AI0-001 AI Concepts and Foundations Practice Test 2 — 15 Questions

Question 1

A financial institution is deploying a reinforcement learning agent to optimize stock trading decisions. The agent is trained in a simulated environment that mimics historical market data. After deployment, the agent performs well initially but then suffers large losses during a period of high volatility that was underrepresented in the training data. The team wants to make the agent more robust to such market conditions without retraining from scratch. They have a budget for additional simulation compute and access to a broader historical dataset including past crises. The agent uses a deep Q-network (DQN) architecture. Which strategy should they adopt?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Modify the DQN to use a recurrent neural network (e.g., DRQN) and train on the expanded dataset. A Deep Recurrent Q-Network (DRQN) can capture temporal dependencies in market data, which is crucial for handling volatile periods that were underrepresented in training. By training on the expanded dataset that includes past crises, the agent can learn from sequential patterns of volatility, making it more robust without requiring a complete retraining from scratch. This approach leverages the existing DQN architecture while adding recurrent layers to better model the dynamic market conditions.

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Increase the replay buffer size and continue training on the original dataset

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Keep the DQN but perform extensive hyperparameter tuning on the original data

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Switch to a policy gradient method with a random exploration strategy

Question 2

A financial services company is developing an AI model to detect fraudulent transactions. The dataset contains 99.9% legitimate transactions and 0.1% fraudulent ones. Which technique should the data scientist use to address the class imbalance problem?

Accepted Answer

Apply Synthetic Minority Oversampling Technique (SMOTE). SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic examples of the minority class (fraudulent transactions) by interpolating between existing minority instances, rather than duplicating them. This addresses the extreme 0.1% fraud rate without introducing overfitting or losing data, making it a standard technique for imbalanced classification problems in financial fraud detection.

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Use a bagging ensemble method

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Undersample the legitimate transactions

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Use cost-sensitive learning with higher weight on fraudulent class

Question 3

A data scientist is training a supervised learning model for customer churn prediction. Which TWO types of bias are most likely to affect the model's fairness and accuracy if not addressed?

Accepted Answer

Selection bias. Selection bias (B) occurs when the training data does not represent the true customer population, e.g., using only data from a specific time period or region, leading to a model that fails to generalize. Measurement bias (C) arises from systematic errors in how features are recorded, such as inconsistent data collection methods across customer segments, which can skew predictions and harm fairness.

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Algorithmic bias

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Sampling bias

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Confirmation bias

Question 4

A healthcare startup is building an AI system to predict patient readmission risk. The team collects structured data from electronic health records (EHR) including age, diagnosis codes, lab results, and previous admissions. During initial training, the model achieves 95% accuracy on the validation set but only 60% accuracy on a holdout test set from a different hospital. The data scientist suspects overfitting. Which action should the team take first to improve generalization?

Accepted Answer

Apply L2 regularization to the model. The model's high accuracy on the validation set but poor accuracy on a holdout test set from a different hospital indicates overfitting to the training data's specific patterns, which do not generalize to new data. L2 regularization (ridge regression) adds a penalty proportional to the square of the weights, discouraging the model from fitting noise and encouraging simpler, more generalizable decision boundaries. This directly addresses overfitting by reducing variance without requiring more data or reducing model capacity too drastically.

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Switch to a linear regression model

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Increase the model complexity by adding more layers

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Collect more data from the same hospital

Question 5

A retail company wants to implement a recommendation system using collaborative filtering. The dataset contains user-item interactions (ratings) for 10,000 users and 5,000 products. The matrix is very sparse (99% missing values). The team plans to use matrix factorization to predict missing ratings. However, the training time is excessively long, and the model is not converging. The data engineer suggests using a smaller learning rate and more iterations. Which additional technique should the team apply to speed up training and improve convergence?

Accepted Answer

Add L2 regularization to the loss function. Adding L2 regularization to the loss function helps prevent overfitting and improves convergence in matrix factorization, especially with extremely sparse data (99% missing). Regularization penalizes large latent factor weights, which stabilizes the optimization process and allows the model to generalize better, reducing the risk of divergence during training.

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Increase the minibatch size

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Reduce the number of latent factors

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Switch to the Adam optimizer

Question 6

A manufacturing company is using a convolutional neural network (CNN) to detect defects on an assembly line. The model was trained on a balanced dataset of defective and non-defective parts. In production, the model shows high precision (95%) but very low recall (50%). The production line manager wants to minimize missed defects (false negatives). The data scientist has access to the original training data and can retrain the model. Which strategy is most effective for increasing recall while maintaining acceptable precision?

