AI-102 Practice Test 14 — 15 Questions

Question 1

You have deployed a chatbot using Azure OpenAI with a system message as shown. The chatbot sometimes provides incorrect answers that are not supported by the sources. What is the most likely cause?

Accepted Answer

The system message does not guarantee grounding; the model may still hallucinate.. A system message in Azure OpenAI provides instructions and context but does not enforce factual grounding. The model can still generate responses that are not supported by the provided sources (hallucination), especially if the system message is not explicitly designed to restrict the model to only use the given data. Grounding requires additional techniques like retrieval-augmented generation (RAG) or explicit constraints in the prompt.

Answer

Content filters are incorrectly configured, allowing harmful content.

Answer

The data ingestion pipeline has errors, so the sources are not available.

Answer

The temperature parameter is too low, causing repetitive answers.

Question 2

You are developing a generative AI application that uses Azure OpenAI Service. You want to ensure that the application does not generate offensive content. Which Azure service should you use?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the correct service because it is specifically designed to detect and filter offensive, inappropriate, or harmful content in text and images. For a generative AI application using Azure OpenAI, this service can be integrated to review prompts and completions in real time, ensuring that generated outputs comply with content policies and do not contain hate speech, violence, or other offensive material.

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Azure AI Bot Service

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Azure AI Language

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Azure AI Search

Question 3

A company uses Azure OpenAI Service to generate product descriptions. They notice that the descriptions sometimes contain factually incorrect information. Which strategy should they use to reduce hallucinations?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) by grounding prompts with a knowledge base.. Retrieval-Augmented Generation (RAG) grounds the model's output in a trusted, external knowledge base, providing factual context that directly reduces hallucinations. By retrieving relevant documents and injecting them into the prompt, the model generates responses based on verified information rather than relying solely on its parametric memory, which is the primary cause of factual inaccuracies in Azure OpenAI Service.

Answer

Increase the temperature parameter to 1.0.

Answer

Reduce the max_tokens parameter to limit output length.

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Add a system message instructing the model to be more careful.

Question 4

You are designing a solution that extracts information from scanned invoices. The solution must automatically classify invoices by vendor and extract key fields (total amount, date, invoice number). Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence custom classification and extraction models. Azure AI Document Intelligence (formerly Form Recognizer) allows you to build custom classification models to identify the vendor from the invoice layout, and custom extraction models to pull key fields like total amount, date, and invoice number. This combination handles both the classification and extraction requirements in a single service, using labeled training data for high accuracy.

Answer

Azure AI Vision OCR and Azure AI Language

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Azure AI Form Recognizer (deprecated) with custom models

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Azure AI Document Intelligence pre-built invoice model

Question 5

Which TWO actions should you take to reduce the cost of using Azure OpenAI for a chatbot that handles high traffic?

Accepted Answer

Use the batch API for non-real-time requests.. The batch API allows you to submit non-real-time requests that are processed asynchronously, often at a lower cost per token compared to real-time inference. This is ideal for chatbot scenarios where some queries (e.g., historical analysis or bulk processing) do not require immediate responses, reducing overall Azure OpenAI consumption costs.

Answer

Increase max_tokens to reduce the number of requests.

Answer

Lower the temperature to 0 to reduce token usage.

Answer

Fine-tune the model to reduce prompt length.

Question 6

Your company uses Azure AI Search to power a customer-facing product catalog search. The search index contains product data from Azure SQL Database. The indexer runs daily. Lately, users complain that new products appear in the catalog with a delay of up to 24 hours. The business requires near real-time indexing (within minutes) for new products. You have the following constraints: - The indexer must continue to run daily for full sync. - You need to minimize changes to the existing architecture. - You cannot use Azure Functions or Logic Apps due to cost. What should you do?

Accepted Answer

Add a high-water mark change detection policy (e.g., based on a LastModified column) to the indexer data source. Set the indexer to run every 5 minutes.. It uses a high-water mark change detection policy (e.g., based on a LastModified column) to identify only new or updated records since the last indexer run. By setting the indexer to run every 5 minutes, you achieve near real-time indexing without changing the existing architecture or incurring additional costs from Azure Functions or Logic Apps. The daily full sync continues to run as scheduled, ensuring consistency.

Answer

Enable change tracking on the Azure SQL Database and configure the indexer to use it.

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Modify the application to use the push API to upload new products to the index as they are added.

Answer

Remove the daily indexer schedule and instead trigger the indexer manually after each new product insertion.

Question 7

You are building a knowledge mining solution using Azure AI Search and Azure AI Language. The solution must extract key phrases, entities, and sentiment from customer feedback documents. After processing, the enriched content should be stored in the search index for full-text search. You need to configure the enrichment pipeline. Which two Azure AI services should you integrate?

Accepted Answer

Azure AI Language and Azure AI Search. The solution requires key phrase extraction, entity recognition, and sentiment analysis, which are all capabilities of Azure AI Language. Azure AI Search is the other required service because it provides the search index and the enrichment pipeline (skillset) that invokes the AI Language skills to process documents and store the enriched content for full-text search.

