AI0-001 Practice Test 24 — 15 Questions

Question 1

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. A wide gap between training and validation accuracy is a classic sign of overfitting, where the model memorizes training data but fails to generalize.

Answer

The validation set is too small

Answer

The model generalizes well

Answer

The test set should be larger

Question 2

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. Removing the dropout layer reduces regularization, allowing the model to fit the training data better and increase training accuracy.

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 3

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. Options A and B are correct. Dropout randomly drops neurons during training, preventing co-adaptation. L2 regularization adds a penalty on weights, discouraging complexity. Option C, increasing learning rate, can hinder convergence. Option D, adding more layers, typically increases overfitting. Option E, smaller batch size, can have a regularizing effect but is not as direct or commonly cited as the primary techniques.

Answer

Using a smaller batch size

Answer

Adding more layers

Answer

Increasing the learning rate

Question 4

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. With severe class imbalance, accuracy is misleading. Precision-Recall AUC focuses on minority class, and SMOTE oversamples it.

Answer

Mean Squared Error; use L2 regularization

Answer

F1 score; use principal component analysis

Answer

Accuracy; collect more data

Question 5

A data scientist is using a gradient boosting model (XGBoost) for a regression task and observes that the model's performance on the training set is much better than on the test set. Which hyperparameter tuning strategy would most effectively reduce overfitting?

Accepted Answer

Reduce the maximum depth of trees. Option A (Increase learning rate) makes each tree more influential, increasing overfitting. Option C (Increase boosting rounds) increases model complexity. Option D (Subsample less than 1.0) introduces randomness but is less direct than tree depth. Option B (Reduce max depth) limits tree complexity, reducing overfitting.

Answer

Increase the number of boosting rounds

Answer

Increase the learning rate

Answer

Subsample less than 1.0

Question 6

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). Option A is correct because recall (true positive rate) measures the ability to find positive (churn) cases, which is the goal in an imbalanced dataset. Option B, precision, is important but less critical when the cost of missing churners is high. Option C, F1-score, balances precision and recall but recall is more directly needed. Option D, specificity, measures true negative rate, not relevant for catching churners.

Answer

Precision

Answer

F1-score

Answer

Specificity

Question 7

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. Option C is correct because LSTMs have gating mechanisms that allow gradients to flow longer, mitigating vanishing gradients. Option A is incorrect because more layers can exacerbate vanishing. Option B is incorrect because a larger learning rate may cause instability. Option D is incorrect because dropout addresses overfitting, not vanishing gradients.

Answer

Add dropout regularization

Answer

Use a larger learning rate

Answer

Increase the number of hidden layers

Question 8

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

Accepted Answer

Overfitting. Option B is correct because training loss decreases while validation loss increases, a classic sign of overfitting. Option A is incorrect because underfitting would show high losses on both sets. Option C is incorrect because vanishing gradient affects training loss progression, not divergence. Option D is incorrect because data leakage typically causes both sets to perform well.

Answer

Data leakage

Answer

Underfitting

Answer

Vanishing gradient

Question 9

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 that is too high causes the optimizer to overshoot minima, leading to oscillations. Reducing the learning rate stabilizes training.

Answer

Learning rate too low; increase it

Answer

Batch size too small; increase it

Answer

Too many epochs; stop early

Question 10

Which TWO of the following are common activation functions used in deep neural networks?

Accepted Answer

ReLU. Sigmoid and ReLU are widely used activation functions. Support Vector Machine is a classifier, not an activation. K-means is a clustering algorithm. Linear regression is a model, not an activation function.

Answer

Linear Regression

Answer

Support Vector Machine

Answer

K-means

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. Option A is correct because ensuring the training data is representative of the target population is fundamental to avoid bias. Option B is incorrect because SMOTE addresses class imbalance, not bias from non-representative sampling. Option C is incorrect because model complexity does not directly address bias. Option D is incorrect because removing demographic features may not eliminate bias if proxy variables remain.

Answer

Applying SMOTE to oversample minority classes

Answer

Using a more complex algorithm

Answer

Removing all demographic features

Question 12

Refer to the exhibit. What is the recall of the model?

Accepted Answer

0.44. Recall = TP / (TP + FN) = 400 / (400 + 500) = 400 / 900 ≈ 0.444, which rounds to 0.44.

Answer

0.80

Answer

0.90

Answer

0.99

Question 13

Which TWO techniques are commonly used for feature scaling? (Choose two.)

Accepted Answer

Standardization. Options A and B are correct because Min-Max scaling and standardization are standard feature scaling methods. Options C, D, and E are incorrect: PCA is dimensionality reduction, one-hot encoding is for categorical variables, and normalization is often synonymous with scaling but here it is less specific.

Answer

One-hot encoding

Answer

Normalization

Answer

PCA

Question 14

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. Option D is correct because homoscedasticity assumes constant variance of residuals; increasing variance indicates heteroscedasticity. Option A is incorrect because linearity is about the relationship, not residual variance. Option B is incorrect because independence refers to errors being independent. Option C is incorrect because normality is about the distribution of residuals, not variance.

Answer

Normality

Answer

Linearity

Answer

Independence

Question 15

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). Option B is correct because target encoding replaces categories with the mean target value, capturing predictive power without explosion of features. Option A is incorrect because one-hot encoding creates many sparse features, inefficient for trees. Option C is incorrect because label encoding implies ordinality, which is misleading. Option D is incorrect because frequency encoding may lose target relationship.

Answer

Label encoding

Answer

One-hot encoding

Answer

Frequency encoding