AI0-001 Practice Test 6 — 10 Questions

Question 1

A healthcare organization uses an AI model to predict patient readmission risk. To comply with patient privacy regulations, they apply differential privacy during training. What is the primary trade-off of using differential privacy?

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

Accepted Answer

Reduced model accuracy for increased privacy. Differential privacy works by adding calibrated noise to the training process or model outputs, which directly reduces the model's accuracy in exchange for a quantifiable privacy guarantee (e.g., ε-differential privacy). This trade-off is fundamental: stronger privacy (lower ε) requires more noise, which degrades predictive performance. The healthcare organization must balance the need to protect patient data against the clinical utility of accurate readmission predictions.

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Increased training time for reduced bias

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Lower interpretability for higher fairness

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Faster inference for lower memory usage

Question 2

An e-commerce company deploys a recommendation system using collaborative filtering. After launch, the system shows high accuracy for popular items but fails to recommend niche products to users who would likely buy them. Which technique should the team implement to improve recommendations for long-tail items?

Accepted Answer

Switch to a hybrid filtering approach that incorporates item metadata. Collaborative filtering relies on user-item interactions, which are sparse for niche products (the long tail). A hybrid filtering approach that incorporates item metadata (e.g., category, description, attributes) can bridge the gap by using content-based signals to recommend niche items even when interaction data is limited. This directly addresses the cold-start and sparsity problems for long-tail items.

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Apply matrix factorization with higher latent factors

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Increase the weight of popular items in the recommendation score

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Collect more user interaction data over time

Question 3

A hospital uses an AI system to prioritize patient triage based on vital signs and medical history. During a trial, the system consistently assigns lower urgency to elderly patients with chronic conditions, even when their symptoms suggest high risk. Which approach best addresses this bias?

Accepted Answer

Audit the training data for representation of elderly patients and retrain with balanced data. The bias originates from the training data underrepresenting elderly patients with chronic conditions, causing the model to learn skewed urgency patterns. Auditing the data for representation and retraining with balanced data directly addresses the root cause by ensuring the model learns from a fair distribution of cases, which is a standard bias mitigation technique in AI systems.

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Use a different dataset from a similar hospital without checking demographics

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Manually increase the weight of age-related features in the model

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Replace the neural network with a decision tree to simplify decision logic

Question 4

A company implements an AI-based chatbot for customer service. After deployment, customers report that the chatbot sometimes uses offensive language. The development team reviews the training data and finds no explicit offensive content. What is the most likely explanation?

Accepted Answer

The model learned biased language patterns from the training corpus. The chatbot's offensive language likely stems from biased or toxic patterns present in the training corpus, even if no explicit offensive content was flagged. Large language models learn statistical associations from their training data, and if the corpus contains subtle biases, stereotypes, or indirect toxic language, the model can reproduce these patterns in its responses. This is a well-known issue in AI ethics and governance, where models inadvertently amplify societal biases embedded in the data.

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There is a bug in the deployment pipeline

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The model is overfitting to rare examples

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The training data was poisoned by an attacker

Question 5

Which THREE factors are common causes of bias in AI systems?

Accepted Answer

Lack of diversity in the development team. A lack of diversity in the development team leads to homogeneity of thought, which can cause blind spots in identifying potential biases in data, features, or model behavior. When the team does not represent the full spectrum of end users, the AI system may inadvertently encode assumptions that disadvantage underrepresented groups, resulting in biased outcomes.

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Cross-validation

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High regularization

Question 6

Which technique adds controlled noise to query results or training data to prevent an attacker from inferring whether a specific individual's data was included in the dataset?

Accepted Answer

Differential privacy. Differential privacy is the technique that adds controlled statistical noise (e.g., Laplace or Gaussian noise) to query results or training data so that the presence or absence of any single individual's data cannot be inferred. It provides a mathematical guarantee (epsilon-differential privacy) that the output distribution is nearly identical whether or not a specific record is included. This directly matches the question's requirement.

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Anonymisation

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Federated learning

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Pseudonymisation

Question 7

A team is training a deep learning model for natural language processing using a large corpus. They notice the model has a very high number of parameters and training is slow. Which technique can reduce the number of parameters without significant performance loss?

Accepted Answer

Apply embedding compression. Embedding compression reduces the dimensionality of the embedding layer, which often contains the majority of the model's parameters in NLP tasks. By using techniques like low-rank factorization or pruning, the model retains most of its representational power while significantly decreasing the parameter count and training time.

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Add more dropout layers

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Use a larger batch size

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Increase learning rate

Question 8

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?

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 9

A data scientist is training a model to detect fraudulent transactions. To protect customer privacy, the team wants to ensure that the model does not inadvertently memorize and reveal sensitive information about individuals in the training set. Which technique should be applied during training?

Accepted Answer

Differential privacy. Differential privacy is the correct technique because it adds calibrated noise to the training process or output, ensuring that the model cannot infer whether any specific individual's data was included in the training set. This directly addresses the goal of preventing memorization and leakage of sensitive information while still allowing the model to learn useful patterns for fraud detection.

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Federated learning

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Homomorphic encryption

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Model quantization

Question 10

A data scientist needs to predict whether a customer will churn (yes/no) based on historical data. Which type of machine learning problem is this?

Accepted Answer

Binary classification. This is a binary classification problem because the target variable has exactly two discrete outcomes: 'yes' (churn) or 'no' (no churn). Classification algorithms such as logistic regression, decision trees, or support vector machines are used to assign input features to one of these two predefined classes. The output is a categorical label, not a continuous value or a reward signal.

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Reinforcement learning

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Regression

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Clustering