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

Question 1

A machine learning engineer is building a spam filter. The dataset contains 10,000 emails, of which 1,000 are spam. The engineer decides to use a Random Forest classifier. Which preprocessing step is most critical to ensure the model generalizes well to new, unseen emails?

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

Accepted Answer

Split the data into training and testing sets before any other preprocessing. Splitting the data into training and testing sets before any other preprocessing prevents data leakage. If preprocessing like normalization or PCA is applied to the entire dataset first, the test set information influences the training process, leading to overly optimistic performance estimates and poor generalization to new, unseen emails.

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Apply Principal Component Analysis (PCA) to reduce dimensionality

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Normalize the numerical features to have zero mean and unit variance

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Encode all features using one-hot encoding

Question 2

A retail company uses a gradient boosting model to predict customer lifetime value (CLV). The model currently uses 50 features including purchase history, demographics, and web behavior. The model's RMSE on the test set is 120. The data science team wants to improve the model's accuracy without increasing training time significantly. They have access to additional data: customer support interaction logs (text), social media sentiment (text), and third-party credit scores (numeric). They also have the ability to perform feature engineering, hyperparameter tuning, and ensemble methods. Which approach is most likely to yield the best improvement in predictive performance with minimal increase in training time?

Accepted Answer

Engineer new features such as average purchase value and recency. Engineering domain-relevant features like average purchase value and recency directly captures the underlying behavioral patterns that drive customer lifetime value, often providing a higher signal-to-noise ratio than adding raw text or third-party data. This approach leverages existing data without significantly increasing the feature dimensionality or training time, unlike adding TF-IDF vectors which would dramatically expand the feature space and slow training.

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Add the customer support text as a feature using TF-IDF vectors

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Use an ensemble of gradient boosting and random forest models

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Perform hyperparameter tuning using grid search

Question 3

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 1% fraud cases. Which technique is most appropriate to address the class imbalance?

Accepted Answer

Oversample the minority class. Oversampling the minority class (e.g., using SMOTE or random oversampling) is the most appropriate technique because it balances the dataset by generating synthetic or duplicate examples of the fraud cases, allowing the model to learn the decision boundary for the minority class without discarding valuable majority-class data. This directly addresses the class imbalance where only 1% of transactions are fraudulent, improving recall and precision for fraud detection.

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Use a linear regression model

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Undersample the majority class

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Increase the learning rate

Question 4

A machine learning engineer is tuning a neural network for image classification. The training loss decreases steadily, but the validation loss starts increasing after 50 epochs. Which action best addresses this issue?

Accepted Answer

Apply early stopping with a patience of 10 epochs. The described behavior—decreasing training loss with increasing validation loss—is a classic sign of overfitting. Early stopping with a patience of 10 epochs directly addresses this by halting training when the validation loss fails to improve for a specified number of epochs, preventing further overfitting while retaining the best model weights.

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Increase the number of hidden layers

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Add more training data

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Increase the batch size

Question 5

A company deploys a deep learning model for real-time object detection in autonomous vehicles. The model was trained on high-end GPUs but needs to run on edge devices with limited computational resources. Which technique is most effective for reducing model size and inference latency while maintaining acceptable accuracy?

Accepted Answer

Quantization. Quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integers), which significantly decreases model size and speeds up inference on edge devices with limited computational resources. This technique directly addresses the constraints of edge deployment while often maintaining acceptable accuracy through careful calibration.

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Hyperparameter tuning

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Batch normalization

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Dropout

Question 6

A data analyst wants to predict housing prices based on square footage, number of bedrooms, and location. Which machine learning approach is most suitable?

Accepted Answer

Linear regression. Linear regression is the most suitable approach because the problem involves predicting a continuous numeric target (housing prices) from multiple independent variables (square footage, bedrooms, location). Linear regression models the linear relationship between the features and the target, providing interpretable coefficients and efficient training for this type of regression task.

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K-means clustering

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Decision tree regression

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Association rule mining

Question 7

A team is training a convolutional neural network (CNN) for medical image diagnosis. They have a limited dataset of 500 labeled images. Which strategy is most effective to improve model generalization?

Accepted Answer

Data augmentation. With only 500 labeled medical images, the primary challenge is overfitting due to limited data. Data augmentation (e.g., random rotations, flips, zooms) artificially expands the training set by creating varied but realistic transformations, which forces the CNN to learn invariant features and significantly improves generalization to unseen data.

