AI0-001 Machine Learning and Deep Learning Practice Test 4 — 15 Questions

Question 1

A team is developing a recommendation system for an e-commerce platform. They want to use collaborative filtering but are concerned about cold-start problems for new users. Which approach would best mitigate the cold-start problem?

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

Accepted Answer

Incorporate user demographic features as side information. Incorporating user demographic features as side information allows the collaborative filtering model to generate initial recommendations for new users based on their demographic profile, effectively addressing the cold-start problem. This approach uses content-based features to bootstrap the recommendation process until sufficient user interaction data is collected.

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Increase the number of latent factors in matrix factorization

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Use a popularity-based baseline for all recommendations

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Use only item-based collaborative filtering

Question 2

A data scientist is training a deep neural network for sentiment analysis. The training loss decreases steadily but the validation loss starts to increase after 10 epochs. What is the most likely cause and best corrective action?

Accepted Answer

Overfitting; apply dropout and early stopping. The scenario describes a classic case of overfitting: the training loss decreases steadily, indicating the model is learning the training data well, but the validation loss increases after 10 epochs, meaning the model is memorizing noise and patterns specific to the training set rather than generalizing. The best corrective action is to apply dropout (which randomly drops neurons during training to reduce co-adaptation) and early stopping (which halts training when validation performance degrades), both of which are standard regularization techniques for deep neural networks.

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Underfitting; increase model complexity

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Vanishing gradients; use ReLU activation

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Data leakage; shuffle data before splitting

Question 3

An organization wants to automate the detection of defective products on an assembly line using computer vision. They have a limited number of labeled images for defective items. Which approach would be most effective?

Accepted Answer

Use transfer learning with a pre-trained model like ResNet and fine-tune on the defect data. Transfer learning with a pre-trained model like ResNet is most effective because it leverages features learned from large datasets (e.g., ImageNet) and adapts them to the defect detection task with limited labeled data. Fine-tuning only the later layers preserves general visual features while specializing for defect classification, avoiding overfitting that would occur with a small dataset.

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Use a support vector machine with handcrafted features

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Train a convolutional neural network from scratch on the limited data

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Synthesize additional defective images using GANs

Question 4

A machine learning engineer is troubleshooting a recurrent neural network that fails to learn long-range dependencies in sequential data. The gradients are computed using backpropagation through time. Which phenomenon is most likely occurring, and what architectural change would best address it?

Accepted Answer

Vanishing gradients; use LSTM or GRU units. In standard RNNs, backpropagation through time (BPTT) multiplies gradients across many time steps, causing them to shrink exponentially (vanishing gradients). This prevents the network from learning long-range dependencies. LSTM or GRU units introduce gating mechanisms that preserve gradient flow over many time steps, directly solving this problem.

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Underfitting; increase the number of time steps

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Exploding gradients; apply gradient clipping

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Overfitting; reduce the number of layers

Question 5

A data scientist is using a gradient boosting model (XGBoost) for a regression task and observes that the model's performance on the training set is much better than on the test set. Which hyperparameter tuning strategy would most effectively reduce overfitting?

Accepted Answer

Reduce the maximum depth of trees. Reducing the maximum depth of trees limits the complexity of individual trees, preventing them from learning overly specific patterns in the training data. In XGBoost, deeper trees can capture noise and lead to high variance, so lowering depth directly reduces overfitting by enforcing simpler models.

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Increase the number of boosting rounds

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Increase the learning rate

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Subsample less than 1.0

Question 6

A deep learning model for autonomous vehicle perception uses a large convolutional neural network. During deployment, the model misclassifies a stop sign that has a small sticker on it. This is likely an example of what type of vulnerability, and which defense is most appropriate?

Accepted Answer

Adversarial attack; implement adversarial training. The misclassification of a stop sign with a small sticker is a classic example of an adversarial attack, where imperceptible perturbations (like a sticker) cause the model to output an incorrect prediction. Adversarial training, which augments the training data with adversarial examples, is the most appropriate defense because it improves the model's robustness against such perturbations by explicitly learning to resist them.

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Model inversion; add differential privacy

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Data poisoning; use robust aggregation

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Transfer learning; use domain adaptation

Question 7

Which THREE of the following are techniques for handling missing data in machine learning?

Accepted Answer

Deletion of rows with missing values. Deleting rows with missing values is a straightforward technique for handling missing data, often used when the missingness is random and the dataset is large enough that removing a few rows does not significantly impact model performance. This method avoids introducing bias from imputation but can lead to loss of valuable information if too many rows are removed.

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Autoencoder reconstruction

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Principal Component Analysis

Question 8

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset contains 99.9% legitimate transactions and 0.1% fraudulent transactions. After training a logistic regression model, the accuracy is 99.9%, but the recall for the fraud class is 0%. Which of the following is the MOST likely cause?

Accepted Answer

The dataset is highly imbalanced, and the model predicts the majority class for all instances.. The dataset has a severe class imbalance (99.9% legitimate, 0.1% fraudulent). A logistic regression model that predicts the majority class (legitimate) for every instance will achieve 99.9% accuracy but 0% recall for the fraud class, because it never identifies any positive fraud cases. This is the classic 'accuracy paradox' in imbalanced classification.

