AI-102 Practice Test 5 — 25 Questions

Question 1

Refer to the exhibit. A developer is configuring a QnA Maker skill for a bot. The skill fails to respond to queries. What is the most likely issue?

Accepted Answer

The modelUrl points to the authoring API instead of the runtime endpoint.. The modelUrl uses the v4.0 API, but the endpoint key and kbId are hardcoded and may be invalid or expired. Additionally, the URL should be for the runtime endpoint, not the authoring API.

Answer

The kbId is not published.

Answer

The skill is not deployed.

Answer

The endpointKey is incorrect.

Question 2

You are designing a generative AI solution that uses Azure OpenAI Service. The solution must generate code snippets in Python and JavaScript. You need to ensure the model reliably outputs code in the correct language based on user input. Which approach should you use?

Accepted Answer

Use a system message to specify the desired language.. System messages in Azure OpenAI Service allow you to set the context or behavior of the model, such as specifying the desired programming language for code generation. This approach is lightweight, requires no retraining, and reliably guides the model to output code in the correct language based on the user's request, leveraging the model's existing training on both Python and JavaScript.

Answer

Set the top_p parameter to a low value.

Answer

Fine-tune the model on a dataset of code in both languages.

Answer

Set the temperature to 0 to make the model deterministic.

Question 3

You are deploying a generative AI application. You have the JSON configuration above. You need to ensure that the model stops generating output at the end of each sentence. The current configuration uses a stop sequence of "
". What should you do?

Accepted Answer

Change the stop sequence to [".", "!" , "?"]. The goal is to stop generation at the end of each sentence. A sentence can end with a period (.), exclamation mark (!), or question mark (?). The current stop sequence of "
" only stops at newline characters, which does not guarantee sentence boundaries. By changing the stop sequence to an array containing [".", "!", "?"], the model will halt output whenever it generates any of these punctuation marks, ensuring each sentence is complete.

Answer

Reduce the maxTokens to 500.

Answer

Set the topP to 0.5.

Answer

Set the temperature to 0.

Question 4

You are deploying an Azure AI solution that uses multiple services. You need to manage access keys securely. Which TWO methods should you use?

Accepted Answer

Use Azure Key Vault to store the keys.. Azure Key Vault is the recommended service for securely storing and managing access keys, secrets, and certificates. By storing AI service keys in Key Vault, you centralize secret management, control access via Azure RBAC, and avoid hardcoding credentials in code or configuration files. This aligns with the principle of least privilege and reduces the risk of accidental exposure.

Answer

Embed the keys directly in application code.

Answer

Store keys in Azure App Configuration.

Answer

Use Key Vault's automatic key rotation feature.

Question 5

Refer to the exhibit. You have trained an object detection model in Azure Custom Vision. The model is published as 'defect-model'. You need to deploy this model to a Docker container for on-premises inference using the Azure IoT Edge runtime. What should you do first?

Accepted Answer

Export the model as a Docker container (e.g., TensorFlow) using the Custom Vision portal.. To deploy a Custom Vision model to an Azure IoT Edge device, you must first export the model as a Docker container (e.g., TensorFlow, ONNX, or DockerFile) from the Custom Vision portal. This export creates a container image that can be deployed to Azure Container Registry and then used as a module in an IoT Edge deployment. Without this export step, you cannot create the containerized module required for on-premises inference.

Answer

Create an Azure Container Registry and push the Custom Vision base image.

Answer

Use the Custom Vision prediction API to call the published endpoint from the edge device.

Answer

Retrain the model with more images to improve mAP.

Question 6

Refer to the exhibit. A developer tries to deploy a custom speech model but receives an error. What should the developer do to resolve the issue?

Accepted Answer

Specify a specific version number for the model.. The error states that 'latest' is not supported; a specific version must be specified.

Answer

Use a different model format.

Answer

Use the model version 'current' instead of 'latest'.

Answer

Re-train the model with more data.

Question 7

You are building a chatbot that must handle customer inquiries about order status. The solution must use Azure AI Language and support multiple languages. You need to configure the project to detect language automatically from user input. Which setting should you enable?

Accepted Answer

Enable multi-lingual detection in the project language settings.. Azure AI Language's multi-lingual detection setting enables the project to automatically identify the language of user input without requiring explicit language selection. This is essential for a chatbot handling customer inquiries in multiple languages, as it allows the service to route the input to the appropriate language-specific model or processing pipeline, ensuring accurate order status responses across languages.

Answer

Set the primary language to English only.

Answer

Use the conversation summarization feature.

