AI-102 Plan and manage an Azure AI solution Practice Test 11 — 15 Questions

Question 1

A company wants to use Azure AI services to extract text from scanned PDF documents. Which Azure AI service should they use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed for extracting text, tables, and key-value pairs from scanned PDFs and images using optical character recognition (OCR) and deep learning models. Unlike general OCR APIs, Document Intelligence can handle complex layouts and preserve document structure, making it ideal for this use case.

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Azure AI Language Understanding (LUIS)

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Azure AI Computer Vision API

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Azure Cognitive Search

Question 2

You are deploying an Azure AI solution that uses Azure OpenAI Service. The solution must be deployed in a way that minimizes latency for users in Asia. However, the company's data residency policy requires data to stay in the United States. What should you do?

Accepted Answer

Deploy the service in a US region and use Azure Front Door with caching to reduce latency.. It satisfies both requirements: data residency (deploying in a US region keeps data within the United States) and latency reduction for Asian users. Azure Front Door with caching stores frequently accessed model responses at edge locations closer to users in Asia, minimizing round-trip time without moving the origin data.

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Use Azure CDN to cache the model responses in Asia.

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Deploy the Azure OpenAI Service in an Asian region and use Azure Front Door to route traffic.

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Deploy the service in multiple regions globally and use Traffic Manager for routing.

Question 3

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.

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Move the service to a different Azure region.

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Use the Free tier of the Azure AI Language service.

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Increase the request timeout value.

Question 4

A company needs to implement a chatbot that answers customer queries using a knowledge base. Which Azure AI service should be used to build the knowledge base?

Accepted Answer

Azure AI Language (Question Answering). Azure AI Language's Question Answering feature is specifically designed to create a knowledge base from structured or unstructured content (e.g., FAQs, product manuals, support documents). It uses a custom question-answering model that can be trained and published as a REST API endpoint, which a chatbot can then query to retrieve precise answers. This makes it the correct service for building the knowledge base itself.

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Azure Bot Service

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Azure AI Translator

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Azure AI Speech

Question 5

Which THREE factors should be considered when choosing a region for deploying Azure AI services?

Accepted Answer

Service availability and feature support.. Service availability and feature support (Option B) is a critical factor because not all Azure AI services are available in every region; for example, certain Cognitive Services like Azure OpenAI or Computer Vision OCR may be in preview or fully supported only in specific regions. Choosing a region without the required service or feature would prevent deployment or limit functionality, directly impacting solution design.

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Number of Azure data centers in the region.

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Cost of the services in each region.

Question 6

You are an Azure AI engineer at Fabrikam Inc. The company has developed a custom vision model using Azure Custom Vision to detect defects on a manufacturing assembly line. The model is deployed as a Docker container to an on-premises edge device using Azure IoT Edge. Recently, the model's inference accuracy has decreased. The operations team reports that the edge device is running low on memory and CPU. The model was trained with images from a specific camera angle, but the camera angle has been changed slightly due to maintenance. You need to improve the model's accuracy. What should you do?

Accepted Answer

Retrain the model with new images captured from the current camera angle.. The decrease in accuracy is most likely due to the change in camera angle, which introduces a domain shift between the training images and the new inference images. Retraining the model with images captured from the current camera angle will realign the training data distribution with the production environment, directly addressing the root cause of the accuracy drop. This is a standard practice in Custom Vision when deployment conditions change.

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Upgrade the edge device to have more memory and CPU.

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Reduce the image resolution to lower memory usage.

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Convert the model to use grayscale images.

Question 7

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.

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Apply a resource tag that specifies the region.

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Configure the endpoint URL to point to a regional endpoint.

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Set the SKU to a tier that supports regional restrictions.

Question 8

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.

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Reduce the number of intents by merging similar ones.

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Increase the confidence threshold for intent predictions.

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Add more entities to the utterances.

Question 9

A company is using Azure Form Recognizer to extract data from invoices. The prebuilt model does not correctly extract a custom field that is specific to the company's invoices. What is the most appropriate action to improve extraction accuracy for this field?

