AI-102 Practice Exam Questions and Answers

When working with Azure OpenAI Studio to configure a chatbot or conversational AI model, two key parameters control the creativity vs determinism of responses:

Temperature

Controls the amount of randomness in the model’s output.

A lower value (closer to 0) makes the output more deterministic and focused.

A higher value increases creativity and randomness.

For deterministic responses, you should reduce this value (for example, 0.2 or 0.0).

Top P (nucleus sampling)

Controls the diversity of responses by limiting the probability space considered.

A lower value (closer to 0) makes the responses more deterministic by narrowing the token choices.

A higher value increases creativity by expanding possible outputs.

For deterministic responses, keep this value low (e.g., 0.1–0.3).

Max response: Sets maximum token length of the output, not determinism.

Stop sequence: Defines custom stopping conditions, not randomness.

Frequency penalty and Presence penalty: Control repetition and encouragement of new topics, not determinism.

Why Not Other Parameters?Thus, the correct parameters to adjust for more deterministic and less creative responses are:

Temperature and Top P.

Correct Answer: Temperature, Top P

Azure OpenAI Service – Parameters

Azure OpenAI Studio Overview

Nucleus Sampling (Top P) explanation

Microsoft References

 SpeechRecognitionLanguage must be set to "fr" (French).

 AddTargetLanguage("de") must be used to specify German as the translation output.

Correct Code Completionvar speechTranslationConfig =

SpeechTranslationConfig.FromSubscription(speechKey, speechRegion);

speechTranslationConfig.SpeechRecognitionLanguage = "fr";

speechTranslationConfig.AddTargetLanguage("de");

SpeechRecognitionLanguage → sets the source spoken language that the Speech service listens to (here, French).

AddTargetLanguage("de") → adds German as the target translation language.

SpeechSynthesisLanguage would be used if you want to speak out the translation, but since the requirement is only about defining input and output languages, we use AddTargetLanguage.

Explanation

Speech translation with Speech SDK

Microsoft Reference

Final Answer:

First blank → SpeechRecognitionLanguage

Second blank → AddTargetLanguage

You are building an application that:

Uses Azure Translator service to translate text.

The text comes from getTextToBeTranslated().

The text can be in many languages.

Requirement: the content must remain within the Americas Azure geography.

Output: must translate text into a single language (English in this case).

The Translator service provides region-specific endpoints.

For Americas geography, you must use the North America (NAM) endpoint:

https://api-nam.cognitive.microsofttranslator.com/translate

Other options like api-apc (Asia Pacific) or api-nam.../detect are incorrect because:

/detect is used to identify the source language, not translate.

/transliterate is for script conversion, not translation.

We need the /translate path.

Step 1 – Choosing the correct endpoint

To specify the target language, you must use the to query string parameter. Example:

?to=en

Options like from=en or suggestedFrom=en are not correct here, because:

The text can be in many languages → we don’t know the source language in advance.

Translator automatically detects the source if from is not specified.

We only need to define the target language.

Step 2 – Setting the correct query parameter

Final Code Snippet (corrected)var endpoint = "https://api-nam.cognitive.microsofttranslator.com/translate ";

var apiKey = "YOUR_SUBSCRIPTION_KEY";

var text = getTextToBeTranslated();

var body = "[{'Text':'" + text + "'}]";

var client = new HttpClient();

client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", apiKey);

var uri = endpoint + "?to=en";

HttpResponseMessage response;

var content = new StringContent(body, Encoding.UTF8, "application/json");

response = await client.PutAsync(uri, content);

Endpoint: https://api-nam.cognitive.microsofttranslator.com/translate

URI: ?to=en

Correct Answer

Translator v3.0 Reference

Region-specific Translator endpoints

Microsoft References

To detect harmful content with Azure AI Content Safety using the .NET SDK, you instantiate a ContentSafetyClient with the endpoint and key. For text moderation, you construct an AnalyzeTextOptions object with the input string (and optionally specify categories, blocklists, etc.). You then call client.AnalyzeText(AnalyzeTextOptions options) and inspect the result (e.g., categories analysis and blocklist matches).

Code completed:

var client = new ContentSafetyClient(new Uri(endpoint), new AzureKeyCredential(key));

var request = new AnalyzeTextOptions("what is the weather forecast for Seattle");

Response response = client.AnalyzeText(request);

Microsoft Azure AI References

Azure AI Content Safety – SDK usage for text analysis with ContentSafetyClient and AnalyzeTextOptions (Analyze Text API).

Azure AI Content Safety – Concepts: text categories (hate/sexual/violence/self-harm) and blocklists, returned in AnalyzeTextResult.

Comprehensive Detailed ExplanationThe scenario:

You are building an Azure WebJob to create knowledge bases from an array of URLs.

You already have a QnAMakerClient object instantiated with the relevant API keys.

You need to know what steps are required to create a knowledge base programmatically.

CreateKbDTO is the request payload for creating a knowledge base.

It contains the name of the knowledge base and its sources, which may include:

A list of URLs.

A list of QnA pairs (QnADTO).

A list of files (FileDTO).

In this case, since the WebJob provides URLs, they will be passed inside the CreateKbDTO.Sources property.

Once the CreateKbDTO object is defined, you use the QnAMakerClient to send the request:

var response = await client.Knowledgebase.CreateAsync(createKbDTO);

This triggers the asynchronous creation of a new knowledge base in the QnA Maker service.

A.  

Create a list of FileDTO objects: Not required here, because the question specifies URLs, not files.

C.  

Create a list of QnADTO objects: Also not needed, since you’re not directly supplying QnA pairs; the knowledge base will be built from URLs.

Step 1 – Create a CreateKbDTO objectStep 2 – Call client.Knowledgebase.CreateAsyncWhy not A or C?

Correct Answer

B.  

D.  

QnA Maker – Create Knowledge Base API

QnAMakerClient Class – Azure SDK for .NET

CreateKbDTO Class

Microsoft References

You are securing an Azure Cognitive Search resource with two specific requirements:

Prevent access to Search1 from the internet

The correct way is to use a private endpoint.

A private endpoint connects the Cognitive Search resource directly into your virtual network (VNet), blocking public internet access.

Configuring an IP firewall can restrict access to specific IP ranges but does not fully prevent internet access.

Azure roles and authentication do not address network-level restrictions.

Limit the access of each app to specific queries

This is done via Azure role-based access control (RBAC).

You can assign different Azure roles or API keys with query rights to control what each app can do.

Key authentication alone only provides coarse-grained access (admin/query keys) but does not allow fine-grained per-app restrictions.

RBAC ensures principle of least privilege is enforced.

Correct Answer:

Prevent access from internet → Create a private endpoint

Limit access to queries → Use Azure roles

Secure access to Azure Cognitive Search using private endpoints

Role-based access control in Azure Cognitive Search

Microsoft References