AI-102 Plan and manage an Azure AI solution — Timed 100 Questions

Question 1

A company plans to deploy an Azure AI solution that processes sensitive customer data. The solution must comply with GDPR and ensure data residency within the European Union. Which Azure resource configuration should be used?

Accepted Answer

Create an Azure AI services resource in a specific EU region and set the data residency option to 'EU'.. Option D is correct because Azure AI services allow you to specify a data residency option when creating the resource in a specific EU region, ensuring that all customer data remains within the European Union as required by GDPR. This configuration explicitly restricts data processing and storage to the chosen geographic boundary, meeting compliance and residency mandates.

Answer

Deploy the Azure AI services in multiple regions globally and use geo-replication.

Answer

Use the Azure AI services 'Data Residency' SKU.

Answer

Use the Free tier of Azure AI services.

Question 2

You need to monitor costs for an Azure AI solution that uses multiple Azure AI services. Which Azure tool should you use to set budgets and receive alerts?

Accepted Answer

Azure Cost Management. Azure Cost Management is the dedicated Azure tool for setting budgets, defining cost thresholds, and configuring alerts when spending exceeds those limits. It provides detailed cost analysis, forecasting, and policy enforcement across all Azure services, including AI services like Cognitive Services and Azure Machine Learning.

Answer

Azure Advisor

Answer

Azure Monitor

Answer

Azure Service Health

Question 3

A company is deploying a custom vision model using Azure Custom Vision. The training data contains images with varying resolutions. The model must achieve high accuracy. Which pre-processing step should be applied to the images before training?

Accepted Answer

Resize all images to the same dimensions (e.g., 224x224).. Custom Vision models use a fixed input size (e.g., 224x224 for ResNet-based architectures). Images with varying resolutions must be resized to the same dimensions before training to ensure consistent tensor shapes for the neural network. Without this step, the model cannot process the data correctly, leading to training failures or degraded accuracy.

Answer

Convert images to grayscale.

Answer

Normalize pixel values to a range of 0-1.

Answer

Apply data augmentation techniques like random cropping.

Question 4

You are designing an Azure AI solution that uses Language Understanding (LUIS) for intent detection. The solution must handle multiple languages dynamically based on the user's locale. What should you do?

Accepted Answer

Create separate LUIS applications for each language and route based on locale.. Option B is correct because LUIS does not natively support multi-language within a single application; each LUIS app is designed for a single language. To handle multiple languages dynamically, you must create separate LUIS applications for each language and route user utterances based on the detected locale, ensuring accurate intent and entity recognition per language.

Answer

Use Azure Translator to translate user input to English before sending to LUIS.

Answer

Train a single LUIS app with utterances in all languages.

Answer

Enable the 'Multi-Language' feature in the LUIS app.

Question 5

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.

Answer

Azure AI Language Understanding (LUIS)

Answer

Azure AI Computer Vision API

Answer

Azure Cognitive Search

Question 6

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.. Option D is correct because 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.

Answer

Use Azure CDN to cache the model responses in Asia.

Answer

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

Answer

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

Question 7

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.. Option D is correct because 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 8

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.

Answer

Azure Bot Service

Answer

Azure AI Translator

Answer

Azure AI Speech

Question 9

A company is planning to use Azure AI Document Intelligence to extract data from invoices. The solution must handle variations in invoice formats. Which TWO actions should be taken to improve accuracy?

Accepted Answer

Include samples of different layouts in the training set.. Option B is correct because including samples of different invoice layouts in the training set enables the custom model to learn variations in structure, field placement, and formatting. This improves the model's ability to generalize across diverse invoice formats, reducing extraction errors. Azure AI Document Intelligence custom models require labeled training data to adapt to specific document types, and diverse samples directly address format variability.

Answer

Avoid using custom models; rely on prebuilt models only.

Answer

Use a large number of unlabeled samples.

Answer

Use a single prebuilt model for all invoices.

Question 10

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.

Answer

Number of Azure data centers in the region.

Answer

Cost of the services in each region.

Question 11

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 12

You are an Azure AI engineer at Contoso Ltd. The company has an Azure AI solution that uses Azure AI Language to analyze customer feedback. The solution is deployed in the East US region and uses the S0 pricing tier. Recently, the volume of feedback has increased significantly, causing the service to throttle requests. The application logs show HTTP 429 (Too Many Requests) errors during peak hours. The development team has already implemented retry logic with exponential backoff, but the errors persist. You need to recommend a solution to handle the increased load without changing the application code. The solution must minimize cost. What should you do?

Accepted Answer

Upgrade the Azure AI Language resource to a higher pricing tier (e.g., S1) or create additional resources and load balance.. Upgrading to a higher pricing tier (e.g., S1) increases the transactions-per-second (TPS) limit, directly addressing the HTTP 429 throttling errors without requiring code changes. Creating additional resources and load-balancing also distributes the request volume, but upgrading the existing resource is the simplest and most cost-effective approach when retry logic already fails.

