AI-102 Practice Test 39 — 15 Questions

Question 1

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?

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

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.

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Increase the temperature parameter to 1.0.

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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 2

Your company deploys an Azure AI Document Intelligence solution to extract data from invoices. During testing, you notice that some fields are not being extracted correctly, especially for invoices from a specific vendor with a non-standard layout. You need to improve extraction accuracy for this vendor's invoices. What should you do?

Accepted Answer

Train a custom model using labeled samples of the vendor's invoices.. Training a custom model using labeled samples of the vendor's invoices allows Document Intelligence to learn the non-standard layout, improving extraction accuracy. Option A is incorrect because while OCR and regex can extract text, they are not effective for structured data extraction from variable layouts. Option B is incorrect because converting invoices to a standard format is time-consuming and may lose important data; it's better to train a model on the actual invoices. Option D is incorrect because the prebuilt invoice model is designed for standard invoice layouts; adjusting confidence thresholds won't fix extraction accuracy for non-standard formats.

Answer

Enable OCR on the documents and use regular expressions to extract fields.

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Convert the invoices to a standard format before processing.

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Use the prebuilt invoice model with confidence threshold adjustment.

Question 3

You are extracting text from scanned documents that are in French. Which capability of Azure AI Document Intelligence should you use?

Accepted Answer

Read API. The Read API is the correct choice because it is specifically designed for extracting printed and handwritten text from scanned documents, including support for multiple languages like French. It performs optical character recognition (OCR) to digitize text without requiring any additional training or customization, making it ideal for general text extraction from scanned documents.

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Custom model

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Layout model

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Prebuilt invoice model

Question 4

Your organization has a large corpus of legal documents stored in Azure Blob Storage. You need to build a solution that allows lawyers to ask natural language questions and get answers directly from the documents, without moving data out of Azure. Which service should you use?

Accepted Answer

Azure AI Search with semantic search. Azure AI Search with semantic search is the correct choice because it allows you to index legal documents stored in Azure Blob Storage, then query them using natural language questions. The semantic search capability re-ranks results based on contextual understanding, enabling the system to extract precise answers from the document corpus without moving data out of Azure.

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Azure AI Document Intelligence

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Azure AI Language Service with custom question answering

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Azure AI Computer Vision

Question 5

A developer is creating a custom text classification model using Azure AI Language. The dataset has 10,000 documents across 50 categories. Which method is most suitable for labeling the data efficiently?

Accepted Answer

Use active learning in the custom text classification project. Active learning in custom text classification projects automatically selects the most informative unlabeled documents for manual review, reducing labeling effort while maximizing model accuracy. With 10,000 documents across 50 categories, active learning prioritizes ambiguous or high-uncertainty samples, making it the most efficient approach for iterative labeling.

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Use a prebuilt model from the Azure AI Language service

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Manually label all documents in the Language Studio

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Use Azure Machine Learning designer to auto-label

Question 6

Refer to the exhibit. You are deploying a generative AI model as an online endpoint in Azure Machine Learning. You receive complaints that the endpoint returns 503 errors during peak hours. What is the most likely cause?

Accepted Answer

The manual scale setting with 2 instances may be insufficient for peak traffic.. 503 errors during peak hours indicate that the endpoint is overwhelmed by the request volume. With manual scaling set to only 2 instances, the compute capacity is insufficient to handle the increased traffic, causing the service to reject requests. Azure Machine Learning online endpoints require sufficient instance count or autoscaling to absorb traffic spikes.

Answer

The request timeout of 30 seconds is too short.

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The environment variable MODEL_CACHE_SIZE is set too low.

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The model version is not specified correctly.

Question 7

Your Azure AI Language custom entity recognition model incorrectly extracts 'Microsoft' as an organization when it refers to the company, but fails to extract 'Microsoft' as a product when it refers to the software. How should you improve the model?

Accepted Answer

Label 'Microsoft' as both 'Organization' and 'Product' in different training sentences with appropriate context. Custom entity recognition models in Azure AI Language learn to distinguish entity types based on context. By labeling 'Microsoft' as 'Organization' in sentences where it refers to the company and as 'Product' in sentences where it refers to the software, you provide the model with the contextual clues needed to disambiguate the same token across different uses. This supervised learning approach directly addresses the model's failure to recognize the product entity.

Answer

Reduce the amount of training data to avoid confusion

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Remove the 'Organization' entity type from the model

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Add more training sentences without labeling the entity type

Question 8

A company uses the Computer Vision Image Analysis API to generate captions for images. The captions are often too generic. How can they improve the descriptiveness of captions?

