AI0-001 Machine Learning and Deep Learning Practice Test 6 — 15 Questions

Question 1

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?

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

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 2

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 3

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 4

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 5

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.

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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 6

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 7

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 8

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 with the mean is a standard technique to handle incomplete data, ensuring the model can process all records without discarding potentially valuable information. Option C is correct because one-hot encoding converts categorical features into a binary vector representation, which is required by most machine learning algorithms that expect numerical input.

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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

Question 9

Refer to the exhibit. An AI specialist reviews the model evaluation report for a binary classifier. The specialist wants to improve recall. Which action is most likely effective?

Accepted Answer

Collect more training data for the minority class. Collecting more training data for the minority class directly addresses class imbalance, which is a common cause of low recall. By providing more examples of the positive (minority) class, the model can learn better decision boundaries and reduce false negatives, thereby improving recall without altering the classification threshold.

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Decrease the classification threshold

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Increase the classification threshold

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Add more features

Question 10

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 11

An AI engineer is training a deep neural network for image recognition. The training loss decreases steadily for the first few epochs but then plateaus and starts to oscillate. Which adjustment is most likely to improve convergence?

Accepted Answer

Reduce the learning rate. The plateau and oscillation of the training loss indicate that the optimizer is overshooting the minimum due to a learning rate that is too high. Reducing the learning rate allows the optimizer to take smaller, more precise steps, dampening oscillations and enabling convergence to a lower loss. This is a standard technique in gradient descent optimization, often implemented via learning rate schedules or adaptive methods like Adam.

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Add more layers

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Increase the learning rate

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Increase the batch size

Question 12

A data scientist is tuning hyperparameters for a support vector machine (SVM) with an RBF kernel. Which two hyperparameters most significantly affect model performance? (Select TWO.)

Accepted Answer

gamma (kernel coefficient). Gamma defines the influence of a single training example, with low values meaning a far reach and high values meaning a close reach. C controls the trade-off between achieving a low error on the training data and minimizing the margin, directly impacting overfitting. Together, they are the two most critical hyperparameters for an SVM with an RBF kernel.

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learning rate

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epsilon (for epsilon-SVR)

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degree (for polynomial kernel)

Question 13

A team is designing a deep learning pipeline for a computer vision task. They want to reduce overfitting. Which two techniques are specifically effective for this purpose? (Select TWO.)

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 that improves generalization.

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Using a smaller batch size

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Adding more layers

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Increasing the learning rate

Question 14

A data scientist is using an ensemble method to combine multiple models. Which three statements about bagging (Bootstrap Aggregating) are true? (Select THREE.)

Accepted Answer

It reduces variance without increasing bias. Bagging reduces variance by training models on different bootstrap samples of the data and averaging their predictions. Since each model is trained independently on a random sample with replacement, the ensemble's variance decreases without introducing additional bias, as the expected prediction remains unbiased. This is a key property that distinguishes bagging from boosting, which reduces both bias and variance.

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It requires the base models to be of different types

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It reduces the error by combining weak learners

Question 15

Which TWO are characteristics of supervised learning?

Accepted Answer

Requires labeled data. Supervised learning requires labeled data because the model learns a mapping from input features to a known target variable. The correct answer B is fundamental: without labeled examples, the algorithm cannot calculate a loss function to adjust its weights during training.

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Does not require target variable

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Uses reinforcement signals

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Learns to cluster data