AI0-001 AI Concepts and Foundations Practice Test 3 — 15 Questions

Question 1

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 2

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. 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 3

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 4

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 5

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 6

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

Accepted Answer

It uses data without labeled responses. 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 7

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 8

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

Question 9

A retail company wants to build a model to predict customer churn based on purchase history and demographics. The dataset includes categorical features like region and gender, and numerical features like total spend. What is the best initial step before training the model?

Accepted Answer

One-hot encode categorical variables and normalize numerical variables. Categorical features like region and gender must be converted to numeric form via one-hot encoding (or similar) because most ML algorithms cannot consume raw strings. Numerical features like total spend should be normalized (e.g., min-max or z-score) so that features on different scales contribute equally during training. This preprocessing step is the correct initial action before model training.

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Train a deep neural network directly on raw data

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Remove all categorical features to simplify the model

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Perform principal component analysis (PCA) on all features

Question 10

A data scientist trains a deep neural network for image classification. The training loss decreases but validation loss starts increasing after 50 epochs. What should the data scientist do to improve generalization?

Accepted Answer

Apply dropout and early stopping. The increasing validation loss while training loss decreases is a classic sign of overfitting. Dropout randomly deactivates neurons during training, which prevents co-adaptation and forces the network to learn more robust features. Early stopping halts training when validation performance stops improving, directly addressing the overfitting by selecting the model with the best generalization before it degrades.

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Decrease batch size

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Add more hidden layers

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Increase learning rate

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.

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Mean squared error

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Accuracy

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R-squared

Question 12

A self-driving car company is developing an object detection system using a convolutional neural network (CNN). The system needs to detect pedestrians and vehicles in real-time with high accuracy. Which technique can reduce inference time while maintaining accuracy?

Accepted Answer

Apply model pruning and quantization. Model pruning removes redundant or less important weights from the CNN, reducing computational load, while quantization converts floating-point weights to lower-precision integers (e.g., INT8). Together, they shrink model size and speed up inference without significantly degrading accuracy, making them ideal for real-time object detection in resource-constrained environments like autonomous vehicles.

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Use a pre-trained model and fine-tune it

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Add more convolutional layers

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Increase number of filters in each layer

Question 13

A marketing team wants to segment customers into groups based on purchasing behavior without predefined categories. Which algorithm should they use?

Accepted Answer

K-means clustering. K-means clustering is an unsupervised learning algorithm that groups data points into clusters based on similarity without requiring predefined labels. Since the marketing team wants to segment customers based on purchasing behavior without predefined categories, K-means is the correct choice as it discovers natural groupings in the data.

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Naive Bayes classifier

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Logistic regression

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Support vector machine

Question 14

An AI model is being developed for medical diagnosis from X-ray images. The dataset contains only frontal chest X-rays. The model achieves high accuracy on test set but fails on lateral views. What is the most likely cause?

Accepted Answer

Dataset bias. The model was trained exclusively on frontal chest X-rays, so it never learned features specific to lateral views. When tested on lateral views, the distribution shift causes poor performance, which is a classic case of dataset bias (sampling bias). The high accuracy on the test set is misleading because the test set also only contained frontal views, masking the model's inability to generalize to other X-ray orientations.

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Underfitting

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Label noise

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Overfitting

Question 15

A team is training a deep learning model for natural language processing using a large corpus. They notice the model has a very high number of parameters and training is slow. Which technique can reduce the number of parameters without significant performance loss?

Accepted Answer

Apply embedding compression. Embedding compression reduces the dimensionality of the embedding layer, which often contains the majority of the model's parameters in NLP tasks. By using techniques like low-rank factorization or pruning, the model retains most of its representational power while significantly decreasing the parameter count and training time.

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Add more dropout layers

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Use a larger batch size

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Increase learning rate