AI0-001 Machine Learning and Deep Learning Practice Test 2 — 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?

Accepted Answer

Split the data into training and testing sets before any other preprocessing. Option C is correct because 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.

Answer

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

Which TWO techniques are commonly used to prevent overfitting in deep neural networks?

Accepted Answer

Dropout. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and forces it to learn more robust features. This reduces overfitting by introducing noise and effectively training an ensemble of sub-networks.

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Using a larger learning rate

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L1 regularization

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Increasing the number of layers

Question 3

Refer to the exhibit. A data scientist is training a binary classifier. Based on the training log, which problem is the model experiencing?

Accepted Answer

Overfitting. The training log shows that the model's training accuracy continues to improve while the validation accuracy plateaus or degrades after a certain number of epochs. This divergence between training and validation performance is the hallmark of overfitting, where the model memorizes the training data noise rather than learning generalizable patterns.

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Underfitting

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Data leakage

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Vanishing gradient

Question 4

A healthcare startup is developing a deep learning model to detect diabetic retinopathy from retinal fundus images. The dataset contains 50,000 images, but only 5% are labeled as positive for the disease. The team uses a convolutional neural network (CNN) with a final sigmoid layer and binary cross-entropy loss. After training for 20 epochs, the model achieves 95% accuracy on the test set, but the recall for the positive class is only 10%. The team suspects the model is biased toward the negative class due to class imbalance. The data is stored in a secure environment, and no additional labeled data can be obtained. The team has access to the following techniques: oversampling the minority class, undersampling the majority class, using class weights in the loss function, applying data augmentation, and using a different architecture. Which course of action is most likely to improve recall for the positive class while maintaining reasonable overall performance?

Accepted Answer

Assign higher class weights to the positive class in the loss function. Assigning higher class weights to the positive class in the loss function directly penalizes misclassifications of the minority class during training. This forces the model to pay more attention to positive samples without altering the dataset distribution, which is critical when no additional labeled data can be obtained and the data is in a secure environment. It improves recall by increasing the gradient contribution from positive samples, while maintaining overall performance because the model still sees the original data distribution.

Answer

Undersample the majority class to balance the dataset

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Oversample the minority class using synthetic image generation

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Replace the CNN with a transformer-based architecture

Question 5

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. Option D is correct because 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.

Answer

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 6

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., SMOTE) creates synthetic samples to balance the dataset, which is a common and effective approach for imbalanced classification.

Answer

Use a linear regression model

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Undersample the majority class

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Increase the learning rate

Question 7

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. Early stopping halts training when validation performance degrades, preventing overfitting.

Answer

Increase the number of hidden layers

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Add more training data

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Increase the batch size

Question 8

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 model weights (e.g., from 32-bit to 8-bit), significantly decreasing model size and speeding up inference with minimal accuracy loss.

Answer

Hyperparameter tuning

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Batch normalization

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Dropout

Question 9

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 a simple and interpretable model for predicting a continuous target variable like housing price.

Answer

K-means clustering

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Decision tree regression

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Association rule mining

Question 10

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. Data augmentation artificially increases the size and diversity of the training set by applying transformations, reducing overfitting.

Answer

Increasing network depth

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Using a larger batch size

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Reducing the number of filters

Question 11

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. L2 regularization adds a penalty on large weights, reducing overfitting and improving test accuracy.

Answer

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 12

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 algorithm that partitions data into K clusters based on similarity.

Answer

Random forest classifier

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Linear regression

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Support vector machine

Question 13

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.

Answer

Increase the number of hidden layers

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Decrease the batch size

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Use a test set

Question 14

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 reduces model complexity, preventing overfitting.

Answer

Increase the maximum features

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Decrease the minimum samples split

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Increase the number of trees

Question 15

A company is preparing a dataset for training a supervised machine learning model. The dataset contains missing values, outliers, and categorical features. Which two preprocessing steps are typically performed to prepare the data? (Choose two.)

Accepted Answer

Impute missing values with the mean. Imputing missing values and encoding categorical features are standard preprocessing steps for most machine learning pipelines.

Answer

Normalize numerical features to a standard range

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Remove all features with low variance

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Increase the number of features using PCA