AI0-001 Quick Quiz — 10 Questions

Question 1

A company uses an AI system to generate marketing images. They are concerned about copyright ownership of the generated content. According to current US copyright law, who typically owns the copyright for AI-generated work?

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

Accepted Answer

No one; the work may be in the public domain. Under US copyright law, copyright protection requires human authorship. Works created entirely by AI without human creative input are generally not copyrightable. The user who provides prompts may not own the copyright if the work lacks human authorship. The AI developer or AI system itself cannot hold copyright.

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The user who provided the input prompts

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The AI system itself, as the creator

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The company that owns the AI model

Question 2

A financial institution uses a machine learning model to approve personal loans. The model was trained on historical data that includes applicant age, income, credit score, and loan amount. Compliance officers have received customer complaints suggesting the model may be discriminating against applicants over 60 years old. Initial analysis shows that the approval rate for applicants over 60 is 20 percentage points lower than for younger applicants with similar credit profiles. The data science team has been asked to investigate and remediate any bias. They have access to the training data, model coefficients, and can retrain or modify the model. What is the FIRST step the team should take?

Accepted Answer

Conduct a fairness audit using appropriate metrics such as disparate impact ratio on the current model.. The first step in addressing potential bias is to conduct a fairness audit using established metrics like the disparate impact ratio (e.g., the 80% rule from the US Equal Employment Opportunity Commission). This quantifies whether the model's approval rate for applicants over 60 is less than 80% of the rate for the younger group, providing a legally and technically sound baseline before any remediation. Without this measurement, any subsequent changes (like resampling or removing features) could be misguided or ineffective.

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Replace the model with a third-party vendor model that claims to be bias-free.

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Re-sample the training data to have equal numbers of applicants over and under 60.

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Remove the age feature from the training data and retrain the model.

Question 3

Which type of neural network is BEST suited for processing sequential data such as time series or natural language?

Accepted Answer

Recurrent Neural Network (RNN). RNNs (including LSTMs) are designed for sequential data with temporal dependencies. CNNs excel at spatial data; transformers are also used but RNNs are the classic answer.

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Generative Adversarial Network (GAN)

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Multi-layer Perceptron (MLP)

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Convolutional Neural Network (CNN)

Question 4

A bank uses an AI system for credit scoring. To meet fairness requirements, they want to ensure the model predicts similar outcomes for individuals who are similar with respect to the target variable, regardless of protected attributes. Which fairness metric addresses this?

Accepted Answer

Individual fairness. Individual fairness requires that similar individuals (with respect to legitimate features) receive similar predictions. Demographic parity requires equal selection rates across groups. Equalized odds requires equality of true positive and false positive rates. Calibration ensures predicted probabilities match actual outcomes across groups.

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Demographic parity

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Calibration

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Equalised odds

Question 5

A company uses a neural network for fraud detection. The dataset has 99% legitimate, 1% fraudulent. The model achieves 99% accuracy but fails to detect most frauds. Which metric should they focus on?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified. In this fraud detection scenario with 99% legitimate and 1% fraudulent transactions, a 99% accuracy can be achieved by simply predicting all transactions as legitimate, which yields 0% recall for the fraud class. Focusing on recall ensures the model captures the majority of fraudulent cases, addressing the critical failure to detect fraud despite high accuracy.

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Precision

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F1-score

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AUC-ROC

Question 6

A data science team is deploying a real-time fraud detection model on edge devices in retail stores. The model must infer under 10ms and fit within 50MB memory. Which combination of techniques should the team apply?

Accepted Answer

Model quantization to INT8 and pruning of low-weight connections. Quantization reduces model precision (e.g., FP32 to INT8) to shrink memory and speed up inference, while pruning removes redundant parameters. Distillation can further compress. These are standard for edge deployment.

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Model parallelism and distributed inference

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Increase batch size and use FP16 precision

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Train a larger model and use distillation to transfer knowledge

Question 7

A financial institution is building a fraud detection system using a supervised learning model. The dataset is highly imbalanced with 99.9% legitimate transactions and 0.1% fraudulent ones. Which approach would be MOST effective to train the model to detect fraud?

Accepted Answer

Use SMOTE to generate synthetic fraudulent transactions. SMOTE (Synthetic Minority Oversampling Technique) is the most effective approach because it generates synthetic fraudulent transactions by interpolating between existing minority class samples, thereby balancing the dataset without losing information. This allows the model to learn decision boundaries for fraud detection more effectively than simple undersampling or metric adjustments, especially given the extreme 99.9% vs 0.1% imbalance.

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Train the model using accuracy as the performance metric

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Undersample the legitimate transactions to match the number of fraudulent ones

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Increase the regularization strength in the model

Question 8

A team monitors a production model for bias. They measure the selection rate for two demographic groups and find a significant difference. Which TWO actions should the team take to mitigate bias? (Choose two.)

Accepted Answer

Retrain the model with a balanced training dataset. Retraining with a balanced training dataset (Option C) directly addresses the root cause of bias by ensuring the model learns from equal representation of both demographic groups, which reduces skewed selection rates. This is a standard data-level mitigation technique in AI fairness, as it prevents the model from overfitting to majority patterns.

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Increase the complexity of the model to capture more patterns

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Add more training data from both groups

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Remove the protected attribute from the model input

Question 9

Which THREE are common causes of data leakage in machine learning pipelines?

Accepted Answer

Using future information to predict the present. Using future information to predict the present is a classic form of data leakage. In time series or sequential data, if a model is trained on features that include values from a later time point, it gains access to information that would not be available at prediction time, leading to overly optimistic performance metrics and poor generalization.

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Using time-based splitting for sequential data

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Using cross-validation on the entire dataset

Question 10

Which principle ensures that AI decisions can be traced back and understood by humans?

Accepted Answer

Transparency. Transparency is the principle that ensures AI decisions can be traced back and understood by humans. It requires that the internal workings of an AI model, including its inputs, decision paths, and outputs, are documented and interpretable, enabling auditability and trust. Without transparency, stakeholders cannot verify whether the AI system is behaving as intended or complying with ethical and regulatory standards.

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Privacy

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Robustness

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Accountability