AI-102 Practice Test 17 — 15 Questions

Question 1

A developer is building a multilingual translation solution using Azure AI Translator. The solution must translate text to French, German, and Spanish. Which parameter should the developer set to specify the target language?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

to. In Azure AI Translator, the target language for translation is specified using the 'to' query parameter in the API request. This parameter accepts language codes such as 'fr' for French, 'de' for German, and 'es' for Spanish. The 'to' parameter is required and can be specified multiple times to translate into multiple target languages simultaneously.

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language

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targetLanguage

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from

Question 2

You plan to use Azure AI Search to index a large number of text documents stored in Azure Blob Storage. The documents are in English. You want to automatically extract key phrases from the content during indexing. What should you add to the skillset?

Accepted Answer

Key Phrase Extraction skill. The Key Phrase Extraction skill is the correct choice because it is specifically designed to identify and extract important phrases from text content, which aligns with the requirement to automatically extract key phrases during indexing. This skill is part of Azure AI Search's cognitive skillset and operates on English text to produce a list of key phrases per document.

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Sentiment skill

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Language Detection skill

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Entity Recognition skill

Question 3

You are developing a conversational agent using Microsoft Copilot Studio that must handle complex multi-turn conversations. The agent needs to maintain context across multiple user inputs. Which feature should you use?

Accepted Answer

Use topics with slots to collect information across turns.. In Microsoft Copilot Studio, topics with slots are specifically designed to handle multi-turn conversations by collecting and persisting information across user inputs. Slots allow the agent to prompt for missing details and maintain context within a topic, enabling complex, stateful interactions without external dependencies.

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Use actions to call external APIs for context.

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Use global variables to store conversation state.

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Use entities to capture user input.

Question 4

You are designing a multilingual chatbot using Azure AI Language. The chatbot must support English, Spanish, and French. You need to minimize development effort and ensure consistent intent recognition across languages. What should you do?

Accepted Answer

Use a single CLU multilingual project that supports all three languages.. Azure AI Language's CLU (Conversational Language Understanding) supports multilingual projects, allowing you to train a single model that handles English, Spanish, and French. This minimizes development effort by avoiding separate projects for each language. Option A is wrong because translating all utterances to English adds latency and complexity, and may lose nuances. Option B is wrong because creating separate CLU projects for each language increases maintenance and deployment overhead. Option D is wrong because LUIS does not offer the same multilingual capabilities as CLU and is being deprecated.

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Use the Azure AI Translator to translate all utterances to English before sending to a single English-only CLU project.

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Create a separate CLU project for each language and combine them with a routing mechanism.

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Use the Language Understanding (LUIS) service with a single app that includes language-specific utterances.

Question 5

You are deploying a solution that uses Azure OpenAI Service to generate financial reports. You need to ensure the outputs are accurate and consistent. Which TWO parameters should you adjust? (Choose two.)

Accepted Answer

Set temperature to 0.. Setting temperature to 0 makes the model deterministic, always choosing the most likely next token. This is critical for financial reports where consistency and reproducibility are required, as it eliminates randomness in the output.

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Set presence_penalty to 0.5.

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Set max_tokens to 2000.

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Set frequency_penalty to 0.7.

Question 6

You are developing a custom chatbot using Azure AI Bot Service and Language Understanding (CLU). The chatbot needs to escalate to a human agent when the user's sentiment is negative. Which component should you use to detect sentiment?

Accepted Answer

Azure AI Language sentiment analysis. Azure AI Language sentiment analysis is the correct component because it provides pre-built sentiment detection capabilities that analyze text and return sentiment labels (positive, negative, neutral) and confidence scores. This directly meets the requirement to detect negative user sentiment in chatbot conversations, enabling escalation to a human agent when needed.

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Azure Cognitive Search

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Orchestration workflow

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QnA Maker

Question 7

You deploy a chat application using Azure OpenAI Service. Users report that the model sometimes generates inappropriate content. You need to implement a safety system that can be customized for your organization's policies. What should you use?

Accepted Answer

Use Azure AI Content Safety with custom categories and severity thresholds. Azure AI Content Safety provides a customizable content moderation service that allows you to define custom categories and severity thresholds aligned with your organization's specific policies. This enables you to filter inappropriate content beyond the default filters, giving you granular control over what the model generates.

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Use the content filter system in Azure OpenAI Studio

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Apply responsible AI templates from Azure AI Studio

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Configure Microsoft Entra ID Conditional Access policies

Question 8

You are building a generative AI solution using Azure Machine Learning prompt flow. The solution must allow business analysts without coding experience to modify prompts and evaluate different model versions. What should you do?

Accepted Answer

Use Azure Machine Learning prompt flow with the visual designer and variant management. Azure Machine Learning prompt flow provides a visual designer that enables non-technical users to modify prompts without coding, and its variant management feature allows them to evaluate different model versions side-by-side. This directly addresses the requirement for business analysts to iteratively refine prompts and compare model outputs in a controlled, no-code environment.

