AI0-001 AI Concepts and Foundations — 50 Questions

Question 1

A company deploys an AI model to predict equipment failure. The model performs well on historical data but fails to generalize to new data from a different factory. Which concept best describes this issue?

Accepted Answer

Overfitting. Option C (Overfitting) is correct because the model learned patterns specific to the historical data from the original factory, including noise and factory-specific nuances, rather than generalizable features. When applied to new data from a different factory, those learned patterns do not hold, causing poor performance. This is the classic symptom of overfitting: high accuracy on training data but low accuracy on unseen data.

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Transfer learning

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Underfitting

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Bias-variance tradeoff

Question 2

A data scientist trains a linear regression model to predict house prices. The model has high bias and low variance. Which action would most likely reduce bias?

Accepted Answer

Add polynomial features. High bias indicates the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding polynomial features increases model complexity by introducing non-linear terms, which allows the linear regression model to better fit the training data and thus reduce bias.

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Apply L2 regularization

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Increase the training dataset size

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Remove irrelevant features

Question 3

An AI engineer trains a deep learning model for image classification. After training, the training accuracy is 99% but validation accuracy is 85%. Which technique would best address this discrepancy?

Accepted Answer

Add dropout layers. The high training accuracy (99%) and lower validation accuracy (85%) indicate overfitting, where the model memorizes training data but fails to generalize. Dropout layers randomly deactivate neurons during training, forcing the network to learn more robust features and reducing overfitting. This technique directly addresses the discrepancy by improving validation performance without sacrificing training capacity.

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Increase data augmentation

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Decrease the learning rate

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Increase the number of layers

Question 4

A company implements a chatbot using a rule-based system. Users complain the chatbot cannot handle new queries. Which AI approach should be considered to improve flexibility?

Accepted Answer

Machine learning. Machine learning (ML) enables a chatbot to learn from new data and adapt to unseen queries, unlike a static rule-based system. By training on historical conversations, an ML model can generalize patterns and handle novel inputs without requiring explicit rules for every scenario.

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Expert system

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Natural language processing (NLP)

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Robotic process automation

Question 5

An AI model for detecting fraudulent transactions has high precision but low recall. Which business impact is most likely?

Accepted Answer

Many fraudulent transactions go undetected. High precision means that when the model flags a transaction as fraudulent, it is very likely correct. However, low recall indicates that the model misses a significant proportion of actual fraudulent transactions. Therefore, the most likely business impact is that many fraudulent transactions go undetected, leading to financial losses.

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The model has no impact on fraud detection

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The model detects all fraudulent transactions

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Many legitimate transactions are flagged as fraud

Question 6

A data scientist splits a dataset into training (80%) and test (20%). After training, the model achieves 95% accuracy on training and 60% on test. Which step should the data scientist take first?

Accepted Answer

Apply regularization. The model shows high training accuracy (95%) but significantly lower test accuracy (60%), which is a classic sign of overfitting. Regularization (Option C) directly addresses overfitting by adding a penalty term to the loss function (e.g., L1 or L2 regularization), discouraging the model from learning overly complex patterns that do not generalize. This is the first step because it targets the core issue without requiring additional data or increasing complexity.

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Collect more data

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Use cross-validation

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Increase model complexity

Question 7

An organization wants to classify support tickets into categories (billing, technical, etc.). Which type of machine learning is most suitable?

Accepted Answer

Supervised learning. Supervised learning is the correct choice because the organization has labeled historical support tickets (e.g., 'billing' or 'technical') and wants to train a model to map new tickets to these predefined categories. This is a classic classification task, where the algorithm learns from input-output pairs to predict the correct label for unseen data.

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Unsupervised learning

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Reinforcement learning

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Regression

Question 8

Which TWO techniques are commonly used to handle missing data in a dataset?

Accepted Answer

Remove rows with missing values. Option C is correct because removing rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that dropping a few rows does not significantly reduce the sample size or introduce bias. Option D is correct because imputing missing values with the mean or median is a common statistical method that preserves the dataset size and is simple to implement, though it can reduce variance and may distort relationships if the data is not missing completely at random.

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Feature scaling

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One-hot encoding

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Principal component analysis (PCA)

Question 9

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

Accepted Answer

Lack of diversity in the development team. Option B is correct because 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 10

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

Accepted Answer

Supervised learning requires labeled data. Option C is correct because 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 11

Refer to the exhibit. A deep learning model is being trained. Based on the training log, which problem is most evident?

Accepted Answer

Overfitting. The training log shows that the training loss continues to decrease while the validation loss increases after a certain epoch, which is a classic sign of overfitting. The model is memorizing the training data rather than learning generalizable patterns, leading to poor performance on unseen data.

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Vanishing gradients

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Underfitting

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Data leakage

Question 12

Refer to the exhibit. A data scientist defines a model configuration in JSON. Which component is missing from the configuration for a complete machine learning pipeline?