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The regularization parameter is too large, causing underfitting.

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The model is overfitting due to too many features.

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The learning rate was too high.

Question 9

A machine learning engineer is preparing to train a deep neural network for image classification. To avoid overfitting, which TWO techniques should the engineer apply? (Select TWO.)

Accepted Answer

Use dropout regularization.. Dropout regularization is a technique that randomly drops a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and reduces co-adaptation. This acts as a form of ensemble learning and significantly reduces overfitting by improving generalization.

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Increase the number of layers.

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Remove all non-linear activation functions.

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Reduce the training dataset size.

Question 10

A company is deploying a machine learning model that predicts customer churn. The model currently has high variance. Which THREE actions should the data scientist take to reduce variance? (Select THREE.)

Accepted Answer

Reduce model complexity (e.g., fewer features, simpler model).. Reducing model complexity (e.g., using fewer features or a simpler algorithm like logistic regression instead of a deep neural network) directly addresses high variance by limiting the model's capacity to overfit to noise in the training data. A simpler model has less flexibility to capture spurious patterns, which reduces the gap between training and validation error.

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Remove outliers from the training data.

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Increase model complexity.

Question 11

A financial institution uses a random forest model to approve loan applications. Recently, the model's false positive rate has increased, leading to more defaults. The data science team reviews the feature importance and finds that the model heavily relies on a feature 'zip code' which correlates with income. The company is concerned about fairness. The regulatory team requires that the model's predictions are not biased against protected groups. Which action BEST addresses the fairness concern while maintaining predictive performance? A. Remove the 'zip code' feature and retrain the model. B. Use adversarial debiasing to train a model that is invariant to protected attributes. C. Add more training data from underrepresented zip codes. D. Apply a post-processing technique that adjusts thresholds for different groups.

Accepted Answer

Use adversarial debiasing to train a model that is invariant to protected attributes.. Adversarial debiasing directly forces the model to learn representations that are not predictive of protected attributes, thereby reducing bias while maintaining performance as much as possible. Option A (post-processing) can adjust thresholds but does not address the underlying model bias; it is a less robust solution. Option B (removing zip code) might lose important information, as zip code could be a proxy for other legitimate factors; also, other features may still correlate with protected attributes. Option C (adding data) does not directly address bias and may not remove the correlation.

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Apply a post-processing technique that adjusts thresholds for different groups.

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Remove the 'zip code' feature and retrain the model.

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Add more training data from underrepresented zip codes.

Question 12

A self-driving car company uses a reinforcement learning agent to navigate. The agent was trained in a simulated environment and achieved high rewards. When deployed in the real world, the agent fails to avoid obstacles. The team collects real-world driving data and uses it to fine-tune the model. However, fine-tuning leads to catastrophic forgetting of the simulated knowledge. Which technique should the team use to mitigate this? A. Increase the learning rate during fine-tuning. B. Use elastic weight consolidation (EWC) to regularize important weights. C. Train the model from scratch using only real-world data. D. Increase the number of layers in the network.

Accepted Answer

Use elastic weight consolidation (EWC) to regularize important weights.. Elastic Weight Consolidation (EWC) is a regularization technique specifically designed to prevent catastrophic forgetting when fine-tuning a neural network on a new task. It identifies the weights that are most important for the original task (simulated driving) and penalizes large changes to those weights during fine-tuning on real-world data, thereby preserving the learned knowledge while adapting to the new domain.

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Increase the number of layers in the network.

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Train the model from scratch using only real-world data.

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Increase the learning rate during fine-tuning.

Question 13

A data scientist is building a classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 1% fraudulent cases. Which approach should the scientist use to evaluate model performance most effectively?

Accepted Answer

F1 score. In highly imbalanced datasets like fraud detection (1% positive class), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy yet fail to detect any fraud. The F1 score (harmonic mean of precision and recall) is the most effective metric because it balances both false positives and false negatives, providing a single score that reflects the model's ability to correctly identify the minority class without being skewed by class imbalance.

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Accuracy

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Recall

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Precision

Question 14

A machine learning team is deploying a model that predicts customer churn. They notice that the model's predictions are highly sensitive to small changes in input features, leading to inconsistent outputs. Which technique should the team apply to improve model stability?

Accepted Answer

Regularization. Regularization (Option C) is the correct technique because it adds a penalty term to the loss function (e.g., L1 or L2 regularization), which constrains the model's weights. This reduces variance and prevents overfitting to noise in the training data, directly addressing the high sensitivity to small input changes (brittleness). By shrinking coefficients, regularization forces the model to learn more general patterns, improving stability and consistency in predictions.

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Increase learning rate

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Feature scaling

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Cross-validation

Question 15

A deep learning model for image classification is overfitting the training data. The team has already tried data augmentation and dropout. Which additional technique should they implement to reduce overfitting?

Accepted Answer

Early stopping. Early stopping (Option D) is the correct additional technique because it halts training when validation performance stops improving, directly preventing the model from memorizing noise in the training data. Since data augmentation and dropout are already in use, early stopping provides a complementary regularization effect by limiting the number of training iterations before overfitting occurs.

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Batch normalization

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Increase number of epochs

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Gradient clipping