AI0-001 AI Concepts and Foundations Practice Test 5 — 15 Questions

Question 1

An organization is developing an AI system to approve loan applications. They want to ensure the model does not discriminate based on race or gender. Which technique BEST addresses this concern?

Accepted Answer

Apply adversarial debiasing during model training.. Adversarial debiasing is a technique that explicitly trains the model to remove sensitive information (like race or gender) from its internal representations, preventing the model from learning discriminatory patterns even if correlated features remain. This directly addresses fairness by making the model's predictions independent of protected attributes, which is more robust than simply removing features (which can still allow proxy discrimination).

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Remove race and gender features from the training data.

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Use a more complex model to capture nuances.

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Collect more training data from diverse populations.

Question 2

A machine learning engineer wants to evaluate a binary classifier. Which metric is MOST appropriate when the positive class is rare (e.g., 1% of total data)?

Accepted Answer

F1-score. When the positive class is rare (e.g., 1% of total data), accuracy is misleading because a classifier that always predicts the negative class would achieve 99% accuracy. The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance by focusing on the positive class performance. It is the most appropriate metric for evaluating binary classifiers on imbalanced datasets.

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True negative rate

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Mean squared error

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Accuracy

Question 3

A company uses a pre-trained language model for a legal document classification task. They have limited labeled data (500 documents). Which strategy is MOST effective for adapting the model to this domain?

Accepted Answer

Fine-tune the pre-trained model on the 500 labeled documents.. Fine-tuning a pre-trained language model on 500 labeled legal documents is the most effective strategy because it leverages the model's existing knowledge of language structure and general semantics, requiring only a small amount of domain-specific data to adapt to the legal classification task. This approach avoids the high data requirements of training from scratch and outperforms rule-based or augmentation-only methods by directly optimizing the model's weights for the target domain.

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Use a rule-based keyword matching system instead.

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Train a new model from scratch on the 500 documents.

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Apply extensive data augmentation to increase dataset size.

Question 4

Which TWO of the following are common techniques to reduce overfitting in a neural network?

Accepted Answer

L2 regularization. L2 regularization (option C) reduces overfitting by adding a penalty term proportional to the squared magnitude of the weights to the loss function. This forces the network to keep weights small, preventing it from fitting noise in the training data and improving generalization.

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Increasing the number of hidden layers

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Using a larger learning rate

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Training for more epochs

Question 5

Which TWO of the following are appropriate uses of unsupervised learning?

Accepted Answer

Detecting unusual patterns in network traffic that may indicate a cyberattack. Unsupervised learning discovers hidden patterns or structures in unlabeled data. Detecting unusual patterns in network traffic (option C) is a classic anomaly detection task, often performed using clustering or autoencoders, where the model learns 'normal' behavior and flags deviations without requiring labeled attack data.

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Classifying emails as spam or not spam

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Predicting the sale price of a house given its features

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Identifying a person from a photo

Question 6

Which THREE of the following are key considerations when deploying an AI model in a production environment?

Accepted Answer

Monitoring model performance for data drift. Data drift refers to the change in the statistical properties of the input data over time, which can degrade model accuracy. Continuous monitoring for data drift is essential in production to detect when the model's assumptions about the data distribution are no longer valid, triggering retraining or alerts.

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Basing acceptance solely on training accuracy

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Maximizing model complexity to achieve the best accuracy

Question 7

A data scientist wants to group customers into segments based on purchasing behavior without predefined labels. Which type of machine learning is most appropriate?

Accepted Answer

Unsupervised learning. Unsupervised learning is the correct choice because the data scientist has no predefined labels and wants to discover natural groupings in customer purchasing behavior. Clustering algorithms, such as K-means or DBSCAN, are used in unsupervised learning to segment data based on inherent patterns without any target variable.

