AI-102 Practice Exam 53 — 20 Questions

Question 1

You call the Azure Computer Vision Analyze API with the above request body. The response includes a 'description' object with captions. Which parameter is responsible for generating captions?

Accepted Answer

Description. The 'description' parameter in the Azure Computer Vision Analyze API request body explicitly requests the service to generate human-readable captions and tags that describe the image content. When set to true, it enables the 'description' object in the response, which contains an array of captions with confidence scores. This is the direct mechanism for caption generation.

Answer

Tags

Question 2

Your organization uses Azure AI Language to perform sentiment analysis and opinion mining on product reviews. You notice that the sentiment scores are often neutral even when the review text contains clearly positive or negative opinions. You suspect the model is not capturing the nuances. Which three actions could improve the sentiment analysis accuracy? (Choose three.)

Accepted Answer

Provide more labeled training examples that cover a wider variety of writing styles and sentiments.. Providing more labeled training examples that cover a wider variety of writing styles and sentiments directly improves the custom model's ability to learn nuanced patterns. Azure AI Language's custom sentiment analysis relies on supervised learning; more diverse, high-quality labeled data helps the model generalize better and reduces the tendency to default to neutral scores for ambiguous or complex reviews.

Answer

Pre-process the text with key phrase extraction to highlight important terms before sentiment analysis.

Answer

Use the basic sentiment analysis API without any customization.

Question 3

An application uses Azure AI Face API to perform face detection and verification. The application must ensure that only users with verified identities can access sensitive data. Which additional Azure service should you integrate to comply with Microsoft's Responsible AI standards for facial recognition?

Accepted Answer

Microsoft Entra ID and Face API Limited Access. D is correct because Microsoft's Responsible AI standards for facial recognition require that applications using the Face API for identity verification must be backed by a verified identity system like Microsoft Entra ID (formerly Azure AD) and must apply for the Face API Limited Access policy. This ensures that only authorized users with verified identities can access sensitive data, aligning with Microsoft's ethical guidelines for facial recognition technology.

Answer

Azure Video Indexer

Answer

Azure AI Content Safety

Answer

Azure AI Vision Image Analysis

Question 4

You are creating a new Custom Vision project with the above JSON. The domainId corresponds to the 'Logo' domain. Which type of model will this project train?

Accepted Answer

A multilabel image classification model for logo detection. The 'Logo' domain in Custom Vision is specifically designed for image classification tasks, not object detection. When you create a project with the 'Logo' domain, it trains a multilabel image classification model, meaning each image can be assigned multiple labels (e.g., multiple logos in one image). This domain is optimized for identifying and classifying logos within images, making it distinct from object detection or general classification.

Answer

An object detection model for logo detection

Answer

An optical character recognition model

Answer

A general image classification model

Question 5

Drag and drop the steps to configure an Azure AI Search index with a custom skill into the correct order.

Accepted Answer

Create the search service, then Define the index, then Create the custom skill, then Set up the indexer with the skillset, then Run the indexer.. Start by creating the search service, define the index, then create the custom skill, set up the indexer with the skillset, and finally run it.

Answer

Create the custom skill, then Define the index, then Create the search service, then Set up the indexer with the skillset, then Run the indexer.

Answer

Define the index, then Create the search service, then Create the custom skill, then Set up the indexer with the skillset, then Run the indexer.

Answer

Set up the indexer with the skillset, then Create the custom skill, then Define the index, then Create the search service, then Run the indexer.

Question 6

Refer to the exhibit. You are configuring an Azure AI Language resource using an ARM template. The settings include PII recognition with domain set to 'phi'. What is the effect of this setting?

Accepted Answer

It will detect protected health information (PHI) such as medical record numbers.. When you set the domain to 'phi' in PII recognition for Azure AI Language, the service is configured to detect protected health information (PHI) as defined by HIPAA, such as medical record numbers, health plan numbers, and patient names. This is a specialized subset of PII that focuses on health-related sensitive data, not general health entities or standard PII categories. Option D correctly identifies this behavior.

Answer

It will detect all standard PII categories plus additional health-specific entities.

Answer

It will enable the entity recognition model to detect health entities.

Answer

It will ignore PII detection and only detect health-related entities.

Question 7

A company is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The chatbot needs to handle user intents for booking flights and checking flight status. After testing, the chatbot frequently fails to distinguish between the two intents when users mention flight numbers. Which action should the engineer take to improve intent recognition?

Accepted Answer

Add more utterances that include flight numbers to the training data for both intents.. Adding more utterances that include flight numbers to both intents provides LUIS with more varied examples of how flight numbers appear in natural language, enabling the model to learn distinguishing patterns. Without sufficient training data containing flight numbers, LUIS cannot reliably differentiate between 'BookFlight' and 'CheckFlightStatus' when users mention flight numbers, as the entity alone does not determine intent.

Answer

Increase the number of intents to split the flight-related queries further.