Accepted Answer

Train a custom model using labeled invoices that include the custom field.. The prebuilt Form Recognizer model is designed for common invoice layouts and may not recognize company-specific fields. Training a custom model with labeled invoices that include the custom field allows the model to learn the field's location and semantics, significantly improving extraction accuracy for that specific field.

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Use the prebuilt model with a custom field mapping.

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Adjust the confidence threshold for the prebuilt model.

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Retrain the prebuilt model with additional invoices.

Question 10

A company is using Azure AI Vision to analyze images from a manufacturing line. The solution must detect defects in real-time. The team discovers that the model's accuracy drops significantly when images are captured under different lighting conditions. What is the best approach to improve the model's robustness?

Accepted Answer

Retrain the model using images captured under various lighting conditions, using data augmentation.. The accuracy drop is caused by a domain shift: the model was trained on images with limited lighting variation but is deployed under diverse lighting. Retraining with images captured under various lighting conditions and applying data augmentation (brightness, contrast, exposure jitter) teaches the model to be invariant to those variations, directly improving robustness. This addresses the root cause rather than masking it at inference time.

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Apply image pre-processing to normalize lighting before sending to the model.

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Increase the number of training images without varying lighting conditions.

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Use a pre-built model from Azure AI Vision instead of a custom model.

Question 11

A developer is tasked with integrating Azure OpenAI Service into an application that generates product descriptions. The developer needs to ensure that the generated content does not contain offensive language. Which Azure AI service should be used in addition to Azure OpenAI?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety (D) is the correct service because it provides built-in content moderation capabilities that can detect and filter offensive, inappropriate, or harmful language in text and images. By integrating Azure AI Content Safety with Azure OpenAI, the developer can automatically screen generated product descriptions for profanity, hate speech, or other offensive content before they are displayed to users, ensuring compliance with content policies.

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Azure AI Search

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Azure AI Vision

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Azure AI Language

Question 12

Drag and drop the steps to create a custom question answering project in Azure Language Service into the correct order.

Accepted Answer

Step 1: Create Azure resource, Step 2: Access Language Studio, Step 3: Create project, Step 4: Add QnA pairs, Step 5: Train and deploy. First, create the Azure resource. Then access Language Studio, create the project, add QnA pairs, and finally train and deploy.

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Step 1: Access Language Studio, Step 2: Create Azure resource, Step 3: Create project, Step 4: Add QnA pairs, Step 5: Train and deploy

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Step 1: Create Azure resource, Step 2: Create project, Step 3: Access Language Studio, Step 4: Add QnA pairs, Step 5: Train and deploy

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Step 1: Add QnA pairs, Step 2: Create project, Step 3: Create Azure resource, Step 4: Access Language Studio, Step 5: Train and deploy

Question 13

Match each Azure AI service to its primary function.

Accepted Answer

Computer Vision: Extracts information from images and videos. The correct matches pair each service with its primary function. Computer Vision analyzes images/videos; Language Service processes text; Speech Service handles audio; Cognitive Search provides AI search. Common confusions include swapping Speech with Computer Vision or Language with Cognitive Search.

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Computer Vision: Converts speech to text and text to speech

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Language Service: AI-powered search over indexed content

Question 14

Match each Azure AI scenario to the appropriate service.

Accepted Answer

Building a conversational chatbot: Azure Bot Service. Correct matches: Chatbot -> Bot Service, Image moderation -> Computer Vision, Sentiment -> Text Analytics. Common confusions include associating Bot Service with image tasks or mistaking Cognitive Search for text analytics.

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Building a conversational chatbot: Azure Machine Learning

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Analyzing images for content moderation: Azure Bot Service

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Classifying text by sentiment: Azure Cognitive Search

Question 15

Your company uses Azure OpenAI Service to generate product descriptions. You need to ensure that the generated content does not include offensive language and adheres to responsible AI principles. What should you implement?

Accepted Answer

Configure content filters in Azure OpenAI. Content filters in Azure OpenAI allow you to define categories (e.g., hate, violence, self-harm) and severity levels (low, medium, high) to automatically block or flag offensive language in generated outputs. This directly enforces responsible AI principles by preventing harmful content from being surfaced to users, without requiring model retraining or encryption changes.

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Enable customer-managed key encryption

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Fine-tune the model with a curated dataset

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Set usage limits and throttling