AI-102 Practice Test 9 — 10 Questions

Question 1

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?

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

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.

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

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Configure Custom Vision to send alerts when drift is detected.

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

Question 2

You are building a chatbot that must understand user intents from free-text input. You have a small set of labeled examples. Which Azure AI Language feature should you use to classify intents with minimal effort?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct choice because it is specifically designed to extract intents and entities from free-text input in a conversational context, and it can be trained with a small set of labeled examples to classify user intents with minimal effort. CLU provides a pre-built pipeline for intent recognition and entity extraction, making it the most efficient option for building a chatbot that understands user intents.

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Entity Linking

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Custom Text Classification

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Language Detection

Question 3

A company wants to build a solution that can identify and redact personally identifiable information (PII) from customer support transcripts. The solution must handle multiple languages. Which Azure AI service should be used?

Accepted Answer

Azure AI Language - PII Detection. Azure AI Language's PII Detection feature is specifically designed to identify and redact personally identifiable information in text across multiple languages. It supports over 30 languages and can detect entities such as names, addresses, phone numbers, and credit card numbers, making it the correct choice for this multilingual PII redaction requirement.

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Azure AI Content Safety

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Azure AI Document Intelligence

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Azure AI Translator

Question 4

Which THREE factors are critical to consider when designing a custom vision solution for a manufacturing quality inspection system?

Accepted Answer

Imbalance between defective and non-defective product samples.. Class imbalance is a critical factor in custom vision solutions for manufacturing quality inspection. If defective samples are rare compared to non-defective ones, the model may become biased toward predicting the majority class, leading to poor recall for defects. Azure Custom Vision allows adjusting the probability threshold and using techniques like oversampling or weighted loss to mitigate this, but the imbalance must be accounted for during dataset preparation.

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The need for optical character recognition (OCR) of product serial numbers.

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Multilingual support for labeling.

Question 5

Your organization needs to monitor Azure AI services for unusual activity patterns that might indicate a security threat. Which Microsoft security solution should you use?

Accepted Answer

Microsoft Sentinel. Microsoft Sentinel is a cloud-native Security Information and Event Management (SIEM) and Security Orchestration Automated Response (SOAR) solution. It is specifically designed to ingest logs from Azure AI services, apply analytics to detect unusual activity patterns, and generate alerts for potential security threats, making it the correct choice for monitoring AI services for security anomalies.

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Microsoft Intune

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Microsoft Defender XDR

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Microsoft Purview

Question 6

Your organization is using Azure AI Search to index a large collection of PDF documents stored in Azure Blob Storage. The index currently returns search results, but users complain that the results are not relevant when they search using natural language phrases. You need to improve the relevance of search results without rewriting the application. What should you do?

Accepted Answer

Enable semantic search on the index and configure a semantic configuration.. Semantic search in Azure AI Search uses advanced language models to understand the intent behind natural language queries, re-ranking results based on semantic relevance rather than just keyword matching. Enabling semantic search and configuring a semantic configuration directly addresses the user complaint about poor relevance for natural language phrases without requiring application changes.

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Increase the number of replicas for the search service to improve query performance.

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Create a new index with a blob indexer that uses the 'content' field only.

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Configure a custom analyzer on the index to handle stop words and synonyms.

Question 7

A manufacturing company uses Azure AI Custom Vision to detect defects on a production line. The model was trained with 500 images per class and achieves 95% accuracy. After deployment, the model's accuracy drops to 80% due to changes in lighting conditions. What is the most effective first step to improve the model's robustness?

Accepted Answer

Capture additional images under the new lighting and retrain the model.. The drop in accuracy is caused by a domain shift—specifically, new lighting conditions that were not represented in the original training set. The most effective first step is to capture additional images under the new lighting and retrain the model, as Custom Vision relies on diverse, representative training data to generalize to real-world variations. This directly addresses the root cause by expanding the training distribution to include the new lighting scenario, which is a fundamental principle of supervised learning in computer vision.

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Reduce the probability threshold to increase recall.

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Use Azure AutoML to automatically find the best algorithm.

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Add more images from the original lighting conditions to the training set.

Question 8

Which TWO Azure AI services can be used to extract text from images and PDFs? (Select two.)

Accepted Answer

Azure AI Vision OCR. Azure AI Vision OCR (option C) is correct because its Read/OCR capability extracts printed and handwritten text directly from images and scanned documents. Azure AI Document Intelligence (option D) is correct because it uses prebuilt and custom models to extract text, key-value pairs, and tables from PDFs, images, and forms. Azure AI Translator (A) performs text translation, not optical character recognition, so it cannot extract text from images or PDFs. Azure AI Search (B) is a search indexing service that can consume extracted text but does not itself perform OCR on images or PDFs. Azure AI Language (E) provides NLP features such as sentiment analysis and entity recognition on existing text, not text extraction from images or PDFs.

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Azure AI Translator

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Azure AI Search

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Azure AI Language

Question 9

Which THREE factors should you consider when choosing between Azure AI Custom Vision and Azure AI Vision pre-built models for an image classification task? (Choose three.)

Accepted Answer

Availability of labeled training data specific to the domain. Azure AI Custom Vision is specifically designed for scenarios where you have domain-specific labeled training data that is not covered by pre-built models. Custom Vision allows you to upload your own labeled images and train a custom model tailored to your unique classification needs, which is essential when off-the-shelf models fail to recognize your target classes.

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Image format support (JPEG, PNG)

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Need for real-time inference latency

Question 10

Your company has a large repository of scanned invoices in PDF format. You need to extract invoice number, date, total amount, and vendor name from these PDFs. Which Azure AI service should you use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is purpose-built for extracting structured data (fields like invoice number, date, total amount, vendor name) from scanned documents such as invoices and receipts. It uses prebuilt models trained specifically on invoice layouts and supports both OCR and key-value pair extraction from PDFs.

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Azure AI Language

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Azure AI Search

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Azure AI Vision