AI-102 Practice Exam 52 — 20 Questions

Question 1

Which TWO are valid techniques for reducing latency in an Azure AI solution that processes real-time chat messages?

Accepted Answer

Deploy Azure AI services in the same region as the application.. Deploying Azure AI services in the same region as the application minimizes network latency by reducing the physical distance data must travel. This ensures lower round-trip times (RTT) for real-time chat message processing, which is critical for maintaining responsiveness in interactive applications.

Answer

Use a larger, more accurate model for better performance.

Answer

Move the application to a different region to use a different pricing tier.

Answer

Increase the client-side timeout to allow for slower responses.

Question 2

Match each Azure AI term to its definition.

Accepted Answer

Azure Cognitive Services: A collection of AI services and APIs that help you build intelligent applications without needing direct AI or data science skills.. Correct matches: Azure Cognitive Services provides pre-built AI APIs; Azure Machine Learning is for ML lifecycle; Azure Bot Service for building bots. Common confusion involves swapping definitions of Cognitive Services and Machine Learning.

Answer

Azure Cognitive Services: A service for building and training machine learning models.

Answer

Azure Machine Learning: A collection of pre-built AI APIs for vision, speech, language, etc.

Answer

Azure Bot Service: A service that provides pre-built AI models for natural language processing.

Question 3

A law firm uses Azure Document Intelligence to extract clauses from legal contracts. They have a custom model trained on 15 labeled contracts. The model extracts clauses with high confidence on similar documents but fails to extract correct clauses from a new batch of contracts that have a different font and layout. The firm needs to improve extraction accuracy without retraining the model from scratch. The solution must minimize manual effort and cost. What should they do?

Accepted Answer

Create a composed model that includes the existing model and a new model trained on 5 contracts with the new layout. Creating a composed model in Azure Document Intelligence allows you to combine the existing model (trained on the original layout) with a new model trained on just 5 labeled contracts from the new layout. This approach improves accuracy on the new layout without retraining from scratch, minimizing manual effort and cost by leveraging the composed model's ability to route documents to the appropriate sub-model based on layout similarity.

Answer

Use the prebuilt-layout model to extract clauses instead

Answer

Increase the OCR confidence threshold in the analysis request

Answer

Label 15 more contracts with the original layout and retrain the model

Question 4

Which THREE factors should you consider when selecting a model for a generative AI solution on Azure?

Accepted Answer

Cost per token and deployment options.. Cost per token directly impacts operational expenses in pay-per-use models like GPT-4 on Azure OpenAI, and deployment options (e.g., global vs. regional, provisioned throughput) affect availability and scalability. These are fundamental considerations when operationalizing a generative AI solution.

Answer

Number of transformer layers in the model.

Answer

Training data source and licensing.

Question 5

Which TWO Azure services are used together to build a custom question-answering solution?

Accepted Answer

Azure AI Language (Custom Question Answering). Azure AI Language's Custom Question Answering (formerly QnA Maker) provides the core service for building a knowledge base of question-answer pairs and processing user queries. Azure AI Search acts as the underlying search index and retrieval engine, enabling the solution to scale, perform semantic ranking, and return relevant answers from the knowledge base. Together, they form a complete custom Q&A pipeline: Custom Question Answering for authoring and orchestration, and Azure AI Search for indexing and retrieval.

Answer

Azure AI Computer Vision

Answer

Azure AI Speech

Answer

Azure AI Translator

Question 6

You need to generate realistic synthetic data for training a machine learning model while ensuring the data does not contain personally identifiable information (PII). Which Azure service should you use?

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service provides access to powerful generative AI models (e.g., GPT-4) that can create realistic synthetic data by learning patterns from training data. Crucially, these models can be configured to avoid memorizing or reproducing PII, and you can apply content filters and data masking to ensure the generated output is free of personally identifiable information.

Answer

Azure AI Search

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Azure AI Document Intelligence

Answer

Azure AI Language

Question 7

You need to generate an image of a cat wearing a hat using Azure OpenAI. Which model should you use?

Accepted Answer

DALL-E. DALL-E is the Azure OpenAI model specifically designed for generating images from natural language descriptions. It uses a diffusion-based architecture to create high-quality, original images based on text prompts, making it the correct choice for generating an image of a cat wearing a hat.

Answer

Codex

Answer

GPT-4

Answer

Whisper

Question 8

You need the project to support English, Spanish, and French. What change should you make to the command?

Accepted Answer

Change --multilingual false to --multilingual true.. The command requires multilingual support for English, Spanish, and French. By default, the `--multilingual` flag is set to `false`, which restricts the project to a single language. Changing it to `true` enables the project to accept utterances in multiple languages, allowing the Conversational Language Understanding (CLU) model to process and train on intents and entities across all specified languages.

