AI-102 Plan and manage an Azure AI solution Practice Test 2 — 15 Questions

Question 1

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 2

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 3

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 4

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 5

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.

Question 6

A developer is configuring an Azure AI Language resource for sentiment analysis. The solution must process social media posts in real-time with a throughput of 1000 requests per minute. After testing, the developer notices that the API returns a 429 (Too Many Requests) error when the load exceeds 500 requests per minute. What is the most likely cause and solution?

Accepted Answer

Upgrade the Azure AI Language resource to a higher tier (e.g., Standard S) to increase the rate limit.. The 429 error indicates the request rate exceeds the resource's allocated tier limit. Azure AI Language resources have predefined rate limits per pricing tier; the Standard S tier offers higher throughput (e.g., 1,000 requests per minute) compared to lower tiers. Upgrading to Standard S directly increases the rate limit to match the required 1,000 requests per minute, making it the correct solution.

Answer

Scale out the resource by creating multiple Azure AI Language instances and load balancing requests.

Answer

Implement retry logic with exponential backoff to handle 429 errors.

Answer

Use Azure API Management to cache responses and reduce calls.

Question 7

A healthcare organization is deploying an Azure AI Document Intelligence solution to extract medical record data. The solution must comply with HIPAA and process sensitive patient data. The data must be encrypted at rest and in transit, and access must be audited. The solution will be used by multiple departments, each requiring different levels of access. Which combination of Azure services should the architect recommend?

Accepted Answer

Azure AI Document Intelligence with private endpoints, managed identity authentication, Azure Storage with customer-managed keys (CMK), and Azure RBAC for access control.. Option C is correct because it ensures HIPAA compliance by using private endpoints to keep traffic within the Azure backbone network, managed identity for secure authentication without storing credentials, customer-managed keys (CMK) for encryption at rest with full control over key rotation, and Azure RBAC to enforce least-privilege access across departments. This combination addresses encryption in transit (private endpoint + HTTPS), encryption at rest (CMK), and audited access (RBAC + Azure Monitor).

Answer

Azure AI Document Intelligence with public endpoint, Azure AD authentication, and Azure Storage with customer-managed keys (CMK).

Answer

Azure AI Document Intelligence with public endpoint, Azure AD authentication, and Azure Storage with encryption at rest using Microsoft-managed keys.

Answer

Azure AI Document Intelligence with service endpoints, shared access signature (SAS) tokens, and Azure Storage with encryption at rest using Microsoft-managed keys.

Question 8

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.. Option C is correct because retraining with augmented data (different lighting) directly addresses the issue. Option A is wrong because it only adds variation but does not ensure the model generalizes to new conditions. Option B is wrong because a different model (e.g., Custom Vision) may still need training data. Option D is wrong because pre-processing alone cannot compensate for lack of training data.

Answer

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

Answer

Increase the number of training images without varying lighting conditions.

Answer

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

Question 9

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.

Answer

Azure AI Search

Answer

Azure AI Vision

Answer

Azure AI Language

Question 10

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

Question 11

Drag and drop the steps to create a custom Azure AI Translator model into the correct order.

Question 12

Drag and drop the steps to troubleshoot a failed Azure AI Search indexer execution into the correct order.

Question 13

Match each Azure AI service to its primary function.

Question 14

Match each Azure AI concept to its description.

Question 15

Match each Azure AI scenario to the appropriate service.