Answer

Reduce the confidence score threshold for intent detection.

Answer

Use the prebuilt domain for flight booking to improve accuracy.

Question 8

Which THREE factors should you consider when selecting a pricing tier for Azure Computer Vision in a production environment?

Accepted Answer

Data residency requirements. Data residency requirements (Option C) are critical when selecting a pricing tier for Azure Computer Vision because the service processes images in specific regional data centers, and some tiers (e.g., Standard S0) support multi-region processing while others may be restricted. Compliance with regulations like GDPR or HIPAA may require that image data never leaves a particular geography, directly influencing which tier and region you can choose.

Answer

Availability of free tier

Answer

Type of storage account for images

Question 9

You are building an agent for a legal firm that uses Azure OpenAI to analyze contracts. The agent must extract key clauses, identify risks, and summarize the contract. The agent uses a RAG pattern with Azure Cognitive Search as the vector database. After deployment, the agent sometimes returns irrelevant information or fails to find relevant clauses. You suspect the issue is with the chunking strategy. The contracts are large, typically 50-100 pages. Currently, you are chunking by page (each page is one chunk). You want to improve retrieval accuracy. Which action should you take?

Accepted Answer

Change chunking to use semantic boundaries: split at clause or section headings.. Splitting contracts at semantic boundaries (clause or section headings) preserves the natural meaning and context of each chunk, which is critical for legal document analysis. Page-level chunking often splits a clause across two pages, causing the vector search to retrieve incomplete or irrelevant information. By aligning chunks with the document's logical structure, the RAG pattern retrieves more coherent and relevant passages for the Azure OpenAI agent to process.

Answer

Keep page-level chunking but add 50% overlap between chunks.

Answer

Use a different embedding model, such as text-embedding-3-large.

Answer

Increase the chunk size to 5 pages per chunk and reduce overlap.

Question 10

A company wants to extract key-value pairs from scanned invoices using Azure AI. Which service should they use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct choice because it is specifically designed to extract key-value pairs, tables, and structured data from scanned documents like invoices. Unlike the Read API or OCR API, which only return raw text or OCR output, Document Intelligence uses prebuilt models (e.g., 'prebuilt-invoice') that understand the semantic layout of invoices, enabling direct extraction of fields such as invoice number, date, and total amount.

Answer

Read API

Answer

Custom Vision

Answer

OCR API

Question 11

Your chatbot uses Azure Bot Service and QnA Maker. Users can ask questions in natural language, and the bot returns answers from a knowledge base. Users report that the bot sometimes returns irrelevant answers. What should you do first?

Accepted Answer

Review and edit the QnA pairs to add alternative phrasings. The core issue is that the bot returns irrelevant answers because the QnA Maker knowledge base lacks sufficient alternative phrasings to match the variety of user questions. By reviewing and editing QnA pairs to add alternative phrasings, you directly improve the synonym and paraphrase coverage, which increases the confidence score for correct matches and reduces irrelevant responses. This is the first and most fundamental troubleshooting step before considering more complex integrations.

Answer

Create multiple QnA Maker knowledge bases for different topics

Answer

Integrate LUIS to detect user intent

Answer

Use Azure AI Search to index the knowledge base

Question 12

You are building a multilingual customer support chatbot using Azure AI Language. The bot must understand user intents in English, Spanish, and French. You have pre-existing labeled data in English only. The solution should minimize manual labeling effort. Which approach should you recommend?

Accepted Answer

Use the multilingual option in conversational language understanding (CLU) and train on the English data only.. Azure AI Language's conversational language understanding (CLU) supports a multilingual project option that allows you to train a single model on labeled data in one language (e.g., English) and have it generalize to understand intents in other languages (e.g., Spanish and French) without needing additional labeled data. This directly minimizes manual labeling effort while still leveraging the pre-existing English data.

Answer

Use Azure AI Translator to detect the language and route to a rules-based intent handler for each language.

Answer

Build a separate CLU project for each language and use the English data to bootstrap labeling with active learning.

Answer

Translate the English labeled data into Spanish and French using Azure AI Translator, then train a separate CLU model per language.

Question 13

Refer to the exhibit. A developer runs this PowerShell script to call Azure OpenAI. The script fails with an authentication error. What is the most likely cause?

Accepted Answer

The script uses the wrong HTTP header; it should use 'api-key' instead of 'Authorization: Bearer'.. The script uses 'Authorization: Bearer' header, but Azure OpenAI requires the API key to be passed in the 'api-key' header. The 'Authorization: Bearer' header is used for Azure AD token-based authentication, not for direct API key authentication. Since the script is using an API key (as indicated by the PowerShell script), the correct header is 'api-key'.

Answer

The script uses the wrong HTTP method; it should use GET.

Answer

The API version is incorrect.

Answer

The endpoint URI is missing the resource name.

