NEW QUESTION # 66
You have a chatbot that uses a QnA Maker application.
You enable active learning for the knowledge base used by the QnA Maker application.
You need to integrate user input into the model.
Which four 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
Graphical user interface, application Description automatically generated
Step 1: For the knowledge base, select Show active learning suggestions.
In order to see the suggested questions, on the Edit knowledge base page, select View Options, then select Show active learning suggestions.
Step 2: Approve and reject suggestions.
Each QnA pair suggests the new question alternatives with a check mark, , to accept the question or an x to reject the suggestions. Select the check mark to add the question.
Step 3: Save and train the knowledge base.
Select Save and Train to save the changes to the knowledge base.
Step 4: Publish the knowledge base.
Select Publish to allow the changes to be available from the GenerateAnswer API.
When 5 or more similar queries are clustered, every 30 minutes, QnA Maker suggests the alternate questions for you to accept or reject.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/improve-knowledge-base
NEW QUESTION # 67
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 intent for location.
Does this meet the goal?
Answer: A
Explanation:
The scenario involves a Language Understanding (LUIS) or Conversational Language Understanding (CLU) model that has:
* An intent called FindContact (used for searching a contact list).
* Training phrases like:
* "Find contacts in London"
* "Who do I know in Seattle?"
* "Search for contacts in Ukraine"
Key Requirement The model must recognize locations (London, Seattle, Ukraine) inside the FindContact intent.
* The proposed solution is to create a new intent for location.
* This is not correct because:
* Intents represent what the user wants to do (the action, e.g., FindContact ).
* Entities represent details within the user's utterance (the data, e.g., location ).
* Here, the intent is already defined: FindContact .
* Location should be modeled as an entity (Location entity), not as a separate intent.
Why the Solution is Incorrect Creating a new intent for each location (London, Seattle, Ukraine) would fragment the model and make it harder to generalize. Instead, the model should capture location as a parameter of the single intent FindContact .
* Keep the existing FindContact intent.
* Add an entity for location (prebuilt GeographyV2 in LUIS or "Location" entity in CLU).
* Train the model so that London , Seattle , Ukraine are recognized as entity values inside the FindContact intent.
Correct Approach
* Intents and entities in Language Understanding
* Prebuilt entity: GeographyV2
Microsoft References
NEW QUESTION # 68
You run the following command.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
Box 1: Yes
http://localhost:5000/status
Also requested with GET, this verifies if the api-key used to start the container is valid without causing an endpoint query.
Box 2: Yes
The command saves container and LUIS logs to output mount at C:\output, located on container host Box 3: Yes
http://localhost:5000/swagger
The container provides a full set of documentation for the endpoints and a Try it out feature. With this feature, you can enter your settings into a web-based HTML form and make the query without having to write any code. After the query returns, an example CURL command is provided to demonstrate the HTTP headers and body format that's required.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-container-howto
NEW QUESTION # 69
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 # 70
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