AI-102 Practice Test 25 — 15 Questions

Question 1

You are designing a generative AI solution using Azure OpenAI Service with your own data indexed in Azure AI Search. Which THREE components are essential for the retrieval-augmented generation (RAG) pattern?

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

Accepted Answer

Data ingestion pipeline to Azure AI Search. A data ingestion pipeline is essential to load and index your data into Azure AI Search, enabling the retrieval step in RAG. Without this pipeline, the search index would have no data to query, breaking the retrieval-augmented generation pattern.

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Azure Functions for orchestration

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Azure API Management for rate limiting

Question 2

Which TWO Azure AI services can be used to build a conversational chatbot that uses generative AI? (Choose two.)

Accepted Answer

Azure AI Bot Service. Azure AI Bot Service (B) provides a dedicated platform for building, deploying, and managing conversational chatbots, integrating with channels like Microsoft Teams and Slack. Azure OpenAI Service (E) enables generative AI capabilities by providing access to large language models (e.g., GPT-4) that can generate human-like responses, making it ideal for powering the conversational intelligence of a chatbot.

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Azure AI Search

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Azure AI Translator

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Azure AI Language

Question 3

You are designing a solution that must extract personally identifiable information (PII) from medical records stored in Azure Blob Storage. The solution must redact the PII before storing the results. Which combination of Azure services should you use?

Accepted Answer

Use Azure AI Language's PII detection feature and a custom Azure Function to redact.. Azure AI Language's PII detection feature is specifically designed to identify and categorize PII entities in text, and combining it with a custom Azure Function allows you to programmatically redact those entities before storing the results in Blob Storage. This provides a serverless, scalable pipeline that meets the requirement of extracting and redacting PII from medical records without manual intervention.

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Use Azure AI Search with cognitive skills for PII detection.

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Use Azure OpenAI to detect and redact PII.

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Use Text Analytics for Health and then manually redact.

Question 4

You are designing an AI solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must handle a high volume of documents with varying layouts. Which approach should you use?

Accepted Answer

Train a custom neural model with labeled invoices. Custom neural models in Azure AI Document Intelligence are designed to handle high volumes of documents with varying layouts. Unlike fixed template models, neural models learn from labeled examples and generalize across different invoice structures, making them ideal for diverse, high-volume scenarios.

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Use a custom template model with fixed field positions

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Use the prebuilt invoice model

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Extract text using OCR and then use regex parsing

Question 5

Match each Azure AI tool to its purpose.

Accepted Answer

Azure Cognitive Search: AI-powered cloud search service for indexing and querying diverse content.. Correct matches: Azure Cognitive Search is an AI search service; Azure Bot Service is for building bots; Azure Cognitive Services offers pre-built AI APIs. Common confusions include swapping Cognitive Search with Machine Learning, and Bot Service with Cognitive Services.

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Azure Cognitive Search: Platform for building, training, and deploying machine learning models.

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Azure Bot Service: Pre-built APIs for vision and speech.

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Azure Cognitive Services: Tools to build and manage bots.

Question 6

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

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Increase the OCR confidence threshold in the analysis request

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

Question 7

You are deploying a Conversational Language Understanding (CLU) model to production. You need to monitor the model's performance and detect when retraining is needed due to concept drift. Which metric should you monitor?

Accepted Answer

Average confidence scores of predictions. Average confidence scores of predictions is the correct metric because a sustained drop in confidence indicates that the model is encountering utterances that differ from its training distribution, which is a classic sign of concept drift. Monitoring confidence scores allows you to detect when the model's predictions become less certain, triggering the need for retraining with new data.

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Response time for each prediction

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Number of endpoint calls

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Number of utterances processed per day

Question 8

A company uses the Face API to detect and identify employees for building access. They need to ensure that the system complies with GDPR requirements for biometric data. Which action should they take?

Accepted Answer

Obtain explicit consent from each employee before enrollment.. Under GDPR, biometric data (such as facial recognition templates) is classified as special category data requiring explicit consent for processing. The Face API itself does not manage consent; the responsibility lies with the application layer. Option C is correct because obtaining explicit consent from each employee before enrollment is a fundamental GDPR requirement for lawful processing of biometric data.

Answer

Store faces in a secure database and delete after 30 days.

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Anonymize the face data by blurring key features.

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Use encryption for stored face templates.

Question 9

An e-commerce company wants to build an agent that helps users track orders, initiate returns, and answer FAQs. The agent should be available on the company's website and mobile app. Which Azure service should the team use to deploy the agent?