Answer

Use Azure Front Door to cache responses and reduce load on the service.

Answer

Switch the Azure AI Language resource to the Free tier to reduce costs.

Answer

Move the service to a different Azure region with higher capacity.

Question 13

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.

Answer

Upgrade the edge device to have more memory and CPU.

Answer

Reduce the image resolution to lower memory usage.

Answer

Convert the model to use grayscale images.

Question 14

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 15

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 16

A company plans to use Azure Cognitive Search to index a large number of PDF documents stored in Azure Blob Storage. The documents contain sensitive personally identifiable information (PII). The search results should only be accessible to authorized users based on their Azure Active Directory (Azure AD) group membership. Which combination of features should the company implement?

Accepted Answer

Index the documents and use security filters with Azure AD group-based access control.. Option B is correct because Azure Cognitive Search supports security trimming via OAuth 2.0 and Azure AD group-based access control. By storing group identifiers in a search index field and using a security filter, only users whose Azure AD group membership matches the stored group IDs can see the corresponding search results. This ensures that sensitive PII in PDF documents is only returned to authorized users without exposing the documents themselves.

Answer

Generate shared access signatures (SAS) for each document and include them in the search index.

Answer

Use Azure RBAC to assign permissions to users for the search service and configure field-level security.

Answer

Store Azure AD group membership in a field in the search index and configure an indexer to map the field.

Question 17

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.

Answer

Use the prebuilt model with a custom field mapping.

Answer

Adjust the confidence threshold for the prebuilt model.

Answer

Retrain the prebuilt model with additional invoices.

Question 18

A company is designing a solution that uses Azure Cognitive Services for text analytics. The solution must meet the following requirements: 
- Detect sentiment in customer feedback. 
- Extract key phrases from the feedback. 
- Identify the language of the feedback automatically. 
- Ensure that the solution can scale to handle thousands of requests per second. 
Which TWO actions should the company take?

Accepted Answer

Use the Text Analytics API for sentiment analysis and key phrase extraction.. Option B is correct because the Text Analytics API (part of Azure Cognitive Services for Language) provides built-in capabilities for sentiment analysis, key phrase extraction, and language detection within a single API call. This eliminates the need for separate services and directly satisfies the requirements for detecting sentiment, extracting key phrases, and identifying language automatically.

Answer

Deploy a separate Language Detection service for language identification.

Answer

Use the Free pricing tier to reduce costs.

Answer

Use Azure Functions to aggregate results from multiple API calls.

Question 19

You are an AI engineer at a healthcare company. The company uses Azure Cognitive Services to process medical records. They have a Computer Vision resource deployed in the East US region. Recently, they implemented a custom vision model for detecting specific anomalies in X-ray images. The model was trained using the Custom Vision portal and exported as a TensorFlow model. They deployed the model to an Azure Kubernetes Service (AKS) cluster using a Docker container. The container runs the model and exposes a REST API endpoint for inference. The endpoint is used by a web application that is also hosted in the same AKS cluster. The web application is experiencing high latency when making inference requests. The latency spikes up to 10 seconds during peak hours. The AKS cluster has autoscaling enabled based on CPU metrics. The container's resource limits are set to 1 CPU core and 2 GB memory. The model's inference time on a single image is approximately 500 ms on the development machine. The team has not changed the model or the application code recently. The number of concurrent users has increased by 50% in the last month. What should you do to reduce inference latency?

Accepted Answer

Increase the CPU limit for the container to 4 cores.. Option D is correct because the inference latency is caused by CPU saturation during peak hours. The container is limited to 1 CPU core, and with a 50% increase in concurrent users, the single core becomes a bottleneck, causing inference times to spike. Increasing the CPU limit to 4 cores allows the model to process multiple requests in parallel, reducing queue wait times and overall latency.

Answer

Increase the memory limit for the container to 8 GB.

Answer

Deploy additional replicas of the container and use a load balancer.

Answer

Optimize the model by quantizing it to reduce inference time.

Question 20

A company is building a custom question-answering solution using Azure AI Language. They need to ensure that the model can provide answers from a set of internal documents, but only to authenticated users from the company's Azure Active Directory tenant. The solution should minimize latency and cost. Which deployment option should the team choose?

Accepted Answer

Deploy a custom question-answering model using a dedicated Azure AI Language resource with a private endpoint and managed identity.. Option A is correct because deploying a custom question-answering model with a dedicated Azure AI Language resource, a private endpoint, and managed identity ensures that only authenticated users from the company's Azure AD tenant can access the solution via private network connectivity, minimizing latency by avoiding public internet routing and reducing cost by using a dedicated (not serverless) resource that can be right-sized.

Answer

Use the public endpoint of Azure AI Language with Azure AD token-based authentication.

Answer

Deploy a serverless endpoint with Azure Functions and Azure Cognitive Search.

Answer

Use the prebuilt question-answering model from Azure AI Language with a custom answer list.