Accepted Answer

Lower the classification threshold for the defective class. Lowering the classification threshold for the defective class directly addresses the recall issue by allowing more samples to be classified as defective, which reduces false negatives. This is the most immediate and effective method because it does not require retraining and can be tuned to balance precision and recall based on the manager's priority of minimizing missed defects.

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Apply data augmentation to defective images

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Use a bagging ensemble of CNNs

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Oversample the defective class in training

Question 7

Which TWO of the following are key stages in the AI lifecycle?

Accepted Answer

Model deployment. Model deployment (C) is a key stage in the AI lifecycle because it involves integrating the trained model into a production environment where it can make predictions on live data. This stage is critical for operationalizing AI and delivering business value, following the model training and validation phases.

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Human annotation of all data

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Model retraining

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Manual feature extraction

Question 8

A company deploys an AI model to predict equipment failure. The model performs well on historical data but fails to generalize to new data from a different factory. Which concept best describes this issue?

Accepted Answer

Overfitting. (Overfitting) is correct because the model learned patterns specific to the historical data from the original factory, including noise and factory-specific nuances, rather than generalizable features. When applied to new data from a different factory, those learned patterns do not hold, causing poor performance. This is the classic symptom of overfitting: high accuracy on training data but low accuracy on unseen data.

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Transfer learning

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Underfitting

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Bias-variance tradeoff

Question 9

A data scientist trains a linear regression model to predict house prices. The model has high bias and low variance. Which action would most likely reduce bias?

Accepted Answer

Add polynomial features. High bias indicates the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding polynomial features increases model complexity by introducing non-linear terms, which allows the linear regression model to better fit the training data and thus reduce bias.

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Apply L2 regularization

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Increase the training dataset size

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Remove irrelevant features

Question 10

A company implements a chatbot using a rule-based system. Users complain the chatbot cannot handle new queries. Which AI approach should be considered to improve flexibility?

Accepted Answer

Machine learning. Machine learning (ML) enables a chatbot to learn from new data and adapt to unseen queries, unlike a static rule-based system. By training on historical conversations, an ML model can generalize patterns and handle novel inputs without requiring explicit rules for every scenario.

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Expert system

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Natural language processing (NLP)

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Robotic process automation

Question 11

An AI model for detecting fraudulent transactions has high precision but low recall. Which business impact is most likely?

Accepted Answer

Many fraudulent transactions go undetected. High precision means that when the model flags a transaction as fraudulent, it is very likely correct. However, low recall indicates that the model misses a significant proportion of actual fraudulent transactions. Therefore, the most likely business impact is that many fraudulent transactions go undetected, leading to financial losses.

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The model has no impact on fraud detection

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The model detects all fraudulent transactions

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Many legitimate transactions are flagged as fraud

Question 12

A data scientist splits a dataset into training (80%) and test (20%). After training, the model achieves 95% accuracy on training and 60% on test. Which step should the data scientist take first?

Accepted Answer

Apply regularization. The model shows high training accuracy (95%) but significantly lower test accuracy (60%), which is a classic sign of overfitting. Regularization (Option C) directly addresses overfitting by adding a penalty term to the loss function (e.g., L1 or L2 regularization), discouraging the model from learning overly complex patterns that do not generalize. This is the first step because it targets the core issue without requiring additional data or increasing complexity.

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Collect more data

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Use cross-validation

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Increase model complexity

Question 13

An organization wants to classify support tickets into categories (billing, technical, etc.). Which type of machine learning is most suitable?

Accepted Answer

Supervised learning. Supervised learning is the correct choice because the organization has labeled historical support tickets (e.g., 'billing' or 'technical') and wants to train a model to map new tickets to these predefined categories. This is a classic classification task, where the algorithm learns from input-output pairs to predict the correct label for unseen data.

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Unsupervised learning

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Reinforcement learning

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Regression

Question 14

Which TWO techniques are commonly used to handle missing data in a dataset?

Accepted Answer

Remove rows with missing values. Removing rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that dropping a few rows does not significantly reduce the sample size or introduce bias. Option D is correct because imputing missing values with the mean or median is a common statistical method that preserves the dataset size and is simple to implement, though it can reduce variance and may distort relationships if the data is not missing completely at random.

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Feature scaling

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One-hot encoding

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Principal component analysis (PCA)

Question 15

Which THREE factors are common causes of bias in AI systems?

Accepted Answer

Lack of diversity in the development team. A lack of diversity in the development team leads to homogeneity of thought, which can cause blind spots in identifying potential biases in data, features, or model behavior. When the team does not represent the full spectrum of end users, the AI system may inadvertently encode assumptions that disadvantage underrepresented groups, resulting in biased outcomes.

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Cross-validation

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High regularization