AI0-001 AI Concepts and Foundations Practice Test 4 — 15 Questions

Question 1

A company is implementing an AI solution for fraud detection. The dataset is highly imbalanced (only 1% fraudulent transactions). Which THREE techniques are most appropriate to address class imbalance? (Select three.)

Accepted Answer

Apply cost-sensitive learning by assigning a higher misclassification cost to the minority class.. Option A is correct because cost-sensitive learning directly addresses class imbalance by assigning a higher misclassification cost to the minority class (fraudulent transactions). This forces the model to penalize false negatives more heavily, thereby improving recall for the minority class without altering the dataset distribution.

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Reduce the number of features using principal component analysis (PCA).

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Use accuracy as the primary evaluation metric.

Question 2

A team is deploying a deep learning model that uses a convolutional neural network (CNN) for image recognition. The model achieves high accuracy but is very slow to infer on edge devices. Which THREE optimization techniques should the team consider to speed up inference without significant accuracy loss? (Select three.)

Accepted Answer

Use weight pruning to remove unnecessary connections in the network.. Weight pruning removes redundant or less important connections (weights) from the neural network, reducing the number of computations required during inference. This directly speeds up inference on edge devices while typically causing only a minor drop in accuracy if done carefully, making it a standard optimization technique for deploying CNNs on resource-constrained hardware.

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Use larger convolutional filters (e.g., 7x7 instead of 3x3) to capture more context.

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Increase the number of convolutional layers to improve feature extraction.

Question 3

Refer to the exhibit. The data scientist notices that the model achieves 98% accuracy on the training set but only 72% on the test set. Which change to the model parameters is most likely to reduce this gap?

Accepted Answer

Reduce max_depth to 3.. The model is overfitting: 98% training accuracy vs. 72% test accuracy. Reducing max_depth to 3 limits the depth of each decision tree, preventing them from memorizing noise and forcing them to learn more generalizable patterns. This is a standard regularization technique for tree-based ensembles.

Answer

Increase n_estimators to 500.

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Set max_depth to None to allow trees to grow fully.

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Switch from RandomForest to a linear model like LogisticRegression.

Question 4

Refer to the exhibit. The model is a neural network for 10-class classification. The training log shows no improvement over 5 epochs. Which of the following is the most likely root cause?

Accepted Answer

The model is suffering from vanishing gradients, preventing weight updates.. The training log shows no improvement over 5 epochs, which is a classic symptom of vanishing gradients in deep neural networks. When gradients become extremely small during backpropagation, weight updates are negligible, causing the loss to stagnate. This is especially common in deep networks with sigmoid or tanh activations, where gradients saturate in the tails of the activation function.

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The batch size is too large, making gradient updates insignificant.

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The output layer uses sigmoid activation instead of softmax.

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The learning rate is too high, causing the loss to oscillate.

Question 5

Refer to the exhibit. A team deploys a sentiment analysis model with this policy. After one month, the monitoring system triggers an alert for feature drift. Which action should the team take first?

Accepted Answer

Compare the current feature distributions with the training set to identify which features drifted.. Option C is correct because when a monitoring system triggers an alert for feature drift, the first step is to diagnose which features have changed. Comparing current feature distributions with the training set identifies the specific features that drifted, enabling targeted remediation such as retraining with recent data or feature engineering. This aligns with the standard MLOps workflow for drift detection and response.

Answer

Review the fairness check settings to ensure protected attributes are still relevant.

Answer

Immediately retrain the model on recent data to adapt to the drift.

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Reduce the classification threshold to 0.5 to increase sensitivity.

Question 6

A data scientist is training a model to classify customer support tickets into categories. The dataset has 10,000 labeled examples, but the 'billing' category contains 8,000 examples while the 'technical' category contains 2,000. Which technique is most appropriate to address this imbalance before training?

Accepted Answer

Apply random oversampling on the 'technical' category.. Option A is correct because random oversampling duplicates examples from the minority class ('technical') to balance the class distribution, preventing the model from becoming biased toward the majority class ('billing'). This technique directly addresses the class imbalance before training, which is critical for classification tasks where the minority class is underrepresented.

Answer

Remove all examples except 'billing' and use a one-class classifier.

Answer

Use accuracy as the only evaluation metric.

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Train the model as is, then adjust thresholds post-training.

Question 7

A team deploying an AI model for real-time fraud detection notices that inference latency is too high. The model is a deep neural network with 50 layers, deployed on a cloud GPU. Which of the following is the BEST approach to reduce latency while maintaining acceptable accuracy?

Accepted Answer

Apply knowledge distillation to create a smaller model.. Knowledge distillation trains a smaller 'student' model to mimic the behavior of a larger 'teacher' model, significantly reducing the number of parameters and layers while preserving most of the original accuracy. This directly addresses the high inference latency caused by the 50-layer DNN by producing a compact model that runs faster on the same GPU hardware.

