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

Question 1

A research team is training a deep neural network for image classification. The training loss decreases rapidly for the first few epochs but then plateaus, while validation loss starts to increase after epoch 10. Which action would best address this issue?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Implement early stopping based on validation loss to prevent further overfitting.. The training loss decreasing rapidly then plateauing while validation loss increases after epoch 10 is a classic sign of overfitting. Early stopping monitors validation loss and halts training when it begins to rise, preventing the model from memorizing noise in the training data. This directly addresses the overfitting issue without requiring architectural or hyperparameter changes that could destabilize training.

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Reduce the batch size to introduce more noise during training.

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Increase the learning rate to help the model escape the plateau.

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Add more convolutional layers to increase model capacity.

Question 2

A chatbot developer uses a transformer-based model for customer service. Users complain that the chatbot sometimes gives offensive responses. Which technique should be applied first to mitigate this issue?

Accepted Answer

Review and filter the training dataset for offensive or biased language, then fine-tune the model.. The root cause of offensive responses in transformer-based models is typically biased or toxic language present in the training data. Reviewing and filtering the dataset to remove such content, followed by fine-tuning the model, directly addresses the source of the problem. This approach aligns with the principle of data-centric AI, where improving data quality is the first step before modifying model architecture or inference parameters.

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Increase the model size to improve its understanding of context.

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Decrease the temperature parameter to make outputs more deterministic.

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Train a separate classifier to detect offensive outputs in real time.

Question 3

A financial institution uses a regression model to predict credit risk. The model has a high R-squared on training data but low R-squared on test data. Which of the following is the most likely cause?

Accepted Answer

The model is overfitting the training data.. A high R-squared on training data combined with a low R-squared on test data is the classic symptom of overfitting. The model has memorized noise and specific patterns in the training set rather than learning generalizable relationships, causing poor performance on unseen data.

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The features were not standardized before training.

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The model is underfitting the training data.

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There is multicollinearity among the input features.

Question 4

An AI system for autonomous vehicles uses reinforcement learning (RL) to navigate. The reward function encourages reaching the destination quickly but penalizes collisions heavily. The agent learns to drive aggressively, causing minor accidents. Which modification to the reward function would best align the agent's behavior with desired safe driving?

Accepted Answer

Add a penalty term for high acceleration and jerky movements.. Adding a penalty for high acceleration and jerky movements directly addresses the root cause of the aggressive driving behavior—smoothness and safety—without undermining the primary goal of reaching the destination. This modification shapes the reward function to penalize unsafe driving patterns, aligning the agent's learned policy with desired safe navigation while preserving the time-based incentive for efficiency.

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Increase the collision penalty to a very large negative value.

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Remove the time-based reward and only reward reaching the destination.

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Use a potential-based reward shaping to encourage progress toward destination.

Question 5

A data scientist is building a natural language processing model to classify customer reviews as positive or negative. Which TWO preprocessing steps are most essential before tokenization? (Select two.)

Accepted Answer

Remove punctuation and special characters.. Removing punctuation and special characters (Option B) is essential because tokenizers typically split on whitespace, so punctuation attached to words (e.g., 'great!', 'bad.') would create noisy tokens like 'great!' and 'bad.' instead of clean tokens 'great' and 'bad'. Converting all text to lowercase (Option C) ensures that words like 'Great', 'great', and 'GREAT' are all mapped to the same token, preventing the model from treating them as distinct features and reducing vocabulary size.

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Perform stemming or lemmatization.

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Remove stop words from the text.

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Replace missing values with a placeholder.

Question 6

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.. 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 7

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 8

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.

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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 9

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 10

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.. 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.

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

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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 11

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.. 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.

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

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Use accuracy as the only evaluation metric.

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

Question 12

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.

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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 13

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.

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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 14

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 15

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. The training loss decreasing while validation loss increases after 20 epochs is the classic signature of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, causing it to perform poorly on unseen validation data.

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Underfitting

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Vanishing gradients

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Data leakage