Answer

Azure AI Language and a custom skill in Azure Functions

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Azure AI Translator and Azure AI Search

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Azure AI Document Intelligence and Azure AI Search

Question 8

A company uses Azure AI Search to index product catalogs. The search must support multilingual queries and return results in the user's language. What should you configure?

Accepted Answer

Assign appropriate language analyzers to fields. Azure AI Search allows you to assign language-specific analyzers (e.g., Microsoft English, French, Arabic) to individual fields in the index. When a query is submitted, the search engine uses the analyzer associated with the field to tokenize and normalize the text according to the linguistic rules of that language, enabling accurate multilingual search and returning results in the user's language.

Answer

Create synonym maps for each language

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Configure scoring profiles based on language

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Enable semantic search

Question 9

Your organization has a knowledge base of technical manuals in PDF format. You need to enable users to ask natural language questions and get answers from the manuals. Which solution should you build?

Accepted Answer

Azure AI Language custom question answering with the documents as sources. Azure AI Language custom question answering is specifically designed to ingest documents (like PDFs) and provide a natural language Q&A interface over them. It uses a built-in extractive reader to find answer spans directly from the source text, making it the most straightforward solution for answering questions from a static knowledge base of technical manuals without requiring additional search or vectorization infrastructure.

Answer

Azure AI Search with integrated vectorization and semantic search

Answer

Azure OpenAI Service with GPT-4o and Azure AI Search as a data source

Answer

Azure AI Document Intelligence to extract text and then use Azure AI Search

Question 10

A company uses Azure AI Vision to analyze product images in an e-commerce application. The solution uses the Analyze Image API with OCR. Recently, the OCR accuracy has decreased for images with handwritten text on product labels. What should the team do to improve accuracy?

Accepted Answer

Switch to the Read API for OCR.. The Read API (part of Azure AI Vision) is specifically optimized for extracting text from images, including handwritten text, and provides higher accuracy for OCR scenarios compared to the general-purpose Analyze Image API. The Analyze Image API's OCR feature is designed for printed text and does not handle handwritten content as effectively. By switching to the Read API, the team leverages a dedicated OCR engine that supports both printed and handwritten text, improving accuracy for product labels.

Answer

Enable OCR in the Analyze Image API configuration.

Answer

Use Azure AI Document Intelligence prebuilt receipt model.

Answer

Retrain the OCR model with additional handwritten samples.

Question 11

A manufacturing company uses Azure AI Custom Vision to classify defects on assembly line parts. The model is currently trained with 500 images per class and achieves 85% accuracy. The company needs to improve accuracy to 95% without collecting new images. Which THREE actions should they take?

Accepted Answer

Use data augmentation (e.g., rotation, scaling). Custom Vision's built-in data augmentation (rotation, scaling, flipping, etc.) artificially increases the diversity of the training set without requiring new images. This helps the model generalize better and reduces overfitting, directly improving accuracy toward the 95% target.

Answer

Change the domain to 'General'

Answer

Increase the number of training iterations

Question 12

Refer to the exhibit. You deployed this Azure AI Service resource. Developers report that they cannot call the service from their local machines using the API endpoint. The developers are not connected to the corporate VPN. What should you do?

Accepted Answer

Add the developers' public IP addresses to the ipRules list.. The exhibit shows that the Azure AI Service resource's network configuration has 'Selected Networks and Private Endpoints' enabled with a defaultAction of 'Deny'. This means only traffic from explicitly allowed IP addresses or virtual networks can reach the endpoint. Since the developers are not on the corporate VPN, they cannot use a virtual network rule. The correct solution is to add their public IP addresses to the ipRules list, which explicitly permits traffic from those specific IPs while keeping the default deny for all other traffic.

Answer

Disable network ACLs entirely.

Answer

Change defaultAction to Allow and remove all rules.

Answer

Add a second virtual network rule for the developers' subnet.

Question 13

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 14

You are designing an agentic solution that uses Azure AI Agent Service to answer customer support queries. The agent needs to retrieve information from a knowledge base stored in Azure AI Search. Which tool should you enable for the agent?

Accepted Answer

Knowledge base. The Knowledge base tool is the correct choice because it is specifically designed to connect an Azure AI Agent to an Azure AI Search index, enabling retrieval-augmented generation (RAG) from structured or unstructured knowledge sources. This tool allows the agent to query the search index and return relevant chunks of information to answer customer support queries without custom code.

Answer

Code Interpreter

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Function calling

Answer

KQL

Question 15

You need to monitor usage and costs of your Azure OpenAI Service deployments. Which Azure tool should you use?

Accepted Answer

Azure Cost Management + Billing. Azure Cost Management + Billing is the correct tool for monitoring usage and costs of Azure OpenAI Service deployments because it provides detailed cost analysis, budget tracking, and usage reports across all Azure services. It allows you to set budgets, create cost alerts, and analyze spending patterns specifically for OpenAI model deployments, including per-model and per-region cost breakdowns.

Answer

Azure Monitor

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Azure Service Health

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Azure Advisor