AI-102 Plan and manage an Azure AI solution Practice Test 5 — 15 Questions

Question 1

You are designing a document processing pipeline using Azure AI Document Intelligence. The pipeline must extract data from both structured forms and unstructured invoices. The solution should support custom models with minimal manual labeling. Which approach should you use?

Accepted Answer

Train a custom neural model with a few labeled samples. Option C is correct because Azure AI Document Intelligence's custom neural models can be trained with as few as five labeled samples, leveraging transfer learning to achieve high accuracy on both structured forms and unstructured invoices. This approach minimizes manual labeling effort while supporting the diverse document types in the pipeline, unlike prebuilt models that are limited to specific layouts or rule-based methods that require extensive manual configuration.

Answer

Use prebuilt invoice models for all documents

Answer

Use Azure AI Language to analyze text

Answer

Use OCR and custom regex patterns

Question 2

A healthcare organization uses Azure AI Health Insights to analyze patient data. They must ensure that PHI is not logged by the AI services. Which configuration should you apply?

Accepted Answer

Enable diagnostic settings to stream logs to a Log Analytics workspace with a data masking rule. Option B is correct because Azure AI Health Insights processes protected health information (PHI), and enabling diagnostic settings to stream logs to a Log Analytics workspace with a data masking rule allows you to redact sensitive PHI from logs before they are stored. Data masking rules in Log Analytics can be configured to hide patterns like patient IDs or names, ensuring compliance with HIPAA while still retaining operational logs.

Answer

Disable all logging for the Azure AI resource

Answer

Configure the resource to use a system-assigned managed identity

Answer

Use Azure Policy to restrict logging to metadata only

Question 3

You are deploying a real-time translation service using Azure AI Translator. The solution must support automatic language detection and translation for customer chat conversations. Which pricing tier should you select to minimize costs while meeting the requirement?

Accepted Answer

Standard S1. The Standard S1 tier (D) is correct because it provides up to 1 million characters per month for translation and language detection, which is sufficient for typical customer chat workloads. The Free tier (F0) is limited to 2,000 characters per request and 2 million characters per month, which may be insufficient for production chat volumes. Higher tiers (S2, S3) offer increased throughput and character limits but incur higher costs, making S1 the most cost-effective choice that meets the requirement.

Answer

Free (F0)

Answer

Standard S3

Answer

Standard S2

Question 4

Your organization is using Azure AI Search with semantic ranking. Users report that search results are not showing relevant documents at the top. You need to improve relevance. What should you configure?

Accepted Answer

Enable semantic search configuration on the index. Semantic search configuration is required to enable semantic ranking, which uses deep learning models to re-rank search results based on contextual relevance rather than just keyword matching. Without this configuration, the index cannot leverage semantic ranking even if the service tier supports it, so enabling it directly addresses the user's complaint about irrelevant documents appearing at the top.

Answer

Add synonyms to the index

Answer

Define a custom scoring profile

Answer

Change the index analyzer to a different language analyzer

Question 5

You are responsible for an Azure AI multi-agent system built on Microsoft Foundry. The system experiences frequent timeout errors when agents call external APIs. You need to implement a resilient pattern. What should you do?

Accepted Answer

Implement retry logic with exponential backoff in the agent tool definitions. Option A is correct because implementing retry logic with exponential backoff in agent tool definitions is a standard resilience pattern for transient failures when calling external APIs. This approach, often using the Retry-After header or a custom backoff strategy, reduces load on the API and prevents cascading timeouts in a multi-agent system built on Microsoft Foundry. It aligns with the recommended practices for building robust AI solutions that depend on external services.

Answer

Disable retry attempts to avoid duplicate requests

Answer

Increase the global timeout for all agents

Answer

Switch to synchronous agent calls

Question 6

You deploy a custom vision model using Azure AI Custom Vision. After deployment, you notice the model has high accuracy on training data but low accuracy on new images. What is the most likely cause?

Accepted Answer

The model is overfitted to the training data. High accuracy on training data but low accuracy on new images is the classic symptom of overfitting, where the model has memorized the training examples (including noise and irrelevant patterns) rather than learning generalizable features. In Azure AI Custom Vision, this typically occurs when the training dataset is too small, too homogeneous, or lacks sufficient variation, causing the model to fail on unseen data.

Answer

The training time was too short

Answer

The training dataset has too few images

Answer

The wrong domain was selected during training

Question 7

Which TWO actions are required to enable private endpoint connectivity for an Azure AI Language resource?

Accepted Answer

Disable public network access on the AI resource. Private endpoint requires a subnet in a VNet and disabling public network access. Option A is wrong because private DNS zone is optional but recommended. Option D is wrong because service tags are not required. Option E is wrong because firewall rules are not needed.

Answer

Configure an Azure Firewall rule to allow the private endpoint

Answer

Create a private DNS zone for the resource

Answer

Add a service tag to the network security group

Question 8

Which THREE components are part of the Azure AI Foundry SDK for building multi-agent solutions?

Accepted Answer

AIAgentClient. A is correct because AIAgentClient is a core component of the Azure AI Foundry SDK specifically designed for building and managing multi-agent solutions. It provides the necessary abstractions to create, orchestrate, and communicate with multiple AI agents within a single project, enabling complex workflows and agent collaboration.

