AI0-001 Practice Test 23 — 15 Questions

Question 1

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

Answer

Switch to a stochastic gradient descent optimizer

Answer

Increase the number of epochs

Question 2

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, data augmentation, and L2 regularization are standard techniques to reduce overfitting by adding regularization or increasing data diversity.

Answer

Use a smaller learning rate

Answer

Increase the number of convolutional layers

Question 3

A team trains a deep learning model for image classification with 1000 classes. The training loss decreases but validation loss starts increasing after 10 epochs. What should they do first?

Accepted Answer

Add dropout layers. Option C is correct because adding dropout layers can help reduce overfitting by randomly dropping neurons during training. Options A, B, and D are incorrect: reducing learning rate may help but not as directly for overfitting, increasing batch size may improve stability but not necessarily overfitting, and data augmentation helps if the dataset is small but the symptom here is overfitting.

Answer

Use data augmentation

Answer

Increase batch size

Answer

Reduce learning rate

Question 4

Which TWO are evaluation metrics for classification problems? (Choose two.)

Accepted Answer

Precision. Options B and D are correct because Precision and Recall are classification metrics. Options A, C, and E are incorrect: Mean Squared Error and Mean Absolute Error are regression metrics, and R-squared is also for regression.

Answer

Mean Absolute Error

Answer

R-squared

Answer

Mean Squared Error

Question 5

A machine learning engineer notices that a linear regression model has high bias. Which action is most likely to reduce bias?

Accepted Answer

Use a more complex model, such as polynomial regression. Option A (Add L2 regularization) increases bias to reduce variance. Option C (Reduce the number of training samples) would increase bias. Option D (Apply feature scaling) does not directly affect bias. Option B (Use a more complex model, such as polynomial regression) increases model flexibility, reducing bias.

Answer

Reduce the number of training samples

Answer

Add L2 regularization

Answer

Apply feature scaling

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

Answer

Undersample the majority class

Answer

Increase the learning rate

Question 7

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

Answer

Batch normalization

Answer

Dropout

Question 8

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. Increasing the classification threshold reduces false positives, using a different algorithm that penalizes false positives more, and collecting more data for the minority class can all improve precision.

Answer

Apply oversampling to the majority class

Answer

Decrease the classification threshold

Question 9

A deep learning model for sentiment analysis has millions of parameters and is trained on a small dataset. Which technique can help prevent overfitting?

Accepted Answer

Dropout. Option A is correct because dropout is a regularization technique that randomly drops neurons during training, reducing overfitting. Options B, C, and D are incorrect: batch normalization helps with internal covariate shift, learning rate scheduling helps convergence, and early stopping can prevent overfitting but is not as specific as dropout for parameter-heavy models.

Answer

Learning rate scheduling

Answer

Batch normalization

Answer

Early stopping

Question 10

Based on the exhibit, what does this indicate about the model?

Accepted Answer

The model has high precision but low recall, missing many positives. Option B is correct because high precision (0.95) means few false positives, but low recall (0.60) means many false negatives, so the model misses many positive instances. Options A, C, and D are incorrect: overfitting cannot be determined from these metrics alone, underfitting would likely show both low precision and recall, and balanced performance would have similar values.

Answer

The model has balanced performance

Answer

The model is underfitting

Answer

The model is overfitting

Question 11

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset contains 99.9% legitimate transactions and 0.1% fraudulent transactions. After training a logistic regression model, the accuracy is 99.9%, but the recall for the fraud class is 0%. Which of the following is the MOST likely cause?

Accepted Answer

The dataset is highly imbalanced, and the model predicts the majority class for all instances.. Option C is correct because a highly imbalanced dataset often leads the model to predict the majority class for all instances, resulting in high accuracy but zero recall for the minority class. Option A (learning rate) would not cause this behavior; it affects convergence speed. Option B (overfitting) typically reduces generalization but not in this specific pattern. Option D (too large regularization) might cause underfitting but would not necessarily yield zero recall for one class.

Answer

The regularization parameter is too large, causing underfitting.

Answer

The model is overfitting due to too many features.

Answer

The learning rate was too high.

Question 12

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 loss is increasing and accuracy decreasing, indicating divergence, which is typically caused by a learning rate that is too high.

Answer

The model is overfitting

Answer

The model is underfitting

Answer

The batch size is too large

Question 13

An organization needs to classify customer emails into categories. They have labeled data for some categories but not all. Which approach should they use?

Accepted Answer

Semi-supervised learning. Option C is correct because semi-supervised learning uses a small amount of labeled data along with a large amount of unlabeled data. Options A, B, and D are incorrect: supervised learning requires labels for all categories, unsupervised clustering would group without category labels, and reinforcement learning is for sequential decision making.

Answer

Unsupervised clustering then labeling

Answer

Supervised learning for all categories

Answer

Reinforcement learning

Question 14

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. Option B is correct because oscillating loss often indicates a learning rate that is too high; reducing it stabilizes training. Option A is incorrect because increasing the learning rate would worsen oscillation. Option C is incorrect because increasing batch size can help but not primarily address oscillation. Option D is incorrect because adding more layers could increase complexity and overfitting.

Answer

Add more layers

Answer

Increase the learning rate

Answer

Increase the batch size

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. Option A is correct because tracking accuracy on a fixed validation set over time directly reveals performance degradation due to distribution shift. Option B is incorrect because training loss may remain low even with drift. Option C is incorrect because retraining daily is a response, not a detection method. Option D is incorrect because comparing prediction distributions is less direct than performance metrics.

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