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1. Which type of machine learning is best suited for detecting patterns in unlabeled data, such as grouping similar stocks based on characteristics?

2. An investment firm seeks to classify new stock data based on limited labeled examples of previous stocks as "high volatility" or "low volatility." They plan to use this model for classifying future stock data as well.

Which technique should they consider?

3. A credit risk model using a neural network shows a large gap between training and test error.

Which of the following techniques would be most effective in addressing this issue?

4. In a risk model using probability distributions not available from empirical data, what is the recommended method for generating values to assign to random variables?

5. A tech company’s fraud detection algorithm has high accuracy but frequently flags transactions from certain minority groups as fraudulent.

What issue does this illustrate?

6. An analyst performs the operation: vector("Europe") = vector("France") - vector("Paris") + vector("Berlin").

What kind of relationship is the analyst trying to identify?

7. During exploratory data analysis, what does a boxplot help to identify?

8. An investment firm’s sentiment analysis of earnings call transcripts is skewed by excessive repetition of words like “growth” and “profit.”

To ensure no single word overpowers the sentiment vector, what approach should the firm take?

9. The Board is concerned about talent retention, as recent surveys show employees are increasingly concerned with the company’s ethics.

How could implementing a practical ethics framework benefit talent retention?

10. A data scientist is working with financial transaction data where transaction amounts vary significantly, ranging from a few cents to thousands of dollars. She decides to apply feature scaling to improve model performance.

Which approach should she use if her data contains several extreme outliers?

11. A bank is analyzing customer feedback to classify it as "Good," "Bad," or "Indifferent" using a Naïve Bayes classifier.

What is a major assumption made by the Naïve Bayes approach in this context?

12. An analyst uses a linear regression model to predict stock returns but notices a pattern in the residual plot, with residuals spreading out as fitted values increase.

What problem might the model be experiencing?

13. A bank analyst wants to classify a new feedback document using Naïve Bayes. The document contains words previously marked as negative.

Which step should the analyst prioritize to classify this document?

14. A financial analyst is building an NLP model to analyze customer feedback on loan services. She notices many comments use negation, such as “not helpful” and “not satisfied.”

To improve sentiment accuracy, what technique should she consider?

15. A data scientist builds a model to predict house prices based on the age of the house. They test three models: a linear, a quadratic, and a ninth-degree polynomial.

Which of these models is most likely to balance bias and variance well?

16. A risk analyst at a hospital is reviewing an AI tool for triaging emergency cases, prioritizing those with the most severe needs.

From a consequentialist perspective, which approach would be ethically justified?

17. In model validation, why is it important to conduct “effective challenge” by independent parties?

18. A bank is reviewing its risk framework and needs to clarify what constitutes a “model.”

What should the bank emphasize in its definition?

19. A financial institution uses a neural network model with thousands of parameters to predict loan defaults. On the training dataset, the model has a nearly zero residual sum of squares (RSS). However, it performs poorly on new data.

What does this indicate?

20. An AI-based credit assessment tool is found to have a high error rate due to outdated data and inadequate updates.

Which criticism category does this best fit?

21. After applying a log transformation to a highly skewed dataset, the skewness was reduced from 1.61 to 0.05.

Which of the following best explains why log transformation is beneficial in this context?

22. A bank decides to use a simple linear regression model to understand the impact of economic variables on credit risk. They aim for a clear understanding of causal relationships.

What is likely sacrificed in this choice?

23. A bank is analyzing customer feedback on its services and encounters a review that heavily repeats the word “bad,” causing this term to dominate the analysis.

Which pre-processing step can help mitigate the impact of this repeated word in the analysis?

24. A portfolio manager is using a multi-arm bandit model to decide daily between several stocks for short- term investments. Each stock has a different historical return profile.

In this scenario, what would be the "reward" in MAB terminology?

25. A financial firm aims to improve fraud detection on a partially labeled dataset. They apply principal component analysis (PCA) to reduce data dimensionality before using labeled data to train their model.

Which unsupervised pre-processing method best describes this approach?

26. A bank uses stratified k-fold cross-validation to validate a fraud detection model, but the dataset has an extreme imbalance: 1% fraud cases and 99% non-fraud cases.

Which additional technique can help balance the classes for better model performance?

27. An analyst uses stepwise regression to build a predictive model for house prices. After starting with an empty model, the analyst adds features one by one until no further reduction in the Akaike Information Criterion (AIC) is achieved.

Which method is the analyst using?

28. A risk manager is evaluating an NLP-based sentiment analysis model that uses dictionary approaches across multiple datasets. He notices inconsistent results when analyzing different document types.

What could explain this inconsistency?

29. A financial services firm is using a neural network with multiple hidden layers to predict the likelihood of loan default (binary classification). They find that the model isn’t learning well with the logistic activation function in the hidden layers.

Which activation function should they consider switching to improve learning?

30. A municipality’s crime prediction algorithm tends to assign higher risk scores to certain groups based on historical crime data.

What challenge does this present in achieving fairness?

31. A government agency uses an AI system to determine eligibility for welfare benefits but faces public concerns about fairness due to its black-box nature.

What is the best step to increase transparency?

32. A tech-savvy customer notices that a bank’s chatbot can handle complex inquiries and respond in a conversational tone.

Which of the following likely enables this advanced capability?

33. A financial analyst is working with a large dataset to build a credit scoring model. The analyst decides to use two-thirds of the data for training, one-sixth for validation, and one-sixth for testing.

What is the primary purpose of the validation set in this case?

34. A financial analyst is using logistic regression to predict the probability of a loan default based on borrower characteristics. The output probability ranges between 0 and 1.

What transformation allows this model to constrain the output in this range?

35. What is a key limitation of using ChatGPT for code generation in complex programming tasks?

36. A financial analyst is using batch gradient descent to train a risk model but notices that the convergence is extremely slow.

Which of the following adjustments could help accelerate convergence?

37. A credit scoring team is using an SVM model to classify loan applicants as either "Low Risk" or "High Risk." They find that some applicants with low income but stable employment history are classified as "High Risk," even though they have a low probability of defaulting.

How can the team adjust their SVM model to account for these cases?

38. A fintech company builds multiple decision trees on different samples of its credit dataset, each tree trained on randomly selected data points with replacement. Afterward, they aggregate predictions by taking the majority vote for classification.

Which ensemble technique is being used?

39. A fraud detection model is tested using a widely available benchmark dataset, but its real-world performance is suboptimal.

Which factor is most likely to cause this issue?

40. A QRM development team has created a market risk model for derivative products.

Which approach should they use to prevent underestimating risks?