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1. A government agency is implementing AI in public services and wants to use a virtue ethics framework.

What should be the main ethical consideration?

2. At a board strategy session, a director expresses skepticism about existential AI risks, citing that current AI is narrow and not close to superintelligence.

Which argument would best support the director’s stance?

3. An investment firm applies the Monte Carlo method for a finite series of market simulations to calculate expected portfolio returns. The method converges slowly, especially during long simulations.

Which of the following describes a primary disadvantage of the Monte Carlo method for this application?

4. A hedge fund is using a reinforcement learning algorithm to optimize the timing of its trades in the stock market to maximize profit over time.

Which of the following best explains how reinforcement learning is suited for this task?

5. In a regression model y=β0+β1x+u, if the parameter β0 (intercept) is estimated to be 2.5, what is the interpretation of this estimate?

6. An analyst uses K-means clustering on a dataset with 500 points and considers different values of K.

According to the rule of thumb, what is the approximate optimal value of K?

7. A retail company uses AI for personalized advertising but receives complaints about invasive targeting.

What should the company do to address potential reputational damage?

8. A bank is training a neural network model to classify loan defaults. During training, the team uses backpropagation to update the weights.

Which of the following best describes how errors are managed in backpropagation?

9. A bank is building a predictive model to assess credit risk, and the analyst wants to include a categorical variable for "Education Level" with categories: "High School," "Bachelor's," "Master's," and "PhD."

Which encoding method is most appropriate to avoid introducing artificial ordering?

10. Why did adding multiple layers to neural networks help overcome the limitations highlighted by Minsky and Papert’s critique of single-layer Perceptrons?

11. An analyst is examining customer satisfaction levels (low, medium, high) to develop a predictive model.

Which type of model should the analyst use to incorporate the ordered nature of satisfaction levels?

12. A multinational corporation is using a neural network to assess employee performance. The HR team is concerned about ensuring fairness and accountability in the AI’s decisions.

Which approach would best address these concerns?

13. An e-commerce platform wants to create a more compact representation of its product features. The team is considering using an autoencoder with fewer hidden units than input features.

What advantage does this setup provide?

14. To improve predictions for identifying potential loan defaults, a bank employs a method that successively builds models, with each model focusing on the errors of the previous ones.

This technique is known as:

15. A financial institution finds that the system surrounding its risk model no longer aligns with its IT infrastructure requirements.

To align with new standards, which adaptation task should they focus on?

16. A healthcare provider faces an accuracy-interpretability trade-off when choosing between a complex AI model and a simpler, interpretable model for diagnosis.

Which of the following would be the most practical reason for choosing the complex model over the interpretable one?

17. A financial institution is analyzing a dataset to classify customers as high or low risk. The dataset includes some labeled data but primarily consists of unlabeled data points. They want a model that can be applied to new customer data in the future.

Which semi-supervised learning approach would be most suitable?

18. A financial analyst wants to apply a regularized regression model that reduces extreme coefficient values without eliminating any features, as they all provide valuable insights.

Which regularization method is most appropriate?

19. In a Q-Q plot comparing an empirical distribution to a theoretical normal distribution, what indicates a close match between the two?

20. A financial analyst needs to perform a complex optimization task as part of their machine learning workflow in Python.

Which library would be most appropriate for this purpose?

21. A tech company tests three different user interface designs for maximizing user engagement. They have limited user interaction data initially and want to avoid prematurely committing to any design.

Which strategy would help them balance exploration and exploitation?

22. A financial firm is developing a quantitative risk model (QRM) to project the future value of its investment portfolio.

Which component is essential to properly assess potential outcomes in this model?

23. Which early concept in neural network theory proposed that repeated activation of one neuron by another would strengthen their connection?

24. What is a primary reason to remove duplicate observations during data cleaning?

25. A financial analyst is fitting a nonlinear model to predict stock returns based on various economic indicators. She decides to use Nonlinear Least Squares (NLS) instead of OLS due to the non-linear nature of the relationship.

Which of the following best describes why NLS is more suitable than OLS in this case?

26. A hiring tool favors candidates from certain universities, as historical data used to train the tool includes past hiring trends that disproportionately favored those institutions.

What best describes this type of bias?

27. An investment firm is using machine learning to classify news articles as positive, neutral, or negative. The team uses a Naïve Bayes classifier for this purpose.

Which of the following statements is true about Naïve Bayes in this context?

28. An insurance company is using a neural network for classifying claims as "Fraudulent" or "Non- Fraudulent." They decide to use ReLU (Rectified Linear Unit) as the activation function for the hidden layers.

What is the primary purpose of using an activation function like ReLU in this neural network?

29. During an NLS optimization, the analyst uses a gradient descent algorithm to update model parameters. If the improvement in the objective function falls below a certain threshold, the optimization process stops.

What is the purpose of this threshold in NLS optimization?

30. A risk analyst is advising a company on a new AI model for employee productivity tracking.

According to the principle of nonmaleficence, what should be the analyst’s primary recommendation?

31. A tech company uses a small set of labeled network data to detect anomalous activities while most of the dataset remains unlabeled. They aim to detect anomalies in real time but are unsure if their data labeling will scale.

Which learning method would best balance accuracy and scalability?

32. A justice system uses an AI model to predict failure-to-appear (FTA) rates in court.

To ensure fairness without explicitly using protected group data, what could be a potential unintended outcome?

33. A hedge fund manager wants to classify market sentiment from daily news headlines using a bag-of- words (BoW) approach. They choose a vocabulary consisting of specific finance-related words like “bull,” “bear,” “rally,” and “crash.”

How will this choice impact the BoW vectors for each headline?

34. A data scientist is tuning the ridge regression model’s hyperparameter, λ, to control the trade-off between model fit and complexity.

If λ is set too high, what effect is most likely?

35. A data scientist at a hedge fund uses grid search to optimize hyperparameters for a trading model. Concerned about computational efficiency, they consider switching to a random search.

What is a key advantage of using random search over grid search in this context?

36. Which of the following scenarios represents a binary classification problem?

37. A financial institution is implementing data access controls to comply with the Gramm-Leach-Bliley Act (GLBA).

What should be a primary focus to meet compliance requirements?

38. A bank has gathered customer comments following transactions and wishes to classify them as "positive" or "negative" for sentiment analysis. However, only 10% of the comments are labeled by a human, as it’s costly to classify each one manually.

Which of the following methods would likely be most effective for the bank?

39. A model developer is implementing Taylor series approximations to simplify market risk calculations for a portfolio with small changes in risk factors.

What should they prioritize to avoid misuse of this approximation?

40. Suppose a variable in a dataset has values between 0 and 1000. You normalize this variable to the range [0, 1]. After scaling, you find a new observation with a value of 1500.

What is the normalized value of this observation?