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

Question 1

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 JSON syntax is incorrect. The code_interpreter tool in Azure AI Foundry supports common Python visualization libraries such as Matplotlib, Seaborn, and Plotly. Therefore, the error is not due to lack of visualization support. The most likely cause is incorrect JSON syntax in the agent's tool configuration or function call, as JSON formatting errors are a frequent source of failures in Azure AI Foundry agents.

Answer

The instructions are too generic

Answer

The code interpreter tool does not support visualization libraries

Answer

The agent name is invalid

Question 2

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 3

You are planning an Azure AI solution that uses Azure AI Document Intelligence to extract data from scanned PDFs. The solution must support both structured and unstructured documents. The extracted data must be validated against a predefined schema before being stored in Azure Cosmos DB. What should you use for schema validation?

Accepted Answer

Azure AI Document Intelligence custom extraction model with a defined schema.. Azure AI Document Intelligence custom extraction models allow you to define a schema (field names, types, and relationships) that the service uses to extract data. The model enforces this schema during extraction—only fields defined in the schema are extracted, and missing fields are returned as null—effectively validating the extracted data against your predefined structure before it is stored in Azure Cosmos DB. This built-in constraint eliminates the need for additional validation logic.

Answer

Azure Functions to parse and validate the extracted data.

Answer

Azure AI Document Intelligence prebuilt model for invoices.

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Azure Logic Apps with a JSON schema validation step.

Question 4

You are deploying an Azure AI Speech custom voice model. After training, the voice sounds unnatural. Which parameter should you adjust to improve naturalness?

Accepted Answer

Use a different language for the training data. Using a different language for the training data (Option D) is necessary when the training language does not match the target voice language, leading to unnatural prosody and pronunciation. Azure AI Speech custom neural voice requires the training data to be in the same language as the desired voice. If a mismatch occurs, switching to the correct language data will significantly improve naturalness.

Answer

Change the audio output format to 48 kHz

Answer

Increase the speaking rate

Answer

Increase the training duration

Question 5

Which TWO of the following are valid ways to authenticate to Azure AI services?

Accepted Answer

Subscription key (API key). Azure AI services support two primary authentication methods: subscription keys (API keys) and Microsoft Entra ID tokens. Subscription keys are simple and used for development and testing, passed in the 'Ocp-Apim-Subscription-Key' header. Managed Identity is Azure's recommended authentication method for services running in Azure, automatically obtaining a Microsoft Entra ID token without managing credentials. While OAuth 2.0 client credentials flow is also valid and uses Microsoft Entra ID, it requires manual credential management and is less commonly used directly with AI services compared to Managed Identity. Shared access signature (SAS) tokens are for Azure Storage, not AI services. X.509 certificates are not supported for direct authentication to Azure AI services.

Answer

Shared access signature (SAS) token

Answer

OAuth 2.0 client credentials flow with Microsoft Entra ID

Answer

X.509 certificate

Question 6

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'.. 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 7

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

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Azure Monitor

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Azure Service Health

Question 8

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 9

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.. 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 10

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 11

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.. 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 12

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.. 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 13

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

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Azure AI Translator

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Azure AI Speech

Question 14

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 15

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.