Accepted Answer

Data preprocessing steps. A complete machine learning pipeline must include data preprocessing steps to transform raw data into a format suitable for model training. The JSON configuration defines the model type, evaluation metrics, and training hyperparameters, but omits any specification for data cleaning, normalization, feature encoding, or splitting, which are essential for reproducibility and model performance.

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Training hyperparameters

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Model type

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Evaluation metrics

Question 13

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. Option D is correct because 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 14

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 15

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset has 99% legitimate transactions and 1% fraudulent. The model achieves 99% accuracy but fails to catch most fraud. Which metric should the team prioritize to evaluate model performance?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases (fraud) correctly identified. With 99% accuracy but failing to catch most fraud, the model is biased toward the majority class (legitimate transactions), so recall is the critical metric to ensure fraud detection improves.

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F1 score

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Precision

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Accuracy

Question 16

A startup is building a chatbot to handle customer inquiries. They want the chatbot to understand context and provide accurate responses without requiring extensive labeled data. Which AI approach is most suitable?

Accepted Answer

Transfer learning with a pre-trained transformer model. Transfer learning with a pre-trained transformer model (e.g., BERT, GPT) is the most suitable approach because it allows the chatbot to understand context and generate accurate responses using knowledge learned from vast general-domain text, requiring only minimal fine-tuning on the startup's specific customer inquiry data. This eliminates the need for extensive labeled datasets, as the model already captures nuanced language patterns and contextual relationships through its self-attention mechanism.

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Reinforcement learning from human feedback

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Rule-based natural language processing

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Convolutional neural networks (CNNs)

Question 17

Which TWO of the following are key characteristics of unsupervised learning?

Accepted Answer

It uses data without labeled responses. Option A is correct because unsupervised learning algorithms, such as k-means clustering or hierarchical clustering, operate exclusively on input data that has no labeled responses. The model must infer the underlying structure directly from the features without any ground-truth outputs to guide it, which is the defining characteristic of unsupervised learning.

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It predicts a target variable based on input features

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It requires a reward signal to learn optimal actions

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It typically requires a separate validation set for tuning

Question 18

A company uses the above policy to control AI model access. A data scientist tries to run inference with model "llama-3-70b" at 150 requests in 30 minutes. What will happen?

Accepted Answer

The first 100 requests are allowed; the remaining 50 are denied. Option C is correct because the policy allows up to 100 requests per 30 minutes for models in the allowed list, and 'llama-3-70b' is in that list. The rate limit is applied per 30-minute window, not per minute, so the first 100 requests are allowed, and the remaining 50 exceed the limit and are denied.

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All requests are allowed because the model is in the allowed list

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All requests are denied because the rate limit is per minute and 150 exceeds the limit

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All requests are denied because the second rule blocks all models

Question 19

A manufacturing company uses a computer vision AI to inspect products on an assembly line for defects. The AI model was trained on images from a single camera angle under bright, uniform lighting. Recently, the company moved the inspection station to a different part of the factory where lighting is dimmer and varies due to nearby windows. The model now misclassifies many non-defective products as defective, causing false alarms and production delays. The team has limited labeled data from the new environment. Which action should the team take to restore inspection accuracy while minimizing downtime?

Accepted Answer

Apply domain adaptation techniques using a small set of labeled images from the new environment. Domain adaptation techniques allow a model trained on a source domain (bright, uniform lighting) to generalize to a target domain (dim, variable lighting) using only a small set of labeled images from the new environment. This approach minimizes downtime because it avoids the need for large-scale data collection or retraining from scratch, and it directly addresses the distribution shift that causes false positives.

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Increase the defect classification threshold to reduce false positives

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Revert to the previous lighting setup by reinstalling bright, uniform lights

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Retrain the model from scratch using a large dataset of images from the new environment

Question 20

A financial institution uses a machine learning model to approve loan applications. The model was trained on historical data that inadvertently encoded a bias against applicants from certain zip codes, leading to discriminatory lending practices. A recent audit reveals that the model's decisions are unfair, and regulators require the bank to remediate the bias without significantly reducing overall approval accuracy. The data science team has access to the training data, the model, and a set of fairness metrics. They also have a small, unbiased validation set. Which course of action should the team take to satisfy regulatory requirements?

Accepted Answer

Implement adversarial debiasing using the unbiased validation set to enforce fairness constraints. Adversarial debiasing directly addresses the bias encoded in the model by training a predictor and an adversary simultaneously. The adversary tries to predict the protected attribute (e.g., zip code) from the model's predictions, while the predictor is penalized for allowing such inference, enforcing fairness constraints. Using the unbiased validation set ensures the debiasing process is guided by ground truth labels that are free from historical bias, allowing the model to retain high accuracy while reducing discrimination.

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Remove the zip code feature from the model and retrain

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Increase the weight of samples from disadvantaged zip codes in the training data

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Retrain the model using only the unbiased validation set