AI0-001 Machine Learning and Deep Learning Practice Test 2 — 15 Questions

Question 1

Refer to the exhibit. The training log shows losses and accuracies over 5 epochs. What is the most likely problem?

Accepted Answer

Overfitting. The training log shows high training accuracy (e.g., 99%) but low validation accuracy (e.g., 60%) across epochs, with the validation loss increasing after an initial drop. This divergence indicates the model has memorized the training data rather than learning generalizable patterns, which is the hallmark of overfitting.

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Data leakage

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Underfitting

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Vanishing gradient

Question 2

Refer to the exhibit. A developer is using the above configuration for a multi-class classification task. The model performs well on training data but poorly on validation data. Which modification could help?

Accepted Answer

Add L2 regularization to the dense layers. The model is overfitting, as indicated by good performance on training data but poor on validation data. Adding L2 regularization to the dense layers penalizes large weights, reducing model complexity and improving generalization. This directly addresses overfitting without disrupting the training dynamics.

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Remove dropout

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Increase the dropout rate

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Increase the learning rate

Question 3

Refer to the exhibit. The training pod is using 2 GPUs. During training, the GPU utilization is only 30% each. What is the most likely cause?

Accepted Answer

The batch size is too small to fully utilize GPUs. With a batch size that is too small, each GPU receives insufficient data per training step to saturate its compute cores. This leads to low utilization (e.g., 30%) because the GPUs spend most of their time idling while waiting for the next batch to be loaded and processed. Increasing the batch size allows each GPU to process more data in parallel, improving throughput and utilization.

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The learning rate is too high

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The image is missing CUDA libraries

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The number of epochs is too high

Question 4

A data scientist needs to predict whether a customer will churn based on historical data containing features like account age, monthly charges, and support tickets. The target variable is binary (churn or not). Which type of machine learning algorithm should be used?

Accepted Answer

Logistic regression. Logistic regression is the correct choice because it is specifically designed for binary classification tasks, such as predicting whether a customer will churn (yes/no). It models the probability of the binary outcome using a logistic (sigmoid) function, making it suitable for this supervised learning problem with a categorical target variable.

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Linear regression

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K-means clustering

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Principal component analysis

Question 5

A team trained a deep neural network on a limited dataset. The training loss decreases consistently, but the validation loss starts increasing after 20 epochs. What is the most likely issue and the best corrective action?

Accepted Answer

Overfitting; apply regularization like dropout. The training loss decreasing while validation loss increasing after 20 epochs is the classic signature of overfitting: the model has memorized the training data but fails to generalize to unseen data. Applying regularization like dropout forces the network to learn more robust features by randomly dropping neurons during training, reducing overfitting. This is the most direct and effective corrective action for this specific symptom.

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Vanishing gradient; use ReLU activation

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Underfitting; increase model complexity

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Data leakage; reshuffle split

Question 6

A company is building a computer vision system to detect defects in manufactured parts. They have 10,000 labeled images per class (defective and non-defective). They want to achieve high accuracy with limited computational resources. Which deep learning architecture and approach is most appropriate?

Accepted Answer

Use a pre-trained VGG16 and fine-tune the last few layers. Using a pre-trained VGG16 and fine-tuning the last few layers leverages transfer learning, which is ideal when you have a moderate-sized labeled dataset (10,000 images per class) and limited computational resources. The pre-trained model already captures general visual features from ImageNet, so only the task-specific layers need to be trained, reducing training time and resource requirements while still achieving high accuracy.

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Train a custom CNN from scratch with many layers

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Use a decision tree ensemble

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Use an RNN to process image sequences

Question 7

A machine learning engineer has a dataset of 100,000 records. She splits it into 70% training, 15% validation, and 15% test sets. After training, the model achieves 95% accuracy on training and 85% on validation. What does the accuracy difference most likely indicate?

Accepted Answer

The model is overfitting. The 10% gap between training accuracy (95%) and validation accuracy (85%) is a classic sign of overfitting. The model has memorized patterns specific to the training set rather than learning generalizable features, causing it to perform worse on unseen validation data. In machine learning, a significant drop in performance from training to validation indicates poor generalization, which is the hallmark of overfitting.

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The validation set is too small

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The model generalizes well

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The test set should be larger

Question 8

A deep learning model for sentiment analysis uses a softmax output layer. The hidden layers currently use tanh activation. Which activation function should replace tanh to mitigate vanishing gradients in deeper networks?

