AI0-001 Multiple Select — 30 Questions

Question 1

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.)

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

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 2

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 3

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 4

Which TWO of the following are effective techniques for detecting bias in an AI model?

Accepted Answer

Fairness metrics such as equal opportunity difference. Fairness metrics such as equal opportunity difference directly quantify bias by measuring the difference in true positive rates between privileged and unprivileged groups. A value of zero indicates perfect fairness, while non-zero values reveal disparate treatment, making it a standard technique for bias detection in AI models.

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Feature importance scores

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Confusion matrix on the entire dataset

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Cross-validation accuracy

Question 5

A company uses an AI model to screen job applicants. A disparate impact analysis reveals that the model's rejection rate for a protected group is significantly higher than for others. Which THREE actions should the company take to address this?

Accepted Answer

Revisit training data for historical bias and consider reweighting. Revisiting the training data for historical bias and applying reweighting directly addresses the root cause of disparate impact. If the training data contains biased labels or skewed representation of the protected group, the model will learn and amplify those biases. Reweighting adjusts the loss function to give more importance to underrepresented or disadvantaged groups, helping to equalize error rates across groups.

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Ignore the disparity because the model is accurate overall

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Remove all demographic attributes from the dataset

Question 6

Which TWO deployment strategies allow for testing a new model version before fully rolling it out?

Accepted Answer

Canary deployment. Canary deployment is correct because it routes a small percentage of live traffic to the new model version while the majority continues using the stable version. This allows real-world validation of the new model's performance and error rates under production load before a full rollout, minimizing blast radius if issues arise.

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Shadow deployment

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Direct cutover

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Blue-Green deployment

Question 7

Which THREE factors are most critical to consider when designing a continuous integration/continuous deployment (CI/CD) pipeline for machine learning?

Accepted Answer

Data quality and schema validation. Data quality and schema validation (A) are critical because ML pipelines are highly sensitive to data drift, missing values, and schema mismatches that can silently degrade model performance. Without automated validation at the CI stage, bad data can pass through and corrupt model training or inference, leading to unreliable outputs in production.

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A/B testing framework for comparing models

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Automated unit testing of application code

Question 8

A company is choosing between fine-tuning and RAG for a legal document assistant. Which TWO factors would MOST strongly favor RAG over fine-tuning?

Accepted Answer

The legal documents are updated frequently (weekly). RAG allows dynamic retrieval from a changing document base without retraining, and provides citation sources for transparency — critical in regulated domains like law.

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The model needs to understand complex legal terminology

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The queries require deep reasoning across multiple documents

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The company has limited compute budget for training

Question 9

Which TWO techniques are commonly used to prevent overfitting in deep neural networks?

Accepted Answer

Dropout. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and forces it to learn more robust features. This reduces overfitting by introducing noise and effectively training an ensemble of sub-networks.

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Using a larger learning rate

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L1 regularization

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Increasing the number of layers

Question 10

A large enterprise is developing an internal LLM-powered assistant that can access the internet and execute code. To mitigate risks from excessive agency (e.g., the model performing unauthorized actions), which THREE security measures should be implemented?

Accepted Answer

Require human-in-the-loop approval for code execution and write operations. Requiring human-in-the-loop approval for code execution and write operations directly enforces a control over the model's agency, preventing it from performing unauthorized actions such as modifying files or executing arbitrary commands. This measure ensures that any action with side effects is vetted by a human operator, mitigating the risk of excessive agency where the LLM could autonomously cause harm.

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Deploy monitoring for anomalous input patterns

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Apply output filtering to block sensitive data in responses

Question 11

Which TWO statements correctly describe the difference between supervised and unsupervised learning?

Accepted Answer

Supervised learning requires labeled data. Supervised learning relies on labeled datasets where each training example is paired with an output label, enabling the model to learn a mapping from inputs to outputs. This is a fundamental distinction from unsupervised learning, which works with unlabeled data to find inherent structures or patterns.

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Supervised learning is only used for classification

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Unsupervised learning always requires a target variable

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Supervised learning is a subset of reinforcement learning

Question 12

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 13

A healthcare AI system is subject to GDPR because it processes patient data. Which THREE requirements must the system satisfy?

Accepted Answer

Right to explanation of decisions. Article 22 of the GDPR grants data subjects the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significant effects. For healthcare AI systems, this means patients have the right to obtain an explanation of the decision reached by the algorithm, such as how a diagnosis or treatment recommendation was derived. This requirement ensures transparency and accountability in high-stakes automated decisions.

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Explicit consent from all data subjects

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Data retention period of at least 10 years

Question 14

Which THREE of the following are key components of an AI governance framework?

Accepted Answer

Regular auditing and monitoring for compliance.. Regular auditing and monitoring for compliance (A) is a key component of an AI governance framework because it ensures that AI systems operate within legal, ethical, and organizational policies over time. Continuous monitoring detects drift, bias, or security violations, while audits provide evidence of adherence to standards such as ISO/IEC 42001 or internal governance rules. Without this, governance becomes a static policy with no enforcement or verification.

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Cloud-based deployment for scalability.

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Model accuracy thresholds for production deployment.

Question 15

Which three techniques are commonly used to mitigate overfitting in neural networks? (Choose three.)

Accepted Answer

Adding L2 regularization. Adding L2 regularization (also known as weight decay) penalizes large weights by adding a term proportional to the squared magnitude of the weights to the loss function. This forces the network to keep weights small, reducing the model's sensitivity to noise in the training data and preventing it from fitting spurious patterns, which is a direct and effective method to combat overfitting.

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Increasing training data

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Reducing number of layers

Question 16

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 17

A machine learning engineer is deploying a model to production. Which TWO practices are essential for ensuring reproducibility of model predictions?

Accepted Answer

Version-control the model artifact (e.g., using MLflow or DVC).. Version-controlling the model artifact (D) is essential because it allows you to reproduce the exact model binary that generated a prediction, ensuring that any changes to the model code, hyperparameters, or training data do not silently alter outputs. Tools like MLflow or DVC store the model along with its metadata, enabling rollback and auditability in production.

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Increase the number of training epochs to ensure convergence.

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Use the same GPU hardware for both training and inference.

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Use parallel data loading to speed up inference.

Question 18

A computer vision team is building an image classifier for rare wildlife species. The dataset has only 500 images per class, and the model overfits. Which THREE data augmentation techniques are most likely to reduce overfitting? (Choose three.)

Accepted Answer

Horizontal flip. Horizontal flip is a simple and effective data augmentation technique that doubles the training data by mirroring images, which helps the model generalize better to variations in orientation. This is particularly useful for wildlife images where the animal may appear facing left or right, reducing overfitting by exposing the model to more diverse examples without collecting new data.

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Adding Gaussian noise

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Random cropping

Question 19

An AI developer is selecting a model architecture for a real-time video surveillance system that must detect objects in each frame and also track movement patterns across frames. Which TWO architectures should the developer combine? (Choose 2)

Accepted Answer

Recurrent neural network (RNN) or LSTM. CNNs are ideal for image feature extraction; RNNs/LSTMs are designed for sequence modelling to track temporal patterns.

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Transformer encoder only

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Generative adversarial network (GAN)

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Variational autoencoder (VAE)

Question 20

A team is deploying an AI model for credit approval. Which TWO ethical considerations must be addressed?

Accepted Answer

Model interpretability. Model interpretability (B) is essential for credit approval because financial decisions must be explainable to regulators and customers under laws like GDPR or ECOA. A black-box model that cannot justify why a loan was denied violates compliance requirements, making interpretability a core ethical and legal necessity.

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Training speed

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Model accuracy

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Model size