Answer

Change --language to "multi".

Answer

Add --description "Multi-language support".

Answer

Change --project-name to "SupportBotML".

Question 9

You are designing a knowledge mining solution that ingests content from SharePoint Online. The solution must index documents and also extract custom metadata such as project name and client ID using a custom skill. The custom skill is an Azure Function that calls an external API. The external API has a rate limit of 100 requests per minute. Your indexer processes 1000 documents per hour. How should you configure the indexer to avoid hitting the rate limit?

Accepted Answer

Schedule the indexer to run every 2 hours with a batch size of 20.. Avoids the rate limit by not calling the external API, but it fails to meet the requirement to extract custom metadata using the specified custom skill. Option B schedules the indexer to run every 2 hours with a batch size of 20, which reduces the frequency of runs and limits the number of documents per batch, thereby keeping requests to the external API under 100 per minute. Option C increases the batch size to 100, which could cause each batch to make 100 requests, potentially exceeding the rate limit if multiple batches are processed concurrently. Option D increases maximum parallelism, allowing more batches to run concurrently and increasing the request rate, which could exceed the limit.

Answer

Use a different custom skill that doesn't call the external API.

Answer

Increase the indexer's batch size to 100.

Answer

Increase the indexer's maximum parallelism to 10.

Question 10

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. 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 11

You are the AI architect for a global e-commerce company. The company uses Azure AI services to power its product recommendation engine. The current solution uses Azure AI Language to extract product attributes from descriptions and Azure AI Search to index and retrieve products. The company is expanding to new markets and must comply with data residency regulations in the European Union and Asia. Additionally, the solution must handle a 10x increase in traffic during holiday sales without downtime. You need to design a solution that meets these requirements. The solution must use Azure AI Language and Azure AI Search. You have the following options: A) Deploy separate Azure AI Language and Azure AI Search resources in each region (EU and Asia) and use Azure Traffic Manager to route users to the nearest region. Use geo-replication for Azure AI Search. B) Deploy a single Azure AI Language resource in the US and use Azure AI Search with geo-replication to handle traffic. Use Azure Front Door for global routing. C) Deploy Azure AI Language resources in each region and use a global Azure AI Search resource with replication. Use Azure Load Balancer to distribute traffic. D) Deploy Azure AI Language and Azure AI Search resources in the US only, and use Azure CDN to cache responses globally. Which option should you choose?

Accepted Answer

Deploy separate resources in each region with Traffic Manager and geo-replication.. Deploying separate Azure AI Language and Azure AI Search resources in each region (EU and Asia) ensures compliance with data residency regulations by keeping data within regional boundaries. Azure Traffic Manager routes users to the nearest region for low latency, and geo-replication for Azure AI Search provides high availability and disaster recovery, handling the 10x traffic increase during holiday sales without downtime.

Answer

Deploy a single US resource with geo-replication and Front Door.

Answer

Deploy US-only resources with CDN caching.

Answer

Deploy Language in each region with a global Search resource.

Question 12

You are a developer for a legal firm. The firm uses Azure Cognitive Search to index legal documents. They have a custom skill that performs OCR on scanned PDFs using Azure Form Recognizer. The skill is implemented as an Azure Function. Recently, the indexer has been failing with the error: "The request was canceled due to the configured HttpClient.Timeout of 100 seconds elapsing." The documents are large (up to 200 pages each). The skill calls the Form Recognizer API asynchronously. You need to resolve the timeout issue without losing the ability to process large documents. Current configuration: batchSize = 1, maxPageSize = 100, timeout = 100 seconds. You cannot change the execution time of the Form Recognizer API. What should you do?

Accepted Answer

Decrease the batchSize to 1 and increase the timeout property in the skillset skill definition to PT300S.. The error indicates the HttpClient timeout (100 seconds) is too short for large documents processed by the custom skill. Increasing the timeout to PT300S (300 seconds) in the skill definition allows the Azure Function to wait longer for the asynchronous Form Recognizer API to complete, while keeping batchSize=1 ensures each invocation handles one document at a time, preventing overload and maintaining reliability for large PDFs.

Answer

Increase the batchSize to 10 to process more documents per invocation, reducing the number of total invocations.

Answer

Increase the maxPageSize to 500 to reduce the number of API calls to Form Recognizer.

Answer

Remove the custom skill and use the built-in OCR skill in the skillset.

Question 13

Your company uses Azure OpenAI to generate code snippets. Developers need to ensure that the generated code does not contain security vulnerabilities. What should you implement?