Accepted Answer

Azure Bot Service. Azure Bot Service is the correct choice because it provides a managed environment for building, deploying, and scaling conversational AI agents that can be integrated with multiple channels, including websites and mobile apps. It supports the Bot Framework SDK, which enables the agent to handle order tracking, returns, and FAQs through natural language understanding (NLU) with LUIS or the newer CLU service.

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Azure Logic Apps

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Azure API Management

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Azure Functions

Question 10

A team is developing a solution to automatically summarize long documents using Azure AI Language. Which feature should they use?

Accepted Answer

Extractive summarization.. Extractive summarization is the correct feature because it specifically identifies and extracts the most important sentences from a document to create a concise summary. Azure AI Language's extractive summarization uses a ranking model to score sentences based on relevance and informativeness, directly addressing the requirement to automatically summarize long documents.

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Sentiment analysis.

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Key phrase extraction.

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Entity recognition.

Question 11

You have an Azure AI Vision resource named MyVisionService. You run the above Azure CLI command and get the keys. Your application uses key1 for authentication. You need to rotate the keys without downtime. What should you do?

Accepted Answer

Update the application to use key2, then regenerate key1. It enables key rotation without downtime. By first updating the application to use key2 (the secondary key), you ensure that authentication continues to work while key1 is being regenerated. After key1 is regenerated, you can optionally update the application back to key1 at a later time. This pattern is standard for Azure Cognitive Services to maintain continuous access.

Answer

Delete and recreate the Cognitive Services resource

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Regenerate key1 immediately and update the application to use the new key1

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Regenerate both keys at the same time

Question 12

You are deploying a chatbot using Azure AI Bot Service and Language Understanding (LUIS). The bot must understand user intent from free-text input. Which component should you train?

Accepted Answer

Language Understanding (LUIS) model. The Language Understanding (LUIS) model is the correct component to train because the bot needs to interpret free-text user input and extract intent. LUIS is a natural language processing service specifically designed for intent recognition and entity extraction from conversational phrases. Training the LUIS model with labeled utterances teaches it to map user expressions to predefined intents, enabling the bot to understand and respond appropriately.

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Speech-to-text model

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QnA Maker knowledge base

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Computer Vision model

Question 13

You are a lead AI engineer for a global retail company. The company is building an AI-powered customer support chatbot using Microsoft Foundry. The chatbot must answer product questions, process returns, and escalate to human agents when needed. The solution uses Azure AI Language for intent recognition and Azure AI Bot Service for bot orchestration. During testing, the chatbot fails to understand customer queries about return policies. The intents 'ProductInquiry' and 'ReturnRequest' are defined, but the model often confuses them. You need to improve intent classification accuracy. The development team has already collected 500 sample utterances for each intent. You have a budget to collect additional data. What should you do?

Accepted Answer

Collect additional utterances that are similar to the confusing ones and retrain. Collecting additional utterances that are similar to the confusing ones directly addresses the ambiguity between the 'ProductInquiry' and 'ReturnRequest' intents. By providing more representative examples of edge cases where the intents overlap, the Azure AI Language model can better learn the subtle linguistic patterns that distinguish them, thereby improving classification accuracy without requiring a complete retraining from scratch.

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Use Azure AI Translator to translate utterances into multiple languages

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Increase the confidence threshold in the bot configuration

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Create a new custom language project and retrain from scratch

Question 14

You are developing a solution that uses Azure AI Video Indexer to analyze surveillance videos for suspicious activity. The solution must generate alerts when a person is detected in a restricted area. Which feature should you use?

Accepted Answer

Azure AI Video Indexer people detection and tracking. Azure AI Video Indexer's people detection and tracking feature is specifically designed to detect and track individuals across video frames, making it ideal for generating alerts when a person enters a restricted area. This feature provides bounding boxes, timestamps, and tracking IDs that enable real-time monitoring and alerting based on spatial rules.

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Azure AI Video Indexer sentiment analysis

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Azure AI Face identify API

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Object detection in Azure AI Vision

Question 15

Your team is developing a chatbot using Azure AI Bot Service with Language Understanding (LUIS). You need to improve intent recognition for user queries that contain typos. What should you recommend?

Accepted Answer

Enable Bing Spell Check in the LUIS app settings. Enabling Bing Spell Check in the LUIS app settings allows the service to automatically correct typos in user utterances before they are processed for intent recognition. This built-in feature integrates directly with LUIS at the endpoint, correcting misspelled words to improve the accuracy of intent and entity extraction without requiring manual data augmentation or external services.

Answer

Add synonym lists for common misspellings

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Use Azure Cognitive Search to correct typos

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Train the model with many examples containing typos