AI0-001 Machine Learning and Deep Learning — 50 Questions

Question 1

A team is reviewing a neural network model summary. The input layer expects 784 features (e.g., 28x28 images). How many parameters does the first dense layer have?

Accepted Answer

100,352. The first dense layer has 128 neurons. The parameter count is computed as input_dim * units = 784 * 128 = 100,352. This model summary excludes bias parameters, so the weight count alone is 100,352.

Answer

100,224

Answer

109,258

Answer

8,256

Question 2

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.

Answer

Use a linear regression model

Answer

Undersample the majority class

Answer

Increase the learning rate

Question 3

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.

Answer

Hyperparameter tuning

Answer

Batch normalization

Answer

Dropout

Question 4

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.

Answer

Increase the maximum features

Answer

Decrease the minimum samples split

Answer

Increase the number of trees

Question 5

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.

Answer

Use a smaller learning rate

Answer

Increase the number of convolutional layers

Question 6

A data scientist is evaluating a trained binary classification model. The model has high accuracy but the precision is low and recall is high. Which three actions are most appropriate to improve precision? (Choose three.)

Accepted Answer

Collect more training data for the minority class. Collecting more training data for the minority class (Option A) helps improve precision because it provides the model with more representative examples of the positive class, reducing the likelihood of false positives. In binary classification, low precision indicates many false positives, often due to class imbalance where the minority class is underrepresented. By enriching the minority class, the model can learn more distinct decision boundaries, thereby reducing false positive predictions.

Answer

Apply oversampling to the majority class

Answer

Decrease the classification threshold

Question 7

Refer to the exhibit. A data scientist is training a neural network and observes the training log above. What is the most likely cause?

Accepted Answer

The learning rate is too high. The training log shows a loss that initially decreases but then suddenly spikes and oscillates wildly, which is a classic sign of divergence caused by a learning rate that is too high. A high learning rate causes the optimizer to overshoot the minima in the loss landscape, leading to instability and failure to converge.

Answer

The model is overfitting

Answer

The model is underfitting

Answer

The batch size is too large

Question 8

Refer to the exhibit. An AI developer implements the above neural network architecture for handwritten digit recognition. The model achieves 85% training accuracy and 83% test accuracy. Which modification is most likely to improve training accuracy?

Accepted Answer

Remove the dropout layer. The model achieves 85% training accuracy and 83% test accuracy, indicating slight overfitting (training accuracy is higher than test accuracy). Removing the dropout layer reduces regularization, allowing the model to fit the training data more closely, which directly improves training accuracy. Dropout randomly drops neurons during training to prevent overfitting, but if the model is already underfitting or the gap is small, removing it can boost training performance.

Answer

Increase the dropout rate to 0.7

Answer

Increase the number of filters in the first Conv2D layer

Answer

Add another dense layer before the output

Question 9

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.

Answer

Precision

Answer

F1-score

Answer

Specificity

Question 10

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.

Answer

Normality

Answer

Linearity

Answer

Independence

Question 11

A healthcare organization wants to use patient data to predict disease risk. They are concerned about bias in the model. Which step is most critical during the data preparation phase to mitigate bias?

Accepted Answer

Ensuring the training data is representative of the target population. Ensuring the training data is representative of the target population is the most critical step during data preparation to mitigate bias because bias often originates from skewed or incomplete data that does not reflect the real-world distribution of patient demographics, conditions, and outcomes. Without a representative dataset, any subsequent preprocessing or modeling will propagate and potentially amplify existing disparities, leading to unfair or inaccurate predictions for underrepresented groups.

Answer

Applying SMOTE to oversample minority classes

Answer

Using a more complex algorithm

Answer

Removing all demographic features

Question 12

A team trains a random forest model on a dataset with 50 features. The model's performance on the test set is significantly worse than on the training set. Which technique is most appropriate to address this issue?

Accepted Answer

Apply cross-validation to tune hyperparameters and reduce overfitting. The model's significantly worse test performance compared to training performance is a classic symptom of overfitting. Cross-validation is the most appropriate technique to address this because it evaluates the model's performance on multiple subsets of the data, providing a more robust estimate of generalization. Tuning hyperparameters (e.g., max_depth, min_samples_split) using cross-validation directly reduces overfitting by constraining model complexity, which is the core issue here.

Answer

Increase the number of trees in the forest

Answer

Use feature scaling

Answer

Perform PCA to reduce dimensions

Question 13

A deep learning model for natural language processing uses a recurrent neural network (RNN) to process long sequences. The gradients vanish after many time steps. Which architectural change is most effective to mitigate this problem?

Accepted Answer

Replace the RNN cells with Long Short-Term Memory (LSTM) units. LSTMs are specifically designed with a gating mechanism (input, forget, and output gates) and a cell state that allows gradients to flow unchanged over many time steps, directly addressing the vanishing gradient problem in standard RNNs. This architectural change preserves long-range dependencies in sequences, which is critical for tasks like language modeling or machine translation.

Answer

Add dropout regularization

Answer

Use a larger learning rate

Answer

Increase the number of hidden layers

Question 14

An organization has a dataset with categorical features having high cardinality (e.g., ZIP codes). They plan to use a tree-based model. Which encoding method is most appropriate?

Accepted Answer

Target encoding (mean encoding). Target encoding (mean encoding) replaces each category with the mean of the target variable for that category, which works well with tree-based models on high-cardinality features because it captures the predictive signal without exploding the feature space. This method avoids the dimensionality explosion of one-hot encoding and the arbitrary ordering of label encoding, making it the most appropriate choice for high-cardinality categorical features in tree-based models.

Answer

Label encoding

Answer

One-hot encoding

Answer

Frequency encoding

Question 15

A company deploys a machine learning model that makes predictions on streaming data. Over time, the data distribution shifts, causing model performance to degrade. Which monitoring strategy is most appropriate to detect this drift?

Accepted Answer

Track the model's accuracy on a fixed validation set over time. Tracking the model's accuracy on a fixed validation set over time directly measures performance degradation caused by data drift. As the streaming data distribution shifts, the model's predictions on the static validation set will become less accurate, providing a clear signal that drift has occurred. This is a standard monitoring approach in production ML systems for detecting concept drift.

Answer

Compare the distribution of predictions to the training set

Answer

Monitor the model's training loss

Answer

Retrain the model daily on new data

Question 16

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.

Answer

Data leakage

Answer

Underfitting

Answer

Vanishing gradient

Question 17

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.

Answer

Remove dropout

Answer

Increase the dropout rate

Answer

Increase the learning rate

Question 18

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.

Answer

The learning rate is too high

Answer

The image is missing CUDA libraries

Answer

The number of epochs is too high

Question 19

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.

Answer

Linear regression

Answer

K-means clustering

Answer

Principal component analysis

Question 20

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.

Answer

Vanishing gradient; use ReLU activation

Answer

Underfitting; increase model complexity

Answer

Data leakage; reshuffle split