Accepted Answer

Integrate a static code analysis tool into the CI/CD pipeline to scan generated code. Integrating a static code analysis tool (e.g., Microsoft Defender for DevOps, SonarQube, or Checkmarx) into the CI/CD pipeline allows automated scanning of generated code for security vulnerabilities before deployment. This approach directly addresses the requirement to ensure generated code is free of vulnerabilities, as Azure OpenAI content filters are not designed to detect code-level security flaws like SQL injection or buffer overflows.

Answer

Set usage quotas to limit the number of code generation requests

Answer

Fine-tune the model on a dataset of secure code examples

Answer

Configure Azure OpenAI content filters to block vulnerable code

Question 14

You run the Azure CLI command shown in the exhibit. After a few minutes, the deployment fails with a quota error. What is the most likely cause?

Accepted Answer

The requested capacity of 10 exceeds the available quota for the gpt-4 model in that region.. The quota error indicates that the requested capacity (10 units) for the gpt-4 model exceeds the available quota in the target region. Azure OpenAI deployments require sufficient model-specific quota, which is region- and model-specific. The error is not related to SKU name validity, model version deprecation, or resource group existence.

Answer

The SKU name 'Standard' is invalid for Azure OpenAI deployments.

Answer

The model version '0613' is deprecated and no longer available.

Answer

The resource group name 'myResourceGroup' does not exist.

Question 15

Refer to the exhibit. You are calling the Azure AI Language NER API. The response returns no entities. What is the most likely reason?

Accepted Answer

The text does not contain any recognized entities. The NER API returns entities only if the input text contains recognized entity types (e.g., Person, Location, Organization, DateTime, etc.). If no entities are found, the API returns an empty entities array. This is the most straightforward and common reason for a zero-entity response, assuming the request is otherwise valid.

Answer

The API version is incorrect

Answer

The document language should be 'es' for Spanish

Answer

The endpoint URL is for the wrong region

Question 16

You are developing an agentic solution that uses multiple AI agents to collaborate on a complex task. To ensure the agents work together effectively, you need to define a clear handoff protocol. Which approach should you use in Azure AI Agent Service to enable agent-to-agent communication?

Accepted Answer

Use Agent Handoff feature. Azure AI Agent Service provides a built-in Agent Handoff feature that enables seamless agent-to-agent communication by defining a structured handoff protocol. This feature allows agents to pass tasks and context to each other without custom code, ensuring efficient collaboration in multi-agent systems.

Answer

Configure a shared memory store

Answer

Implement a custom API for agent communication

Answer

Orchestrate agents sequentially using a script

Question 17

Which THREE factors should be considered when choosing between Azure Computer Vision and Azure Custom Vision? (Choose three.)

Accepted Answer

The need for custom model retraining over time.. Azure Custom Vision is specifically designed for scenarios where you need to retrain a model over time with new labeled data, such as when the visual characteristics of objects change (e.g., new product packaging). Azure Computer Vision is a pre-trained API that cannot be retrained; it only supports fixed, generic models. Custom Vision allows iterative training with your own images, making it essential when model drift or evolving requirements demand periodic retraining.

Answer

Whether the solution runs on edge devices.

Answer

The geographic region of the Azure subscription.

Question 18

A company uses Azure Face API to verify employee identities for building access. They need to ensure that only live faces are used, not photos or videos. Which feature should they enable?

Accepted Answer

Enable liveness detection using session-based verification.. Azure Face API's liveness detection with session-based verification is specifically designed to prevent spoofing attacks using photos, videos, or masks. It analyzes subtle cues such as micro-movements, texture, and depth to confirm the presence of a live person, ensuring that only live faces are accepted for identity verification.

Answer

Set a high confidence threshold for face matching.

Answer

Face identification with a large person group.

Answer

Face detection with attributes such as age and emotion.

Question 19

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 20

Refer to the exhibit. The developer is using Azure OpenAI with data sources (preview) to ground the model on a custom dataset. The assistant response includes a citation [^1]. However, the developer notices that the citation does not appear in the final output displayed to the user. What is the most likely cause?

Accepted Answer

The 'include_contexts' parameter in the API call is set to false.. The 'include_contexts' parameter controls whether citation metadata from the data sources is included in the final output. When set to false, the API suppresses the citation markers (e.g., [^1]) even though the underlying context was used to generate the response. This is a preview feature of Azure OpenAI with your own data, where the citation rendering is explicitly gated by this parameter.

Answer

The citation format [^1] is not supported in Azure OpenAI with data sources.

Answer

The system message overrides the citation rendering.

Answer

The 'context' object in the assistant response is not being passed back in the prompt for the next turn.