Answer

Azure AI Search client

Answer

Azure OpenAI client

Question 9

Which TWO Azure AI services can be used to build a conversational AI system that handles multi-turn dialogues with context?

Accepted Answer

Azure AI Language. Azure AI Language provides conversational language understanding (CLU) capabilities that enable you to build a model capable of understanding multi-turn dialogues by maintaining context across user utterances. Azure OpenAI Service offers advanced language models like GPT-4, which can handle multi-turn conversations with context through prompt engineering and conversation history, making it suitable for building conversational AI systems.

Answer

Azure AI Search

Answer

Azure AI Content Safety

Answer

Azure AI Bot Service

Question 10

Refer to the exhibit. You are deploying an AI project in Microsoft Foundry using an ARM template. The deployment fails with an error indicating that the hub resource is not in the same region. What is the most likely cause?

Accepted Answer

The hub and project are in different Azure regions. In Azure AI Foundry, the hub and project resources must reside in the same Azure region because the project is a child resource of the hub and relies on the hub's regional endpoint for metadata storage and compute orchestration. The ARM template deployment fails with a region mismatch error when the location property of the project specifies a different region than the hub's location, as Azure enforces regional affinity for child resources to ensure low-latency communication and data residency compliance.

Answer

The resource group for the hub is different from the project's resource group

Answer

The identity type is SystemAssigned, but the hub requires UserAssigned

Answer

The location property is misspelled in the template

Question 11

Refer to the exhibit. You are configuring an agent in Azure AI Foundry. The agent fails to start because the specified model is not available in the current Azure OpenAI resource. What should you do to resolve the issue?

Accepted Answer

Deploy the gpt-4-0613 model in the Azure OpenAI resource. The agent fails to start because the specified model (likely gpt-4-0613) is not deployed in the Azure OpenAI resource. In Azure AI Foundry, agents require an existing model deployment to invoke; you cannot use a model that hasn't been deployed. Option B correctly resolves this by deploying the required model in the Azure OpenAI resource.

Answer

Modify the system_prompt to include the model version

Answer

Change the connection_type to 'Weak'

Answer

Change the provider to 'AzureAI'

Question 12

Refer to the exhibit. A developer creates an agent in Azure AI Foundry with a code_interpreter tool. The agent is supposed to generate plots but returns errors. What is the most likely cause?

Accepted Answer

The code interpreter tool does not support visualization libraries. The code_interpreter tool in Azure AI Foundry is designed for executing Python code in a sandboxed environment, but it does not include or support visualization libraries such as Matplotlib, Seaborn, or Plotly. Therefore, any attempt to generate plots using these libraries will result in errors, making option C the correct answer.

Answer

The instructions are too generic

Answer

The JSON syntax is incorrect

Answer

The agent name is invalid

Question 13

You are designing an Azure AI solution that uses Azure AI Language to analyze customer support transcripts. The solution must identify key phrases, detect sentiment, and extract custom entities specific to your product catalog. Which two Azure AI Language features should you enable?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction (Option B) is correct because it identifies the main points and important terms in customer support transcripts, such as 'refund request' or 'account issue,' which directly supports analyzing the content. Custom Entity Extraction (Option D) is correct because it allows you to define and extract domain-specific entities from your product catalog, such as product names or model numbers, using a trained custom entity extraction model. Together, these two features enable both general insight extraction and tailored, product-specific data extraction from the transcripts.

Answer

PII Detection

Answer

Summarization

Question 14

You are planning a multi-region deployment of Azure AI services to ensure high availability and low latency for global users. You need to decide how to manage API keys and endpoint URLs across regions. What is the recommended approach?

Accepted Answer

Use Azure API Management to expose a single endpoint that routes to regional Azure AI services resources, each with its own key stored in Key Vault.. Option C is correct because it combines Azure API Management as a unified gateway with regional Azure AI Services resources, each secured by individual keys stored in Azure Key Vault. This architecture provides global load balancing, regional failover, and centralized key management without exposing keys in application code. API Management can route requests based on latency or geography, ensuring high availability and low latency while Key Vault rotates keys securely.

Answer

Create separate Azure AI services resources per region and hardcode the keys in application configuration.

Answer

Create a single Azure AI services resource in one region and share the key across all applications.

Answer

Use Microsoft Entra ID authentication with managed identities and have each application call the region-specific endpoint directly.

Question 15

Refer to the exhibit. You are testing a conversational language understanding (CLU) application in Azure AI Language. You send the JSON request to the prediction endpoint and receive a 200 response but with an empty topIntent. What is the most likely cause?

Accepted Answer

The conversationItem is missing a required 'role' field.. B is correct because the JSON request for a conversational language understanding (CLU) application requires a 'role' field within the 'conversationItem' object when using the conversation analysis endpoint. Without this field, the service cannot properly interpret the utterance's context (e.g., user vs. system role), leading to a successful HTTP 200 response but an empty 'topIntent' as the model fails to classify the intent.

Answer

The projectName or deploymentName is incorrect.

Answer

The language parameter is not supported.

Answer

The utterance text is too short.