AI-102 Practice Test 38 — 15 Questions

Question 1

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.

Answer

Azure Portal

Answer

Azure Monitor

Answer

Azure Advisor

Question 2

Which TWO components are required to create a custom text classification model in Azure AI Language?

Accepted Answer

A set of labeled documents. Option A (a set of labeled documents) is correct because custom text classification in Azure AI Language is a supervised learning feature that requires training data in the form of documents tagged with the custom categories (labels) you want the model to learn. Option C (a project in Azure AI Language) is correct because you must create a custom text classification project in Azure AI Language (via Language Studio or the REST API) to hold your dataset, labels, training configuration, and deployed model. Option B (a QnA Maker knowledge base) is incorrect because QnA Maker is for building question-and-answer bots, not for training custom classification models. Option D (a Language Understanding (LUIS) app) is incorrect because LUIS is a separate conversational language understanding service for intents and entities, not custom text classification. Option E (an Azure Functions app) is incorrect because Azure Functions is a serverless compute service and is not a required component for creating or training a custom text classification model.

Answer

A QnA Maker knowledge base

Answer

A Language Understanding (LUIS) app

Answer

An Azure Functions app

Question 3

Which THREE factors should you consider when designing a knowledge mining solution that uses Azure AI Search and custom skills to extract insights from large volumes of documents?

Accepted Answer

The maximum execution time of the custom skill must fit within the indexer timeout. Option B is correct because custom skills run inside the indexer execution pipeline, and each skill invocation must complete within the indexer's timeout limits (for example, the default HTTP timeout of 3 minutes 30 seconds for a WebApiSkill); a long-running custom skill will cause the indexer to fail or time out. Option C is correct because enabling incremental enrichment (by setting the indexer's data source change detection and using a high-water mark) lets Azure AI Search skip documents whose content has not changed, avoiding unnecessary re-invocation of expensive custom skills and reducing cost and processing time. Option E is correct because the indexer can invoke skills concurrently across documents, so a custom skill must not depend on shared mutable state or on a specific call order; making it stateless and idempotent ensures consistent, repeatable enrichment results under parallel execution. Option A is not a primary design factor here because knowledge store projections are an output concern and do not fundamentally constrain the design of the custom-skill enrichment pipeline in the way timeout, incremental enrichment, and statelessness do. Option D is incorrect because semantic ranking is configured on the search index/query side (semantic configuration), not as a required element of the skillset, so it is not a factor in designing the custom-skill extraction pipeline.

Answer

The number of knowledge store projections affects indexing speed

Answer

Semantic ranking configuration must be included in the skillset

Question 4

You are deploying an Azure OpenAI model for a healthcare application. You need to ensure that the model does not generate medical advice and that all responses include a disclaimer. Which configuration should you use?

Accepted Answer

Configure a system message with instructions and enable content filtering.. Configuring a system message with explicit instructions (e.g., 'Do not provide medical advice; always include a disclaimer') combined with Azure AI Content Safety's content filtering allows you to enforce behavioral guardrails and block harmful outputs at the application layer. The system message sets the model's behavior, while content filtering provides a secondary safety net to catch policy violations, ensuring compliance in a regulated healthcare environment.

Answer

Ground the model with your own medical documents.

Answer

Set max_tokens to 50 to limit response length.

Answer

Use Azure AI Content Safety to filter medical terms.

Question 5

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 6

You are designing a solution to detect brand logos in social media images. The logos vary in size and orientation. You need to achieve high accuracy with minimal false positives. Which approach should you recommend?

Accepted Answer

Train an Azure Custom Vision object detection model with labeled logo images.. Azure Custom Vision allows you to train a custom object detection model with your own labeled dataset of brand logos, enabling high accuracy for specific logo shapes, sizes, and orientations. This approach directly addresses the need for minimal false positives by learning the exact visual features of the logos, unlike generic pre-built models.

Answer

Use Azure Computer Vision Describe API to generate captions and filter by logo mentions.

Answer

Use Azure Computer Vision Analyze API with domain-specific models.

Answer

Use Azure Form Recognizer to extract logo positions from images.

Question 7

You are building a solution to generate product descriptions using Azure OpenAI Service. You need to ensure that the output adheres to a specific tone (professional, friendly) and length (50-100 words). Which parameter should you adjust?

Accepted Answer

Set the system message with instructions about tone and length.. The system message in Azure OpenAI Service is specifically designed to set the overall behavior and context for the model, including tone and length constraints. By providing instructions like 'Respond in a professional and friendly tone, and keep the output between 50 and 100 words,' the model will adhere to these guidelines throughout the conversation. This is the primary mechanism for controlling qualitative aspects of the output, as opposed to parameters that control randomness or token limits.

Answer

Configure max_tokens to limit response length.

Answer

Modify the top_p parameter.

Answer

Adjust the temperature parameter.

Question 8

You are developing a customer support chatbot using Azure OpenAI Service. The chatbot must only answer questions related to the company's product catalog and policies. You want to minimize the risk of the chatbot generating harmful or off-topic responses. Which approach should you use?