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Increasing network depth

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Using a larger batch size

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Reducing the number of filters

Question 8

An AI developer observes that the training accuracy of a neural network is high, but the test accuracy is low. The model uses a ReLU activation function and Adam optimizer. Which approach is most likely to improve test accuracy?

Accepted Answer

Add L2 regularization to the loss function. The scenario describes overfitting: high training accuracy but low test accuracy. Adding L2 regularization (weight decay) penalizes large weights, which reduces model complexity and improves generalization to unseen data. This directly addresses the overfitting problem without altering the model architecture or optimization algorithm.

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Increase the learning rate

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Switch to a stochastic gradient descent optimizer

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Increase the number of epochs

Question 9

A machine learning engineer needs to choose an algorithm for grouping customers into segments based on purchasing behavior without any labels. Which algorithm should the engineer use?

Accepted Answer

K-means clustering. K-means clustering is an unsupervised learning algorithm that groups unlabeled data into clusters based on feature similarity, making it ideal for segmenting customers by purchasing behavior without predefined labels. It partitions data into K clusters by minimizing within-cluster variance, which directly addresses the requirement of discovering natural groupings in the data.

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Random forest classifier

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Linear regression

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Support vector machine

Question 10

While training a deep neural network, the loss function fails to converge and oscillates wildly. Which adjustment is most likely to stabilize training?

Accepted Answer

Reduce the learning rate. When the loss function oscillates wildly and fails to converge, it typically indicates that the learning rate is too high, causing the optimizer to overshoot the minima. Reducing the learning rate allows the gradient descent updates to take smaller, more stable steps, which helps the loss converge smoothly. This is a fundamental hyperparameter tuning step in deep learning training.

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Increase the number of hidden layers

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Decrease the batch size

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Use a test set

Question 11

A data scientist is training a random forest model on a large dataset and notices that the model is overfitting. Which hyperparameter adjustment is most likely to reduce overfitting?

Accepted Answer

Decrease the maximum depth of trees. Decreasing the maximum depth of trees limits how deep each decision tree can grow, which reduces the model's capacity to learn overly specific patterns from the training data. This directly combats overfitting by enforcing simpler trees that generalize better to unseen data.

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Increase the maximum features

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Decrease the minimum samples split

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Increase the number of trees

Question 12

A deep learning engineer is training a convolutional neural network for image classification. The model is overfitting the training data. Which three techniques can help reduce overfitting? (Choose three.)

Accepted Answer

Add dropout layers. Dropout layers randomly deactivate a fraction of neurons during training, which prevents co-adaptation and forces the network to learn more robust features. This reduces overfitting by acting as a form of ensemble learning without increasing model complexity.

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Use a smaller learning rate

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Increase the number of convolutional layers

Question 13

A data scientist is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 80% non-churn and 20% churn. The model achieves 95% accuracy but fails to identify churners correctly. Which metric should the scientist focus on to evaluate model performance properly?

Accepted Answer

Recall (TPR). Recall (True Positive Rate) measures the proportion of actual churners correctly identified by the model. With 80% non-churn and 20% churn, a model can achieve 95% accuracy by simply predicting the majority class (non-churn) for all samples, resulting in zero true positives for churn. Recall directly exposes this failure by quantifying how many churners are captured, making it the critical metric for imbalanced classification problems.

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Precision

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F1-score

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Specificity

Question 14

A team is implementing a machine learning pipeline to classify images for a defect detection system. They are considering using a pre-trained convolutional neural network (CNN) and fine-tuning it on their small dataset. What is the primary advantage of transfer learning in this scenario?

Accepted Answer

It allows the model to leverage learned features from a large dataset, reducing training time and required data. Transfer learning allows the team to start with a pre-trained CNN (e.g., trained on ImageNet) that has already learned general features like edges, textures, and shapes from a massive dataset. By fine-tuning only the later layers on their small defect dataset, they dramatically reduce training time and the amount of labeled data needed, while still achieving high accuracy.

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It ensures the model is not biased toward the original dataset

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It eliminates the need for data preprocessing

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It reduces the risk of overfitting by using a larger model

Question 15

A company uses linear regression to predict sales based on advertising spend. The model's residuals show a pattern of increasing variance as spend increases. Which assumption of linear regression is violated?

Accepted Answer

Homoscedasticity. The pattern of increasing residual variance with higher advertising spend violates the assumption of homoscedasticity, which requires constant variance of errors across all levels of the independent variable. In linear regression, heteroscedasticity like this can lead to inefficient coefficient estimates and unreliable confidence intervals, often detected via a Breusch-Pagan test or residual plot analysis.

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Normality

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Linearity

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Independence