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Reinforcement learning

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Supervised learning

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Semi-supervised learning

Question 8

An AI model achieves high accuracy on training data but performs poorly on new test data. The data scientist suspects the model has memorized noise. Which technique directly adds a penalty term to the loss function to address this?

Accepted Answer

L2 regularization. L2 regularization (also known as weight decay) directly adds a penalty term proportional to the squared magnitude of the model's weights to the loss function. This discourages the model from fitting the noise in the training data by keeping weights small, thereby reducing overfitting and improving generalization to new test data.

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Batch normalization

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Data augmentation

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Dropout

Question 9

A company deploys a chatbot using a large language model (LLM). After launch, users report that the chatbot sometimes generates plausible but false information. This phenomenon is known as:

Accepted Answer

Hallucination. Hallucination in LLMs refers to the generation of plausible but factually incorrect or nonsensical information. This occurs when the model's probabilistic next-token prediction produces confident-sounding outputs that deviate from training data or real-world facts, often due to insufficient grounding or training data gaps.

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Gradient explosion

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Overfitting

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Concept drift

Question 10

In the AI lifecycle, which phase involves splitting data into training, validation, and test sets?

Accepted Answer

Data preprocessing. Data preprocessing is the phase where raw data is cleaned, transformed, and prepared for modeling. Splitting the dataset into training, validation, and test sets is a critical step during this phase to ensure unbiased evaluation and prevent data leakage. This split occurs before any model training begins, making it part of preprocessing rather than training or evaluation.

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Model training

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Data collection

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Model evaluation

Question 11

A self-driving car uses an AI model that learns by trial and error, receiving rewards for correct actions and penalties for mistakes. This type of learning is:

Accepted Answer

Reinforcement learning. Reinforcement learning (RL) is the correct answer because the self-driving car's AI model learns through trial and error, receiving rewards for correct actions and penalties for mistakes. This feedback-driven process, where an agent interacts with an environment to maximize cumulative reward, is the defining characteristic of reinforcement learning, not supervised or unsupervised learning.

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Supervised learning

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Unsupervised learning

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Transfer learning

Question 12

An AI team notices that their model's performance degrades over time because the statistical relationship between input features and the target variable changes. This issue is called:

Accepted Answer

Concept drift. Concept drift occurs when the statistical relationship between input features and the target variable changes over time, causing model performance to degrade. This is distinct from data drift, which involves changes in the input data distribution alone. In the AI0-001 context, concept drift directly addresses the shift in the underlying mapping from features to labels.

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Data drift

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Overfitting

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Model drift

Question 13

Which metric is most appropriate for evaluating a binary classification model where the positive class is rare and false positives are costly?

Accepted Answer

Precision. Precision is the most appropriate metric when the positive class is rare and false positives are costly because it measures the proportion of true positive predictions among all positive predictions. In this scenario, minimizing false positives is critical, and precision directly penalizes them by requiring high confidence before labeling an instance as positive. This aligns with the business need to avoid costly false alarms, such as in fraud detection or medical diagnosis for rare diseases.

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Accuracy

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F1-score

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Recall

Question 14

A company wants to create an AI system that can identify objects in images. They have a large dataset of labeled images. Which type of neural network architecture is most suitable?

Accepted Answer

Convolutional neural network (CNN). Convolutional neural networks (CNNs) are specifically designed to process grid-like data such as images. They use convolutional layers to automatically learn spatial hierarchies of features (edges, textures, objects) from pixel data, making them the most suitable architecture for image classification tasks with labeled datasets.

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Transformer

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Generative adversarial network (GAN)

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Recurrent neural network (RNN)

Question 15

A data scientist is preparing a dataset for supervised learning. Which TWO steps are essential?

Accepted Answer

Labeling the data. Labeling the data is essential for supervised learning because the algorithm requires input-output pairs to learn a mapping function. Without labeled data, the model cannot be trained to predict outcomes, as supervised learning relies on ground-truth targets for error correction during training.

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One-hot encoding all features

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Normalizing features

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Removing outliers