IBM A1000-119 Advanced Practice Exam: Hard Questions 2025

A financial institution is implementing an AI system for credit risk assessment that must handle concept drift in customer behavior patterns while maintaining regulatory compliance. The system shows degrading performance over time despite initially high accuracy. Which combination of approaches would BEST address this challenge while ensuring explainability for regulatory audits?

An enterprise AI team notices that their deep learning model for medical image classification achieves 98% accuracy on the training set but only 76% on the validation set, with particular difficulty on rare disease cases that represent 5% of the dataset. The model uses a standard cross-entropy loss function. What is the MOST comprehensive approach to address this issue?

A manufacturing company is deploying an AI-powered predictive maintenance system that must operate in real-time on edge devices with limited computational resources. The system needs to process sensor data from 500+ data points every 100ms and predict equipment failures. Which architectural approach would BEST balance performance, latency, and resource constraints?

During the development of a conversational AI system for customer service, the team discovers that the model occasionally generates responses containing personally identifiable information (PII) from training data, despite data anonymization efforts. The system uses a large language model fine-tuned on customer interaction logs. What is the MOST effective multi-layered approach to mitigate this privacy risk?

An AI research team is comparing different neural network architectures for a computer vision task. They observe that a convolutional neural network (CNN) significantly outperforms a standard fully-connected network despite having fewer total parameters. When analyzing the receptive fields and parameter sharing mechanisms, what fundamental principle BEST explains this performance difference?

A data science team is building a classification model and must choose between optimizing for precision versus recall. The application is a fraud detection system for financial transactions where false positives result in customer inconvenience and manual review costs ($10 per case), while false negatives result in actual fraud losses (average $500 per case). The baseline model shows 85% precision and 70% recall. Which optimization strategy should they pursue?

A healthcare organization is implementing an AI diagnostic assistant that will provide treatment recommendations to physicians. During testing, they discover that the model performs differently across demographic groups, with 92% accuracy for the majority population but only 78% accuracy for underrepresented minorities. What is the MOST comprehensive approach to address this algorithmic bias while maintaining clinical utility?

An e-commerce company is developing a recommendation system that must balance multiple competing objectives: maximizing user engagement, promoting product diversity, ensuring fairness to sellers, and optimizing revenue. Their current collaborative filtering approach optimizes only for predicted ratings. Which advanced approach would BEST address this multi-objective optimization challenge?

A machine learning engineer is debugging a gradient descent optimization issue where the training loss oscillates wildly and fails to converge. After investigation, they find that different features in the dataset have vastly different scales (some in range 0-1, others in range 0-10000). The model uses a fixed learning rate of 0.01. What combination of techniques would MOST effectively address this convergence problem?

An organization is establishing an AI governance framework for deploying multiple AI systems across different business units. They need to ensure responsible AI practices, regulatory compliance, risk management, and accountability. Which governance structure would provide the MOST comprehensive and effective framework?