AI-102 Plan and manage an Azure AI solution Practice Test 16 — 15 Questions

Question 1

Your team is developing a chatbot using Azure AI Bot Service. You need to ensure that the bot can handle multiple languages and respond appropriately. Which Azure AI service should you integrate to perform language detection?

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

Accepted Answer

Azure AI Language. Azure AI Language provides pre-built language detection capabilities as part of its natural language processing (NLP) features. By integrating this service, the bot can analyze incoming text and identify the language, enabling it to route responses appropriately or trigger language-specific logic.

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Azure AI Speech

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Azure AI Content Safety

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Azure AI Translator

Question 2

You are testing an Azure OpenAI Service chat completion with function calling. The assistant returned null content and a tool call. What does this indicate?

Accepted Answer

The model is using a tool to answer the question, so content is null. In Azure OpenAI Service function calling, when the model determines that it needs to invoke a tool (e.g., an external API or database query) to fulfill the user's request, it returns a `tool_calls` object with `content` set to `null`. This is the expected behavior—the model is signaling that it is delegating the response to the tool, not that it has failed or is blocked.

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The model is unable to answer the question and returned an error

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The system message is preventing the model from responding

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The function call syntax is invalid and the model skipped it

Question 3

You are deploying a custom text classification model using Azure AI Language. The model must be retrained monthly with new labeled data. You need to automate the retraining process with minimal manual intervention. Which approach should you use?

Accepted Answer

Use the Azure AI Language REST API to trigger training and deployment on a schedule using Azure Logic Apps. Azure Logic Apps can schedule HTTP requests to the Azure AI Language REST API to trigger training and deployment of a custom text classification model. This approach automates the monthly retraining process without manual intervention, using the API's capabilities for model creation, training, and deployment.

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Create an Azure DevOps pipeline that manually retrains the model every month

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Retrain the model manually using Language Studio each month

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Use Azure Machine Learning to host the custom model and automate retraining

Question 4

Your organization uses Microsoft Entra ID for identity management. You are building an AI solution that uses Azure AI Vision to analyze images. The solution must use managed identities to authenticate to the Vision resource. Which RBAC role should you assign to the managed identity?

Accepted Answer

Cognitive Services User. The Cognitive Services User role (B) is the correct RBAC role because it grants the minimum required permissions for a managed identity to call Azure AI Vision APIs (e.g., analyze image, OCR) without allowing any write or management operations. This role is specifically designed for accessing Azure Cognitive Services endpoints, and it aligns with the principle of least privilege for authentication via managed identities.

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Owner

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Reader

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Contributor

Question 5

You are planning to use Azure AI Content Safety to moderate user-generated content in a social media application. The solution must detect hate speech and self-harm content. Which Content Safety features should you enable?

Accepted Answer

Hate and self-harm content filters. Azure AI Content Safety provides pre-built filters for specific harm categories, including hate speech and self-harm. Enabling the 'Hate and self-harm content filters' directly activates the detection models for these categories, meeting the requirement without needing custom categories or additional features.

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Severity levels for all categories

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Custom categories for hate speech and self-harm

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Image moderation

Question 6

Which TWO Azure AI services can you use to implement a custom question-answering system?

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service (A) is correct because it lets you build a custom question-answering system by grounding a chat/completions model on your own data (e.g., via the On Your Data feature or by supplying retrieved context), enabling natural-language answers over proprietary content. Azure AI Language (D) is correct because it includes the Custom Question Answering feature (formerly QnA Maker), which builds a knowledge base from documents, URLs, and question-answer pairs and exposes it through a REST API for question answering. Azure AI Bot Service (B) is a framework for hosting and connecting conversational bots, not a question-answering knowledge service, so it does not itself implement custom Q&A. Azure AI Translator (C) only performs text translation between languages and has no question-answering capability. Azure AI Search (E) is a search/indexing service that can retrieve relevant documents but does not generate or manage question-answer pairs on its own, so it is not one of the two services for implementing custom question answering.

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Azure AI Bot Service

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Azure AI Translator

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Azure AI Search

Question 7

You executed the Azure CLI command to list Azure OpenAI resources. You need to programmatically access the endpoint of the resource named 'gpt4' in a script. What is the most reliable way to extract the endpoint?

Accepted Answer

Use az cognitiveservices account show with --query to filter by name. `az cognitiveservices account show` with the `--query` parameter allows you to retrieve the specific endpoint property for a named resource using JMESPath filtering. This approach is reliable, scriptable, and avoids parsing unstructured output, which is error-prone. The Azure CLI returns structured JSON, and `--query` extracts the exact value without manual string manipulation.

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Use Azure PowerShell Get-AzCognitiveServicesAccount cmdlet

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Parse the table output using string manipulation

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Deploy a new Azure OpenAI resource with a known endpoint

Question 8

You need to monitor the costs of your Azure AI services across multiple subscriptions. Which Azure tool should you use to track spending and set budgets?

Accepted Answer

Azure Cost Management. Azure Cost Management is the dedicated tool for monitoring, analyzing, and controlling cloud spending across multiple subscriptions. It provides cost analysis, budget creation, and alerting capabilities specifically designed for tracking Azure AI services costs at scale.