Accepted Answer

ReLU. ReLU (Rectified Linear Unit) is correct because it outputs zero for negative inputs and a positive linear slope for positive inputs, which avoids the saturation problem of tanh. In deeper networks, tanh gradients can vanish as activations approach ±1, slowing or halting learning. ReLU's non-saturating nature keeps gradients flowing for positive inputs, mitigating the vanishing gradient problem.

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Sigmoid

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Softmax

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Linear

Question 9

A fraud detection model is trained on a dataset where only 0.1% of transactions are fraudulent. The model achieves 99.9% accuracy but fails to catch most frauds. Which metric should the team prioritize, and which technique could help?

Accepted Answer

Precision-Recall AUC; use oversampling like SMOTE. The dataset is highly imbalanced (0.1% fraud), so 99.9% accuracy is misleading because a model that predicts 'not fraud' for every transaction achieves it. Precision-Recall AUC focuses on the positive class (fraud) and is robust to class imbalance, unlike accuracy or ROC-AUC. Oversampling like SMOTE generates synthetic fraud samples to balance the dataset, helping the model learn the minority class patterns.

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Mean Squared Error; use L2 regularization

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F1 score; use principal component analysis

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Accuracy; collect more data

Question 10

A dataset contains features on vastly different scales (e.g., age 0-100 vs. income 0-1,000,000). Which preprocessing step is essential before training a neural network?

Accepted Answer

Feature scaling (standardization or normalization). Neural networks rely on gradient-based optimization, where features with larger scales can dominate the weight updates, causing unstable convergence or slow training. Feature scaling (standardization or normalization) ensures all features contribute equally to the loss function, preventing the model from being biased toward high-magnitude features like income versus age.

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Data augmentation

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Dimensionality reduction

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One-hot encoding

Question 11

During training of a neural network, the loss oscillates and does not converge smoothly. The learning rate is set to 0.1. What is the most likely cause and what adjustment should be made?

Accepted Answer

Learning rate too high; decrease it. A learning rate of 0.1 is relatively high for many neural network architectures. When the learning rate is too high, the optimizer takes steps that overshoot the minimum of the loss function, causing the loss to oscillate or even diverge instead of converging smoothly. Decreasing the learning rate allows for smaller, more stable weight updates, leading to smoother convergence.

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Learning rate too low; increase it

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Batch size too small; increase it

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Too many epochs; stop early

Question 12

A team is building a model to predict stock prices based on time series data. They need to capture long-term dependencies and avoid vanishing gradients. Which architecture is best suited?

Accepted Answer

LSTM. LSTM (Long Short-Term Memory) networks are specifically designed to capture long-term dependencies in sequential data through their gating mechanisms (input, forget, and output gates), which regulate the flow of information and mitigate the vanishing gradient problem that plagues standard RNNs. This makes them ideal for time series forecasting tasks like stock price prediction, where historical context over many time steps is critical.

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Standard RNN

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Autoencoder

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CNN

Question 13

Which THREE techniques can help reduce overfitting in neural networks?

Accepted Answer

Increasing training data size. Increasing the training data size helps reduce overfitting by providing the model with more examples to learn from, which reduces the variance and improves generalization. With more data, the model is less likely to memorize noise and instead learns the underlying patterns, making it more robust on unseen data.

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Using a larger learning rate

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Increasing number of layers

Question 14

Which TWO are key differences between Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)?

Accepted Answer

RNNs have internal memory; CNNs do not. RNNs possess a hidden state that acts as internal memory, allowing them to retain information from previous time steps, which is essential for processing sequential data. In contrast, CNNs lack this internal memory mechanism; they process inputs independently without maintaining a state across different inputs, making them unsuitable for tasks requiring temporal context.

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CNNs are designed for sequential data; RNNs for spatial data

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CNNs can handle variable-length inputs; RNNs require fixed-size inputs

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CNNs use backpropagation; RNNs do not

Question 15

Refer to the exhibit. What is the most likely issue and what action should be taken?

Accepted Answer

Overfitting; apply early stopping around epoch 15. The training loss continues to decrease while the validation loss starts to increase after approximately epoch 15, which is a classic sign of overfitting. The model is memorizing the training data rather than generalizing, so applying early stopping around epoch 15 would prevent further divergence and preserve the best validation performance.

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Learning rate is too low; increase it

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Underfitting; increase model complexity

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Data imbalance; use class weights