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Provide the analysts with a Jupyter notebook using the OpenAI Python SDK

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Deploy a chatbot in Microsoft Copilot Studio and let analysts configure it

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Implement a custom web UI using Azure Static Web Apps and Azure Functions

Question 9

You are using Azure OpenAI Service to generate code snippets for a development team. You notice that the generated code sometimes contains security vulnerabilities. You need to minimize the risk of generating insecure code while maintaining productivity. What should you do?

Accepted Answer

Use system messages to instruct the model to prioritize security. System messages in Azure OpenAI Service allow you to set the context and behavior of the model, including instructing it to prioritize security when generating code. This approach directly influences the model's output without requiring retraining or sacrificing flexibility, making it the most effective way to reduce security vulnerabilities while maintaining productivity.

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Fine-tune the model on a dataset of secure code

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Set the temperature parameter to 0

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Disable content filtering to allow more flexibility

Question 10

You are troubleshooting an agent built with Microsoft Copilot Studio. The agent uses a custom topic to check inventory levels. The topic calls a Power Automate flow that returns JSON with 'inStock' boolean. The agent sometimes says 'Item is in stock' even when the flow returns false. What is the most likely cause?

Accepted Answer

The topic's condition is using a variable that is not being updated with the flow output.. The most likely cause is that the topic's condition is referencing a variable that does not get updated with the flow's output. In Microsoft Copilot Studio, when a Power Automate flow returns data, the output must be explicitly assigned to a topic variable. If the condition checks a different variable (e.g., a default or uninitialized one), it will not reflect the actual 'inStock' value from the flow, leading to incorrect responses like 'Item is in stock' even when the flow returns false.

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The Power Automate flow has a timeout and returns default true.

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The agent's response is based on a different variable that defaults to true.

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The agent's topic is not parsing the JSON output correctly.

Question 11

Which THREE components are required to implement a Retrieval-Augmented Generation (RAG) solution with Azure OpenAI Service? (Choose three.)

Accepted Answer

An embedding model (e.g., text-embedding-ada-002). An embedding model like text-embedding-ada-002 is essential for converting user queries and document chunks into dense vector representations. These vectors enable semantic similarity search in a vector database, which is the core retrieval step in RAG. Without embeddings, the system cannot match user intent to relevant content.

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A fine-tuned model

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Azure AI Content Safety

Question 12

You are building an agent using Microsoft Copilot Studio to handle customer returns. The agent must collect the order ID, reason for return, and then provide a return shipping label. The process requires the user to provide information step-by-step. Which type of conversation flow should you implement?

Accepted Answer

Use multiple question nodes in a sequential flow.. Microsoft Copilot Studio uses 'Question' nodes to collect user input one piece at a time in a sequential flow, which matches the step-by-step requirement for order ID, reason, and shipping label. This approach ensures each piece of data is validated before moving to the next, maintaining a guided conversation.

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Use an adaptive card to collect all inputs in one step.

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Use a single question node to collect all information at once.

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Use a generative answers node to parse the user's intent.

Question 13

You run the above PowerShell command to check the responsible AI policy applied to an Azure OpenAI Service deployment. The output shows 'MyPolicy'. You need to verify that the policy blocks hate speech. What should you do?

Accepted Answer

Navigate to Azure AI Content Safety and view the policy 'MyPolicy'.. Azure AI Content Safety is the dedicated service for managing and viewing the details of content filtering policies, including custom policies like 'MyPolicy'. The PowerShell command only confirms the policy name is applied to the deployment; to verify that the policy blocks hate speech, you must inspect the policy's configuration (e.g., severity thresholds for hate categories) within the Azure AI Content Safety portal. The Azure Portal deployment properties only show the policy name, not its rules.

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Use the Azure Portal to review the deployment's properties.

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Run Get-AzCognitiveServicesAccountDeployment with -ExpandProperties.

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Use Azure Monitor to check the deployment's logs.

Question 14

Which THREE factors should you consider when choosing between Azure AI Custom Vision and Azure AI Vision pre-built models for an image classification task? (Choose three.)

Accepted Answer

Availability of labeled training data specific to the domain. Azure AI Custom Vision is specifically designed for scenarios where you have domain-specific labeled training data that is not covered by pre-built models. Custom Vision allows you to upload your own labeled images and train a custom model tailored to your unique classification needs, which is essential when off-the-shelf models fail to recognize your target classes.

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Image format support (JPEG, PNG)

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Need for real-time inference latency

Question 15

You are building a generative AI application using Azure OpenAI Service. You need to ensure that the application handles user data securely. Which TWO practices should you implement?

Accepted Answer

Disable data logging in the Azure OpenAI Service resource. Disabling data logging in Azure OpenAI Service ensures that prompts and completions are not stored by the service for monitoring or abuse detection. This is a key security practice for handling sensitive user data, as it prevents inadvertent retention of confidential information. By default, Azure OpenAI may log data for operational purposes, so explicitly disabling logging is necessary to meet data privacy requirements.

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Store prompts and completions in Azure Blob Storage

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Use system messages to instruct the model not to store data

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Configure content filtering to block sensitive data