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Azure Portal

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Azure Monitor

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Azure Advisor

Question 9

You are designing an AI solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must handle a high volume of documents with varying layouts. Which approach should you use?

Accepted Answer

Train a custom neural model with labeled invoices. Custom neural models in Azure AI Document Intelligence are designed to handle high volumes of documents with varying layouts. Unlike fixed template models, neural models learn from labeled examples and generalize across different invoice structures, making them ideal for diverse, high-volume scenarios.

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Use a custom template model with fixed field positions

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Use the prebuilt invoice model

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Extract text using OCR and then use regex parsing

Question 10

Your team is developing a chatbot using Azure AI Bot Service with Language Understanding (LUIS). You need to improve intent recognition for user queries that contain typos. What should you recommend?

Accepted Answer

Enable Bing Spell Check in the LUIS app settings. Enabling Bing Spell Check in the LUIS app settings allows the service to automatically correct typos in user utterances before they are processed for intent recognition. This built-in feature integrates directly with LUIS at the endpoint, correcting misspelled words to improve the accuracy of intent and entity extraction without requiring manual data augmentation or external services.

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Add synonym lists for common misspellings

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Use Azure Cognitive Search to correct typos

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Train the model with many examples containing typos

Question 11

You are planning to deploy an Azure AI Content Safety solution. What is the primary requirement for using the service?

Accepted Answer

An active Azure subscription. An active Azure subscription is the primary requirement because Azure AI Content Safety is a cloud-based service that requires a valid subscription for resource provisioning, API access, and billing. Without an Azure subscription, you cannot create the Content Safety resource or authenticate API calls, regardless of other configurations.

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Data must be stored in the same region as the service

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All users must have Microsoft Entra ID P2 licenses

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The application must be written in C#

Question 12

You need to monitor the performance of an Azure AI Language service custom entity recognition model. Which metric should you track to evaluate the model's ability to correctly identify entities?

Accepted Answer

F1 score. The F1 score is the standard metric for evaluating custom entity recognition models in Azure AI Language, as it balances precision (correctly identified entities) and recall (missed entities). Unlike accuracy, which can be misleading due to class imbalance in entity labeling, F1 provides a harmonic mean that reflects the model's ability to correctly identify entities without bias toward the majority class.

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Throughput

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Response latency

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Accuracy

Question 13

Which TWO actions should you take when designing an Azure AI solution that uses Microsoft Foundry to ensure responsible AI practices?

Accepted Answer

Implement a human-in-the-loop review for critical decisions. Option A is correct because implementing a human-in-the-loop review for critical decisions ensures that high-impact or sensitive outcomes are validated by a person before action is taken, which is a core responsible AI safeguard against harmful or erroneous automated decisions. Option C is correct because running an AI fairness assessment on the model systematically evaluates performance across demographic groups and helps detect and mitigate bias, directly supporting Microsoft's responsible AI principles of fairness and inclusiveness. Option B does not belong because optimizing for maximum throughput is a performance and cost concern, not a responsible AI practice, and can even conflict with safety if it bypasses safeguards. Option D does not belong because retaining all training data indefinitely raises privacy, data minimization, and compliance risks rather than promoting responsible AI. Option E does not belong because removing explainability metrics reduces transparency and accountability, which is the opposite of responsible AI design.

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Optimize the model for maximum throughput

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Store all training data indefinitely for auditability

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Remove all explainability metrics to simplify the model

Question 14

Which TWO Azure AI services can be used together to build a solution that transcribes customer service calls and detects sentiment?

Accepted Answer

Azure AI Language (sentiment analysis). Azure AI Speech (option B) is correct because its speech-to-text capability transcribes the audio of customer service calls into text, which is the required first step for this solution. Azure AI Language (option A) is correct because its sentiment analysis feature evaluates the transcribed text and returns sentiment scores/labels (positive, negative, neutral, mixed), satisfying the detection requirement. Together, B feeds transcription output into A for sentiment evaluation. Option C (Azure AI Translator) is not needed since the scenario does not require language translation. Option D (text-to-speech) is the reverse of what is needed—it synthesizes speech from text rather than transcribing calls. Option E (conversational language understanding) extracts intents and entities for conversational apps, not sentiment, so it does not fulfill the requirement.

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Azure AI Translator

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Azure AI Speech (text-to-speech)

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Azure AI Language (conversational language understanding)

Question 15

Refer to the exhibit. You are creating a Custom Vision project using the Azure AI Custom Vision API. The training data is stored in Azure Blob Storage with a SAS URI. The project creation fails with an authorization error. What is the most likely reason?

Accepted Answer

The SAS token is expired or has insufficient permissions. The Custom Vision project creation fails because the SAS URI used to access training data in Azure Blob Storage has an expired token or lacks sufficient permissions (e.g., read/list). Custom Vision requires a valid SAS token with at least read and list permissions to import images from the container. An expired or under-permissioned SAS token results in an authorization error when the service attempts to access the blob storage.

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The domain 'general' is invalid

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The exportModelContainerUri is missing

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The project type 'Classification' is not supported