Answer

Enable spelling correction in the project settings.

Question 8

Which TWO Azure AI services can be used to extract text from images and PDFs? (Select two.)

Accepted Answer

Azure AI Vision OCR. Azure AI Vision OCR (Optical Character Recognition) is designed to extract printed and handwritten text from images, while Azure AI Document Intelligence (formerly Form Recognizer) specializes in extracting text, tables, and key-value pairs from documents, including PDFs. Both services provide robust text extraction capabilities from visual sources, making them the correct choices.

Answer

Azure AI Translator

Answer

Azure AI Search

Answer

Azure AI Language

Question 9

Your organization uses Microsoft Purview to govern AI models. You need to ensure that data used for training complies with internal policies. What should you configure?

Accepted Answer

Set up Data Estate Insights to monitor data usage. Data Estate Insights in Microsoft Purview provides monitoring and analytics over data estate health, including data usage patterns. By configuring Data Estate Insights, you can track how training data is accessed and used, enabling you to verify compliance with internal policies on data consumption for AI model training.

Answer

Use Microsoft Purview Data Map to catalog training data

Answer

Apply sensitivity labels to training data

Answer

Enable Microsoft Purview Information Protection

Question 10

A company is deploying an Azure AI solution that uses Azure Cognitive Services. The solution must comply with data residency requirements that mandate all customer data be stored within a specific geographic region. Which action should the company take when creating the Cognitive Services resource?

Accepted Answer

Select the appropriate region during resource creation.. Data residency requirements are satisfied by physically storing customer data within a specific geographic boundary. When creating an Azure Cognitive Services resource, selecting the appropriate region (e.g., 'West Europe' or 'East US') during the provisioning process ensures that all data processed and stored by that service instance remains within that Azure datacenter region. This is the fundamental and only guaranteed method to enforce data residency at the resource level.

Answer

Apply a resource tag that specifies the region.

Answer

Configure the endpoint URL to point to a regional endpoint.

Answer

Set the SKU to a tier that supports regional restrictions.

Question 11

You have an Azure AI solution that uses Azure AI Language to perform sentiment analysis. The solution is experiencing high latency. Which action should you take to reduce latency?

Accepted Answer

Scale the service by increasing the number of instances or using a higher pricing tier.. Scaling the Azure AI Language service by increasing the number of instances or moving to a higher pricing tier (e.g., from Standard S0 to a tier with higher throughput) directly addresses high latency by providing more capacity to handle concurrent requests. High latency often results from hitting the service's rate limits or throughput constraints, and scaling alleviates this bottleneck without changing the underlying architecture.

Answer

Move the service to a different Azure region.

Answer

Use the Free tier of the Azure AI Language service.

Answer

Increase the request timeout value.

Question 12

You are deploying an Azure AI multi-agent solution on Microsoft Foundry. The solution uses three agents that must share context and pass tasks among themselves. To ensure reliable orchestration and traceability, which agent pattern should you implement?

Accepted Answer

Sequential agent pattern. The sequential agent pattern is correct because the scenario requires agents to share context and pass tasks among themselves in a reliable, traceable manner. In Microsoft Foundry, the sequential pattern ensures each agent executes in a defined order, passing state and results to the next agent, which provides clear orchestration and full traceability of the workflow.

Answer

Parallel agent pattern

Answer

Sub-agent pattern

Answer

Custom agent pattern

Question 13

You need to analyze the sentiment of social media posts in real time using Azure AI Language. Which approach should you use?

Accepted Answer

Call the sentiment analysis REST API for each post. The sentiment analysis REST API in Azure AI Language is designed for real-time, per-document analysis. By calling the API for each social media post as it arrives, you achieve the lowest latency and can process posts individually without batching or streaming overhead, which is essential for real-time sentiment analysis.

Answer

Use Azure AI Search with cognitive skills

Answer

Use the batch processing feature in Azure AI Language

Answer

Send posts to an Event Hub and use Stream Analytics

Question 14

Your company uses Microsoft 365 Copilot to generate meeting summaries. Some users report that summaries include information from meetings they did not attend. What is the most likely cause?

Accepted Answer

Users have access to meeting artifacts via shared calendars or transcripts.. Microsoft 365 Copilot generates meeting summaries by aggregating content from meeting artifacts such as transcripts, recordings, and shared calendars. If a user has access to a meeting's transcript or recording (e.g., via a shared calendar or because the meeting was recorded and stored in a location the user can access), Copilot can include that meeting's information in summaries even if the user did not attend. This behavior is by design, as Copilot respects existing permissions on the underlying data.

