AI-102 Exam Dumps : Designing and Implementing a Microsoft Azure AI Solution

Explanation:

Scenario: When the response confidence score is low, ensure that the chatbot can provide other response options to the customers.

When no good match is found by the ranker, the confidence score of 0.0 or "None" is returned and the default response is "No good match found in the KB". You can override this default response in the bot or application code calling the endpoint. Alternately, you can also set the override response in Azure and this changes the default for all knowledge bases deployed in a particular QnA Maker service.

Choosing Ranker type: By default, QnA Maker searches through questions and answers. If you want to search through questions only, to generate an answer, use the RankerType=QuestionOnly in the POST body of the GenerateAnswer request.

Data visualization’s primary purpose is to make data understandable and meaningful through visuals such as charts, dashboards, and graphs.

It helps to communicate significance (option A) and show patterns, correlations, and trends over time (option B).

C. Implement machine learning to predict future values → Not visualization, but rather ML/AI.

D. Enforce business logic across reports → Business logic is handled in data modeling or ETL, not visualization.

Correct Answers: A and B

When deploying an Azure OpenAI model using the Microsoft.CognitiveServices/accounts/deployments resource in an ARM template, the throughput for a Standard deployment is set on the SKU object. Specifically:

Use the capacity field of the SKU to specify the per-deployment rate.

Provide the integer value that represents the requests per minute (RPM) you want the deployment to handle.

Therefore, to meet the requirement of 600 requests per minute, complete the template as:

"sku": {

"name": "Standard",

"capacity": 600

}

This uses a single deployment SKU with a defined capacity. The ARM schema for accounts/deployments explicitly includes sku.capacity for resources that support scale (Azure OpenAI deployments do), and Azure OpenAI quota/rate-limit guidance confirms that rate limits are assigned per deployment.

Microsoft Azure AI Solution References

ARM template—Microsoft.CognitiveServices/accounts/deployments (2023-05-01): property reference showing sku.capacity. Microsoft Learn

Resource type reference—latest accounts/deployments: confirms SKU usage on deployments. Microsoft Learn

Azure OpenAI quota and rate limits: explains assigning deployment-level limits (RPM/TPM). Microsoft Learn