Answer

Deploy the model on a more powerful GPU.

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Reduce the batch size for inference.

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Replace the DNN with a logistic regression model.

Question 8

An AI system is being developed to diagnose diseases from medical images. The model achieves 99% accuracy on the test set, but when deployed in a different hospital, performance drops significantly. Which of the following is the MOST likely cause?

Accepted Answer

The training data does not represent the new hospital's population or imaging equipment.. The model's high accuracy on the test set but poor performance in a different hospital indicates a distribution shift between the training data and the deployment environment. This is a classic case of dataset shift, where the training data does not represent the new hospital's patient population or imaging equipment, leading to degraded model generalization.

Answer

The model is being attacked by adversarial examples.

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The model is overfitted to the training data.

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Data leakage occurred during preprocessing.

Question 9

A company wants to use AI to analyze customer reviews and determine sentiment (positive, negative, neutral). Which AI subfield is most directly applicable?

Accepted Answer

Natural language processing. Natural language processing (NLP) is the AI subfield that enables machines to understand, interpret, and generate human language. Analyzing customer reviews for sentiment requires processing text, extracting meaning, and classifying it as positive, negative, or neutral, which is a core NLP task called sentiment analysis.

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Reinforcement learning

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Computer vision

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Robotics

Question 10

A team is training a neural network for image classification. They observe that training loss decreases steadily but validation loss starts increasing after 20 epochs. What is the most likely issue?

Accepted Answer

Overfitting. Option A is correct because increasing validation loss while training loss continues to decrease is a classic sign of overfitting. Option B (underfitting) would show poor training loss. Option C (vanishing gradients) would cause slow convergence. Option D (data leakage) would affect validation if leaked, but pattern is overfitting.

Answer

Underfitting

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Vanishing gradients

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Data leakage

Question 11

An organization is developing an AI system to approve loan applications. They want to ensure the model does not discriminate based on race or gender. Which technique BEST addresses this concern?

Accepted Answer

Apply adversarial debiasing during model training.. Adversarial debiasing is a technique that explicitly trains the model to remove sensitive information (like race or gender) from its internal representations, preventing the model from learning discriminatory patterns even if correlated features remain. This directly addresses fairness by making the model's predictions independent of protected attributes, which is more robust than simply removing features (which can still allow proxy discrimination).

Answer

Remove race and gender features from the training data.

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Use a more complex model to capture nuances.

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Collect more training data from diverse populations.

Question 12

A machine learning engineer wants to evaluate a binary classifier. Which metric is MOST appropriate when the positive class is rare (e.g., 1% of total data)?

Accepted Answer

F1-score. When the positive class is rare (e.g., 1% of total data), accuracy is misleading because a classifier that always predicts the negative class would achieve 99% accuracy. The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance by focusing on the positive class performance. It is the most appropriate metric for evaluating binary classifiers on imbalanced datasets.

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True negative rate

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Mean squared error

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Accuracy

Question 13

A company uses a pre-trained language model for a legal document classification task. They have limited labeled data (500 documents). Which strategy is MOST effective for adapting the model to this domain?

Accepted Answer

Fine-tune the pre-trained model on the 500 labeled documents.. Fine-tuning a pre-trained language model on 500 labeled legal documents is the most effective strategy because it leverages the model's existing knowledge of language structure and general semantics, requiring only a small amount of domain-specific data to adapt to the legal classification task. This approach avoids the high data requirements of training from scratch and outperforms rule-based or augmentation-only methods by directly optimizing the model's weights for the target domain.

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Use a rule-based keyword matching system instead.

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Train a new model from scratch on the 500 documents.

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Apply extensive data augmentation to increase dataset size.

Question 14

A team is designing an AI system for autonomous driving. They need to decide between an end-to-end deep learning approach versus a modular pipeline (perception, planning, control). Which is a key advantage of the modular approach?

Accepted Answer

Each module can be validated separately.. Option C is correct because modular systems allow independent testing and debugging of each component. Option A (end-to-end simplicity) is not true; modular is more complex. Option B (lower latency) is not inherent. Option D (end-to-end can be more robust to novel situations) is debated but modular offers better interpretability.

Answer

It typically has lower inference latency.

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It handles novel scenarios better due to joint training.

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It requires less engineering effort.

Question 15

Which TWO of the following are common techniques to reduce overfitting in a neural network?

Accepted Answer

L2 regularization. L2 regularization (option C) reduces overfitting by adding a penalty term proportional to the squared magnitude of the weights to the loss function. This forces the network to keep weights small, preventing it from fitting noise in the training data and improving generalization.

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Increasing the number of hidden layers

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Using a larger learning rate

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Training for more epochs