Answer

The meeting organizer granted everyone in the organization view access.

Answer

Copilot is using Bing search results to augment summaries.

Answer

Copilot is incorrectly configured to ignore meeting permissions.

Question 15

You are deploying a generative AI solution that uses DALL-E to generate images. The application must ensure that generated images do not contain violent content. Which feature should you enable?

Accepted Answer

Configure Azure AI Content Safety to moderate images. Azure AI Content Safety is a dedicated service for detecting and moderating harmful content, including violence, in images and text. By integrating this service into your DALL-E image generation pipeline, you can scan generated images for violent content before they are delivered to users, ensuring compliance with safety policies. This is the correct approach because Azure AI Content Safety provides pre-built, customizable content moderation models specifically designed for this purpose.

Answer

Use grounding with Azure AI Search

Answer

Fine-tune the DALL-E model

Answer

Enable content filtering in DALL-E

Question 16

A company uses Azure OpenAI to generate product descriptions. They want to ensure that the descriptions are consistent in style and tone. Which strategy should they use?

Accepted Answer

Provide a few examples of desired style in the prompt (few-shot learning).. Few-shot learning (option B) is the correct strategy because it directly controls style and tone by providing examples of desired output within the prompt. This leverages the model's in-context learning ability without modifying the underlying model weights, making it ideal for enforcing consistency without the cost and complexity of fine-tuning.

Answer

Fine-tune the model on a dataset of product descriptions.

Answer

Set max_tokens to a small value to limit output length.

Answer

Increase the temperature to 1.0 for more creativity.

Question 17

You have the above Azure AI Search skillset. The indexer fails with the error 'The skill 'sentiment-skill' cannot find the input '/document/pages/*' because the path does not exist.' What is the most likely cause?

Accepted Answer

The SplitSkill did not split the content because the document content is too short.. The error indicates that the input path '/document/pages/*' does not exist. This path is generated by the SplitSkill when it splits the document content into pages. If the SplitSkill does not produce any output, the path will not exist. The most likely cause is that the document content is too short to be split, so no pages are created. Therefore, option A is correct. Option B is incorrect because the SplitSkill is defined. Option C is incorrect because the SentimentSkill's input path is correct, but the source is missing. Option D is incorrect because the error is about a missing path, not a misspelled targetName.

Answer

The SplitSkill is not defined in the skillset.

Answer

The SentimentSkill is missing a required input.

Answer

The targetName in SplitSkill is misspelled.

Question 18

You are testing a Conversational Language Understanding application. You send the JSON request shown in the exhibit. What is the purpose of this request?

Accepted Answer

Analyze the utterance for intent and entities.. The JSON request sends a user utterance to a Conversational Language Understanding (CLU) endpoint, which is designed to analyze natural language input. The response will include the predicted intent (e.g., 'GetWeather') and extracted entities (e.g., 'location: Seattle'), fulfilling the core function of CLU: intent and entity recognition. This is not a generative or translation task; it is a classification and extraction operation.

Answer

Translate the text to another language.

Answer

Generate a response to the user.

Answer

Summarize the conversation.

Question 19

A company plans to deploy a Copilot Studio agent to Microsoft Teams. The agent should be available to all employees in the company. The security team requires that only authenticated users from the company's Microsoft Entra ID tenant can access the agent. Which channel configuration should be used?

Accepted Answer

Publish the agent to the Teams channel and configure authentication to require Microsoft Entra ID with the company's tenant ID.. Publishing the Copilot Studio agent to the Teams channel and configuring authentication to require Microsoft Entra ID with the company's tenant ID ensures that only authenticated users from that specific tenant can access the agent. This meets the security requirement by restricting access to the company's Entra ID tenant, while the Teams channel provides native integration for all employees.

Answer

Publish the agent to the Direct Line channel and embed it in a Teams tab.

Answer

Publish the agent to the Web channel and share the link in Teams.

Answer

Publish the agent to the Teams channel and turn off authentication.

Question 20

Which TWO Azure AI services provide capabilities to detect and analyze faces in images? (Choose two.)

Accepted Answer

Azure AI Face API. Azure AI Face API (B) is correct because it is specifically designed for face detection, recognition, and analysis, including attributes like age, emotion, and landmarks. Azure AI Vision Image Analysis (D) is correct because it includes the 'Detect Faces' capability within its broader image analysis feature set, allowing extraction of face bounding boxes and attributes without requiring a dedicated Face API resource.

Answer

Azure AI Custom Vision

Answer

Azure AI Document Intelligence

Answer

Azure Video Indexer