AI-102 Plan and manage an Azure AI solution Practice Test 1 — 15 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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Create an Azure AI services resource in a specific EU region and set the data residency option to 'EU'.. 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

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.

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Normalize pixel values to a range of 0-1.

Answer

Apply data augmentation techniques like random cropping.

Question 3

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.. 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 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

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 6

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.. Azure Cognitive Search supports security trimming via OAuth 2.0 and Microsoft Entra ID group-based access control. By storing group identifiers in a search index field and using a security filter, only users whose Microsoft Entra ID 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 7

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.. 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 8

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.. 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 9

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.. 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 Microsoft Entra ID 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 10

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 11

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.. 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 12

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

Accepted Answer

Prepare parallel data, Create Custom Translator resource, Upload parallel data, Train model, Deploy model. Start with parallel data, create the resource, upload to custom model, train, then deploy.

Answer

Create Custom Translator resource, Prepare parallel data, Upload parallel data, Train model, Deploy model

Answer

Upload parallel data, Create Custom Translator resource, Train model, Deploy model

Answer

Prepare parallel data, Create Custom Translator resource, Train model, Upload parallel data, Deploy model

Question 13

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

Accepted Answer

Step 1: Check indexer execution history, Step 2: Review error messages, Step 3: Verify data source connectivity, Step 4: Check skillset execution, Step 5: Re-run the indexer. First check history, review errors, verify data source, check skillset, then re-run.

Answer

Step 1: Verify data source connectivity, Step 2: Check indexer execution history, Step 3: Review error messages, Step 4: Check skillset execution, Step 5: Re-run the indexer

Answer

Step 1: Review error messages, Step 2: Check indexer execution history, Step 3: Check skillset execution, Step 4: Verify data source connectivity, Step 5: Re-run the indexer

Answer

Step 1: Check skillset execution, Step 2: Verify data source connectivity, Step 3: Check indexer execution history, Step 4: Review error messages, Step 5: Re-run the indexer

Question 14

Match each Azure AI concept to its description.

Accepted Answer

Azure AI Perception: Processes visual and auditory data to understand the world. The correct matches pair each Azure AI concept with its primary function. Common confusions occur between Perception and Content Safety (both deal with content analysis) and between Decision and Communication (both involve interaction but different purposes).

Answer

Azure AI Perception: Detects and monitors offensive or risky content

Answer

Azure AI Decision: Creates conversational agents and speech-based interactions

Question 15

You are deploying an Azure AI Search solution that indexes customer support tickets. The solution must support fuzzy search for misspelled keywords and boost results from premium customers. Which two features should you configure?

Accepted Answer

Scoring profiles. Scoring profiles allow you to boost search results based on specific criteria, such as customer tier, by assigning higher weights to certain fields or adding functions that increase scores for premium customers. This directly supports the requirement to prioritize results from premium customers.

Answer

Synonym maps

Answer

Suggesters

Answer

Custom analyzers