Microsoft Azure AI Fundamentals Advanced Practice Exam: Hard Questions 2025

A healthcare organization is implementing an AI system to assist radiologists in detecting tumors from medical images. The system occasionally produces false negatives, missing some tumors. During the model evaluation phase, which combination of ethical principles and technical metrics should be prioritized to minimize patient harm while maintaining system usefulness?

A financial services company is developing a machine learning model to predict loan default risk. After deployment, they notice the model performs significantly worse on applications from a specific geographic region that was underrepresented in the training data. The model's overall accuracy remains high at 92%. What is the PRIMARY issue this scenario represents, and what should be the remediation approach?

A data science team is building a regression model using Azure Machine Learning to predict energy consumption. They have 50,000 samples with 200 features. After training, the model achieves 98% R-squared on training data but only 65% R-squared on validation data. The team also notices that small changes in input features produce wildly different predictions. What is the MOST likely problem and appropriate solution?

An e-commerce company wants to build a recommendation system using Azure Machine Learning. They have both explicit feedback (product ratings) and implicit feedback (clicks, views, time spent). The system needs to handle the cold-start problem for new users and new products while scaling to millions of users and items. Which approach would be MOST effective?

A global manufacturing company is implementing Azure Computer Vision to perform quality control inspection of products on assembly lines across multiple factories. Each factory has different lighting conditions, camera angles, and slight variations in product appearance. The system needs to detect defects with 99%+ accuracy while minimizing false positives. What architectural approach would BEST address these requirements?

A security company is developing a facial recognition system using Azure Face API for building access control. They need to balance security (preventing unauthorized access) with user convenience (minimizing legitimate user rejections). The system will operate in varying lighting conditions and must handle scenarios where users wear glasses or have facial hair changes. What configuration strategy should they implement?

A multinational customer service platform is implementing Azure Cognitive Services for Language to analyze customer feedback across 15 languages. They need to perform sentiment analysis, extract key phrases, identify product mentions (custom entities), and detect language automatically. The solution must handle code-mixed text (multiple languages in one message) and maintain data residency compliance for EU customers. What is the MOST appropriate architecture?

A legal firm is building a document processing system using Azure Cognitive Services to analyze thousands of contracts. They need to extract specific clauses (termination conditions, liability limits, payment terms), understand relationships between entities (parties, dates, obligations), and summarize key points. The contracts vary in format (scanned PDFs, Word documents) and structure. Which combination of Azure services would BEST accomplish this?

A marketing company is deploying Azure OpenAI Service to generate personalized email campaigns for millions of customers. They need to ensure content appropriateness, prevent generation of harmful content, maintain consistent brand voice, control costs, and handle rate limiting for high-volume generation. What implementation strategy should they adopt?

A software company is integrating Azure OpenAI Service into their application to provide a conversational assistant that answers questions about their product documentation. The documentation is updated weekly, contains technical specifications, and users expect factually accurate responses without hallucinations. The system must cite sources and handle questions outside the documentation scope appropriately. What is the MOST effective architectural pattern?