AI0-001 Exam Simulation — 80 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.

Answer

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.

Answer

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.

Answer

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

Question 11

An AI model is trained to predict loan default. The training data contains 95% non-default and 5% default. Which metric is most appropriate to evaluate model performance given the imbalanced dataset?

Accepted Answer

F1-score. The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance. In this dataset with 95% non-default and 5% default, accuracy would be misleadingly high (95%) even if the model never predicts default, while F1-score penalizes poor recall of the minority class.

Answer

Mean squared error

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Accuracy

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R-squared

Question 12

Which TWO of the following are best practices for securing an AI model against adversarial attacks?

Accepted Answer

Adversarial training with perturbed examples.. Adversarial training explicitly augments the training dataset with perturbed examples (e.g., using FGSM or PGD attacks) to teach the model to recognize and resist malicious inputs. This method directly hardens the model against evasion attacks by improving its decision boundary robustness.

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Model pruning to reduce the number of parameters.

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Increasing model complexity to capture more patterns.

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Hyperparameter optimization using grid search.

Question 13

An organization is deploying a machine learning model that classifies loan applications. They want to prevent an attacker from reconstructing individual customer records from the model's predictions. Which type of attack should they defend against?

Accepted Answer

Model inversion. Model inversion attacks allow an attacker to reconstruct the original training data by analyzing the model's predictions. In this scenario, the attacker could use the model's outputs to infer sensitive details about individual loan applicants, such as income or credit history, violating privacy. Defending against model inversion is critical when predictions can be used to reverse-engineer private training records.

Answer

Membership inference

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Data poisoning

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Adversarial example

Question 14

A company needs to store large volumes of unstructured data (PDFs, images, logs) for future AI model training. The data must be easily accessible by data scientists using Spark and must support cost-effective storage. Which data infrastructure is MOST appropriate?

Accepted Answer

Amazon S3 data lake. A data lake stores raw, unstructured data at low cost and integrates with Spark. Data warehouses are for structured, processed data; vector databases are for embeddings.

Answer

Snowflake data warehouse

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Relational database like Amazon RDS

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Pinecone vector database

Question 15

A self-driving car company is testing an AI model for pedestrian detection. During simulation, the model fails to detect pedestrians in low-light conditions. The safety team wants to improve robustness without retraining the entire model from scratch. Which approach is most appropriate?

Accepted Answer

Apply data augmentation techniques to simulate low-light conditions in the training dataset.. Data augmentation techniques, such as adjusting brightness, contrast, and adding noise, can synthetically create low-light training examples from existing data. This improves the model's robustness to low-light conditions without requiring a full retraining from scratch, as it directly addresses the distribution shift in the input data.

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Replace the convolutional layers with transformer layers to improve attention.

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Use adversarial training to add imperceptible perturbations to training images.

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Increase the model's depth by adding more convolutional layers.

Question 16

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 17

An organization wants to implement an AI ethics board. Which composition best ensures independence and expertise?

Accepted Answer

Mix of internal stakeholders and external ethicists. An AI ethics board must combine internal stakeholders (who understand organizational context, data flows, and operational constraints) with external ethicists (who provide independent, unbiased perspectives and specialized knowledge of ethical frameworks like IEEE Ethically Aligned Design or the EU AI Act). This composition ensures the board can evaluate AI systems for bias, fairness, and transparency without being dominated by business or technical interests, which is critical for maintaining trust and regulatory compliance.

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All members from the legal department

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IT department head and data scientists

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Only senior executives from the company

Question 18

A retail company uses a gradient boosting model to predict customer lifetime value (CLV). The model currently uses 50 features including purchase history, demographics, and web behavior. The model's RMSE on the test set is 120. The data science team wants to improve the model's accuracy without increasing training time significantly. They have access to additional data: customer support interaction logs (text), social media sentiment (text), and third-party credit scores (numeric). They also have the ability to perform feature engineering, hyperparameter tuning, and ensemble methods. Which approach is most likely to yield the best improvement in predictive performance with minimal increase in training time?

Accepted Answer

Engineer new features such as average purchase value and recency. Engineering domain-relevant features like average purchase value and recency directly captures the underlying behavioral patterns that drive customer lifetime value, often providing a higher signal-to-noise ratio than adding raw text or third-party data. This approach leverages existing data without significantly increasing the feature dimensionality or training time, unlike adding TF-IDF vectors which would dramatically expand the feature space and slow training.

Answer

Add the customer support text as a feature using TF-IDF vectors

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Use an ensemble of gradient boosting and random forest models

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Perform hyperparameter tuning using grid search

Question 19

During model training, the data science team discovers that many input features contain missing values. Which step should be taken to improve data quality?

Accepted Answer

Implement data validation checks to handle missing data appropriately (e.g., imputation).. Data validation checks, such as imputation (e.g., mean, median, or KNN imputation), directly address missing values by estimating plausible replacements based on the available data. This improves data quality and prevents bias or loss of information that could degrade model performance. In the context of AI implementation, handling missing data is a fundamental data preprocessing step to ensure robust model training.

Answer

Increase the model complexity to handle missing data.

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Ignore missing values and train the model.

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Remove all records with missing values.

Question 20

A team is using an API from a cloud AI service to generate text. They notice that repeated requests with the same prompt return different outputs. They want consistent responses for testing. Which parameter should they adjust?

Accepted Answer

Set the temperature to 0. Setting the temperature to 0 makes the model deterministic, producing the same output for the same input, which is ideal for testing.

Answer

Increase the top_p parameter to 1.0

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Set the frequency_penalty to 0

Answer

Increase the max_tokens parameter