Question 14

Refer to the exhibit. You are configuring an agent in Azure AI Agent Service. You want the agent to be able to execute Python code and call a custom function to get weather data. What is the issue with the JSON configuration?

Accepted Answer

The model 'gpt-4o' is not supported. The Azure AI Agent Service currently does not support the gpt-4o model for assistants. The supported models include GPT-4, GPT-4-turbo, and GPT-4-32k. Using an unsupported model will cause the agent to fail during configuration or runtime.

Answer

The function definition is missing the 'strict' parameter

Answer

The 'code_interpreter' tool type should be 'code_interpreter' not 'code_interpreter'

Answer

The 'assistant_id' should be a thread ID

Question 15

You need to analyze customer service call transcripts to identify common issues. The solution must extract key phrases, detect sentiment, and identify the language used. The transcripts are stored in Azure Blob Storage. Which Azure AI Language feature should you use to process them asynchronously?

Accepted Answer

Async Text Analytics API. The Async Text Analytics API (now part of Azure AI Language's asynchronous operations) is designed for batch processing of documents stored in Azure Blob Storage. It supports key phrase extraction, sentiment analysis, and language detection in a single asynchronous call, making it ideal for processing large volumes of customer service call transcripts without blocking the application.

Answer

Conversational Summarization API

Answer

Custom Text Classification API

Answer

Opinion Mining API

Question 16

A manufacturing company uses Azure Custom Vision to detect defects on an assembly line. The model is deployed to a container on a local edge server. Recently, the model's accuracy dropped. You suspect data drift. What should you do to monitor and retrain the model?

Accepted Answer

Periodically collect new images with labels, retrain the model in Custom Vision, and redeploy the updated container.. Custom Vision models deployed to containers on edge devices do not expose a REST endpoint that Azure Machine Learning's data drift monitoring can directly access. The only way to detect drift and retrain is to periodically collect new labeled images from the production line, retrain the model in Custom Vision, and redeploy the updated container to the edge server.

Answer

Use Azure Machine Learning data drift monitoring on the Custom Vision endpoint.

Answer

Configure Custom Vision to send alerts when drift is detected.

Answer

Enable active learning in Custom Vision to automatically retrain the model.

Question 17

Your company uses Azure AI Language to analyze customer feedback from surveys. The current pipeline extracts key phrases and sentiment. The data science team wants to identify emerging topics over time, such as new product complaints or feature requests. You need to modify the pipeline to track topic evolution. Which Azure AI Language feature should you enable?

Accepted Answer

Custom text classification. Custom text classification is the correct choice because it allows you to define and train a model to categorize feedback into custom classes (e.g., 'new product complaint', 'feature request') and track their frequency over time, enabling topic evolution analysis. Unlike prebuilt features, custom classification adapts to your specific domain and can be retrained as new topics emerge, directly supporting the data science team's goal of identifying emerging topics.

Answer

Named entity recognition (NER)

Answer

Conversation summarization

Answer

Key phrase extraction

Question 18

Your team is developing a chatbot using Azure AI Bot Service. You need to ensure that the bot can handle multiple languages and respond appropriately. Which Azure AI service should you integrate to perform language detection?

Accepted Answer

Azure AI Language. Azure AI Language provides pre-built language detection capabilities as part of its natural language processing (NLP) features. By integrating this service, the bot can analyze incoming text and identify the language, enabling it to route responses appropriately or trigger language-specific logic.

Answer

Azure AI Speech

Answer

Azure AI Content Safety

Answer

Azure AI Translator

Question 19

A company wants to use Azure AI Translator to translate customer emails from English to French. They need to ensure that the translation preserves the tone and formality of the original text. What should they configure in the request?

Accepted Answer

Set the 'formality' parameter to the desired level (e.g., 'formal' or 'informal').. Azure AI Translator provides a 'formality' parameter that allows you to specify the desired level of formality (e.g., 'formal' or 'informal') in the translated text. This parameter directly controls the tone and register of the output, ensuring that the translation preserves the original email's tone and formality, which is critical for customer communications.

Answer

Set the 'category' parameter to 'general' to use a standard translation model.

Answer

Set the 'scope' parameter to 'document' to ensure context-aware translation.

Answer

Set the 'language' parameter to 'fr' and the 'from' parameter to 'en'.

Question 20

You need to build a solution that reads text from images in multiple languages, including Arabic and English, and translates the text into English. The solution must preserve the original layout as much as possible. Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence Read and Azure AI Translator. Azure AI Document Intelligence Read (formerly Form Recognizer Read) is optimized for extracting text from images and documents while preserving the original layout, including bounding box coordinates for each text element. Azure AI Translator then translates the extracted text into English. This combination meets the requirement for multi-language OCR (including Arabic and English) and layout preservation.

Answer

Azure AI Document Intelligence Read and Azure AI Language

Answer

Azure AI Vision OCR and Azure AI Translator

Answer

Azure AI Speech and Azure AI Translator