Accepted Answer

Use the Dense Captioning feature.. The Dense Captioning feature in the Computer Vision Image Analysis API generates one-sentence descriptions for each of up to 10 regions detected in an image, providing more specific and detailed captions than the single generic caption. This directly addresses the problem of captions being too generic by breaking the image into meaningful areas and describing each one individually.

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Use the Object Detection API instead.

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Train a custom model with Custom Vision.

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Increase the confidence threshold for captions.

Question 9

You are developing a chat application that uses Azure OpenAI Service to answer customer queries. The solution must ensure that the model does not generate harmful or offensive content. Which Azure AI service should you configure?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is specifically designed to detect and filter harmful or offensive content in text and images, making it the appropriate service to integrate with an Azure OpenAI chat application to enforce content safety policies. It provides APIs for content moderation, including severity-based filtering for hate, self-harm, sexual, and violence categories, which directly addresses the requirement to prevent the model from generating harmful output.

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Azure AI Bot Service

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Azure AI Search

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Azure AI Language

Question 10

You are developing a chatbot that uses Azure AI Language to understand user intents. The chatbot must handle multiple languages and direct users to the appropriate support team based on the detected intent. Which Azure AI Language feature should you use?

Accepted Answer

Conversational language understanding (CLU). Conversational language understanding (CLU) is the correct feature because it is specifically designed to extract intents and entities from user utterances in a multi-language conversational context. CLU enables the chatbot to detect the user's intent (e.g., 'billing', 'technical support') and route them to the appropriate support team, while also supporting multiple languages through its language-agnostic model training and per-language project configurations.

Answer

Text Analytics

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QnA Maker

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Translator

Question 11

Refer to the exhibit. You are using Microsoft Graph to retrieve user information for use in a Microsoft 365 Copilot extension. The response shows that the mail and mobilePhone fields are null. What is the most likely reason?

Accepted Answer

The user has not configured those properties in their Microsoft Entra ID profile.. The mail and mobilePhone fields are null because the user has not populated these attributes in their Microsoft Entra ID (formerly Azure AD) profile. Microsoft Graph returns the actual stored values for these properties; if they are empty or unset, the API response will show null. This is the most common and straightforward reason for null values in user profile fields.

Answer

The API call required additional permissions.

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The user is a guest user.

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The user does not exist in the tenant.

Question 12

A team is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The chatbot must handle multiple languages. What should you configure?

Accepted Answer

Create separate LUIS applications for each language. LUIS does not natively support multilingual models within a single app. Each LUIS application is designed for a single language, so to handle multiple languages, you must create separate LUIS apps—one per language—and route user utterances to the appropriate app based on the detected language. This ensures accurate intent and entity recognition tailored to each language's linguistic patterns.

Answer

Configure a single LUIS app with multilingual utterances

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Use Azure AI Search to index translated content

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Use Azure Translator to translate utterances before calling LUIS

Question 13

You are developing a solution that uses Azure AI Translator to translate documents from English to French. You need to ensure that the translated text maintains the original formatting, such as HTML tags. Which feature should you use?

Accepted Answer

Set the content type to 'text/html' in the translation request.. Azure AI Translator's Translate API accepts a contentType parameter, and setting it to 'text/html' tells the service to treat the input as HTML, preserving tags and only translating the human-readable text nodes. This is the documented mechanism for maintaining markup structure during translation. Without it, the service treats input as plain text and may mangle or translate tag names.

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Use the Translator glossary to preserve formatting.

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Use the Translator parallel corpus to train a custom model.

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Use the Transliterate method to preserve formatting.

Question 14

You are building a computer vision solution to detect defects on a manufacturing assembly line. The solution must process images in real-time with low latency, and you need to choose an Azure service. Which service should you use?

Accepted Answer

Azure Custom Vision. Azure Custom Vision is the correct choice because it allows you to train a custom image classification or object detection model tailored to detect specific manufacturing defects. It supports real-time, low-latency inference via a Docker container deployed to edge devices or directly through the prediction API, meeting the assembly line's performance requirements.

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Azure Computer Vision API

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Azure Video Indexer

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Azure Form Recognizer

Question 15

You are designing an NLP solution to analyze legal documents. The solution must identify specific clauses and parties involved. Which Azure AI service is most appropriate?

Accepted Answer

Custom Named Entity Recognition in Azure AI Language. Custom Named Entity Extraction (Custom NER) in Azure AI Language is the correct choice because it allows you to train a model to recognize domain-specific entities like legal clauses and party names from your own labeled data. Pre-built NER only recognizes generic entity types (e.g., person, organization, location) and cannot be customized for legal terminology. This makes Custom NER the only option that meets the requirement to identify specific clauses and parties unique to legal documents.

Answer

Pre-built Named Entity Recognition in Azure AI Language

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Text Analytics for Health

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Immersive Reader