1. A retail chain wants to implement an AI solution to improve inventory management. The team decides to forecast product demand using historical sales data: Which type of machine learning algorithm is most appropriate for this problem?
2. Which AI application is best suited for reducing customer churn in a subscription-based service?
3. A company wants to detect fraudulent transactions. Which algorithm should they prioritize for high interpretability and reliability?
4. A predictive maintenance system for machinery uses ML. The model’s success depends on the accuracy of the predictions. What metric should the team focus on?
5. Which AI application is most suitable for automating product recommendations on an e-commerce platform?
6. A healthcare provider uses ML for disease diagnosis. Which factor is most critical for ensuring reliable predictions?
7. When presenting an AI model’s findings to non-technical stakeholders, what is the best approach?
8. A loan approval AI system exhibits bias against a minority group. What is the primary cause of this issue?
9. A team is building a predictive model with a limited dataset of high-quality records. What strategy will maximize the model’s performance?
10. A machine learning team has a large dataset with significant noise. Which step should they prioritize?
11. During data preprocessing, which transformation is best for handling skewed numerical features?
12. Which technique is most effective for dealing with categorical data before inputting into an ML algorithm?
13. Which method is most appropriate for extracting meaningful features from audio data?
14. A company wants to analyze customer feedback emails. Which processing step is crucial for handling textual data?
15. A dataset contains numerical features with varying ranges. What is the recommended transformation for features like "price" and "age"?
16. A company uses customer demographics for credit scoring. What ethical issue should the team address?
17. What is the main business risk when working with uncleaned, inconsistent data in a production ML system?
18. You need to develop a model for detecting spam emails. Which type of learning algorithm is most appropriate?
19. For image classification tasks, which architecture is most commonly used?
20. Which optimization technique adjusts the step size dynamically during training?
21. What is the primary benefit of hyperparameter tuning in machine learning models?
22. Why is it important to maintain a separate test dataset when training a model?
23. Which strategy ensures a fair evaluation of model performance on small datasets?
24. A model has a high accuracy but poor recall. What issue does this indicate?
25. Which evaluation metric is most suitable for an imbalanced dataset?
26. What is the primary ethical concern when deploying a predictive policing AI model?
27. Which business risk arises from a lack of continuous monitoring of deployed models?
28. A company wants to deploy a machine learning model in a production environment. Which deployment strategy ensures minimal downtime during deployment?
29. Which is the best practice for deploying models in a microservices architecture?
30. What is the primary security concern when exposing a model as an API endpoint?
31. Which method helps secure sensitive data in an ML pipeline?
32. A model's performance decreases over time due to changes in user behavior. What maintenance approach addresses this issue?
33. What is the purpose of model versioning in a production environment?
34. A company discovers that its AI model produces biased results for certain demographic groups. What immediate action should they take?
35. What business risk arises if a model deployed for fraud detection starts producing false positives frequently?
36. An AI system predicts outcomes that negatively impact specific stakeholders. What ethical framework can help evaluate its deployment?
37. You are working with a dataset where 40% of the entries are missing for a key numerical feature. What is the most appropriate preprocessing strategy?
38. Your dataset includes a categorical feature with 50 unique values. Which encoding method minimizes the risk of creating a sparse dataset?
39. A dataset contains time-series data with timestamps at irregular intervals. Which preprocessing step is necessary before model training?
40. You are building a deep learning model with imbalanced classes. What is the most effective technique to handle this imbalance?
41. Which model is most appropriate for anomaly detection in transactional data?
42. You have a classification problem where interpretability is a priority. Which model should you use?
43. Which of the following techniques is best for hyperparameter tuning when computational resources are limited?
44. During training, you notice your model consistently overfits the training data: What is the most effective strategy to mitigate this issue?
45. A binary classifier outputs high accuracy but low precision. What does this indicate?
46. Which metric is most appropriate for evaluating the performance of a model on highly imbalanced data?
47. You are deploying an ML model in a serverless environment. Which framework is best suited for scaling the deployment?
48. Which deployment strategy is best for testing a new version of a model without disrupting the current production system?
49. What is the primary purpose of concept drift detection in an ML pipeline?
50. Which tool is most suitable for setting up continuous monitoring of ML models in production?
51. A production model's predictions are unexpectedly incorrect for a certain subset of inputs. What is the first step in troubleshooting?
52. You observe high latency in predictions from your deployed ML model. What is a common cause of this issue?
53. A financial institution deploys an AI model for credit scoring. Which regulation ensures compliance with personal data handling?
54. An ML model is flagged for potential discrimination against specific demographics. Which action is ethically and technically appropriate?
55. You are tasked with building a real-time fraud detection system. Which combination of technologies is most suitable?
56. Which approach is best for reducing model inference time in edge devices?
57. A client requests to integrate real-time weather data into a predictive model. Which architecture best supports this integration?
58. Which approach is effective for handling data imbalance in binary classification?
59. Which technique helps reduce overfitting in a deep learning model?
60. What is a key advantage of early stopping in neural network training?
61. Which method provides feature importance rankings for a trained Random Forest model?
62. For a text classification model, which tool can explain predictions by analyzing word contributions?
63. Which metric best identifies prediction drift in a regression model post-deployment?
64. You detect reduced accuracy in a production model. Which is the most likely cause?
65. What is the primary advantage of using distributed training for deep learning models?
66. Which framework best supports parallelized data preprocessing for large-scale datasets?
67. You are tasked with improving customer sentiment analysis for a multinational retailer. Which approach best handles multilingual text data?
68. An AI model predicts loan approvals but consistently favors a particular demographic. What tool can be used to audit and address this bias?
69. Which unsupervised learning technique is best for reducing the dimensions of a dataset with highly correlated features?
70. A recommendation system must provide personalized suggestions based on past user behavior. Which algorithm is most suitable?
71. Which technique is most effective for fine-tuning a pre-trained deep learning model for image classification on a new dataset?
72. A pre-trained language model generates grammatically incorrect sentences for a specific task. What is the best approach to improve performance?
73. A recommendation engine must integrate seamlessly with an e-commerce platform. Which deployment strategy ensures high availability?
74. Which tool is best suited for end-to-end orchestration of ML workflows, including preprocessing, training, and deployment?
75. Which metric evaluates both recall and precision, especially when false positives and false negatives are equally costly?
76. For a multi-class classification problem, which evaluation metric is most comprehensive?
77. In a production ML system, which mechanism ensures the system adapts to significant changes in input data distributions?
78. What should be prioritized when replacing an outdated model in a live production system?
79. Which cloud-native service is most suitable for deploying a scalable deep learning model?
80. To optimize a machine learning model for inference on a mobile device, which approach should you take?