Accepted Answer

Use a system message that instructs the model to only answer product-related questions.. A system message sets the foundational behavior of the model by providing high-level instructions that guide all subsequent responses. By explicitly instructing the model to only answer product-related questions, you establish a clear boundary that minimizes off-topic or harmful outputs. This approach leverages the model's instruction-following capability, which is more effective than parameter tuning alone for content restriction.

Answer

Set the max_tokens parameter to 100.

Answer

Set the temperature parameter to 0.

Answer

Set the top_p parameter to 0.1.

Question 9

You need to extract handwritten text from scanned forms. Which Azure Computer Vision feature should you use?

Accepted Answer

Read API. The Read API is specifically designed for extracting printed and handwritten text from images and documents, including scanned forms. It uses advanced deep-learning models optimized for text recognition and is the correct service for this task in Azure Computer Vision.

Answer

OCR API (optical character recognition)

Answer

Tag API

Answer

Describe API

Question 10

You are testing an Azure OpenAI chat completion. The response shown in the exhibit is returned. What does the finish_reason of 'content_filter' indicate?

Accepted Answer

The model's response was blocked by the content filter.. The 'content_filter' finish_reason indicates that the Azure OpenAI content filtering system detected that the model's generated response violated one of the configured content policies (e.g., hate, violence, self-harm, sexual content). The response was therefore blocked before being returned to the user, and the finish_reason explicitly signals this filtering action rather than a normal completion or a stop due to token limits.

Answer

There was a system error during processing.

Answer

The user's prompt was flagged by the content filter.

Answer

The model refused to answer due to insufficient data.

Question 11

A developer is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The bot needs to handle multiple intents, including 'BookFlight', 'CancelFlight', and 'CheckWeather'. During testing, the bot frequently confuses 'BookFlight' and 'CancelFlight' intents. What is the most effective way to improve intent classification accuracy?

Accepted Answer

Add more varied training utterances for 'BookFlight' and 'CancelFlight' intents.. Adding more varied training utterances for the 'BookFlight' and 'CancelFlight' intents directly addresses the root cause of confusion: insufficient or overlapping training data. LUIS relies on diverse utterance patterns to distinguish between semantically similar intents; increasing the quantity and variety of labeled examples improves the model's ability to learn discriminative features, thereby boosting classification accuracy.

Answer

Reduce the number of intents by merging similar ones.

Answer

Increase the confidence threshold for intent predictions.

Answer

Add more entities to the utterances.

Question 12

Your organization uses Azure AI Document Intelligence to extract data from invoices. The solution must identify custom fields not present in the prebuilt models, such as 'purchase order number' located in varying positions across documents. What should you do?

Accepted Answer

Train a custom extraction model using labeled sample invoices.. Azure AI Document Intelligence (formerly Form Recognizer) allows you to train a custom extraction model using labeled sample invoices. This approach enables the model to learn custom fields like 'purchase order number' that appear in varying positions, which prebuilt models cannot handle. By providing labeled examples, the model generalizes to extract the field accurately from new documents.

Answer

Use the layout model and apply manual post-processing.

Answer

Use Azure AI Forms Recognizer with prebuilt receipt model.

Answer

Use the prebuilt invoice model with field merging.

Question 13

You are developing a generative AI solution that uses Azure OpenAI Service. The solution must generate product descriptions in multiple languages. You need to ensure that the model consistently follows specific formatting rules, such as including a bullet list of features. Which strategy should you use?

Accepted Answer

Set a system message with explicit formatting instructions.. System messages in Azure OpenAI Service allow you to set persistent instructions that guide the model's behavior across the entire conversation. By including explicit formatting rules—such as requiring a bullet list of features—in the system message, you enforce consistent output structure without retraining the model. This approach is efficient, cost-effective, and directly leverages the API's design for controlling response format.

Answer

Fine-tune the model with a dataset containing formatted examples.

Answer

Increase the max_tokens parameter to allow longer outputs.

Answer

Adjust the temperature parameter to a lower value.

Question 14

A healthcare company is using Azure AI Document Intelligence to extract patient data from forms. They need to ensure that all extracted data is encrypted at rest using a customer-managed key (CMK) and that the service endpoint is restricted to a specific virtual network. Which combination of steps should they take?

Accepted Answer

Create a private endpoint and associate a customer-managed key in the resource encryption settings. It combines a private endpoint (which restricts the service endpoint to a specific virtual network by providing a private IP address within that VNet, eliminating public internet exposure) with a customer-managed key (CMK) in the resource encryption settings, which ensures data at rest is encrypted using a key stored in Azure Key Vault that the customer controls. This directly meets both requirements: network isolation via private endpoint and CMK-based encryption at rest.

Answer

Use a service endpoint and configure a managed identity

Answer

Disable public network access and enable CMK via Azure Key Vault

Answer

Configure IP firewall rules and enable CMK via Azure Key Vault

Question 15

You are developing a generative AI application that uses Azure OpenAI Service. You want to ensure that the application does not generate offensive content. Which Azure service should you use?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the correct service because it is specifically designed to detect and filter offensive, inappropriate, or harmful content in text and images. For a generative AI application using Azure OpenAI, this service can be integrated to review prompts and completions in real time, ensuring that generated outputs comply with content policies and do not contain hate speech, violence, or other offensive material.

Answer

Azure AI Bot Service

Answer

Azure AI Language

Answer

Azure AI Search