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

Question 1

A data scientist is building a model to predict whether a credit card transaction is fraudulent, using labeled historical data. Which machine learning paradigm is being used?

Accepted Answer

Supervised learning. Supervised learning uses labeled historical data to train a model that predicts a target variable. Here, the credit card transactions are labeled as fraudulent or not, so the model learns from these labels to classify new transactions.

Answer

Reinforcement learning

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Unsupervised learning

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Self-supervised learning

Question 2

A deep learning engineer is training a transformer model and notices that validation perplexity increases after a few epochs while training perplexity continues to decrease. Which of the following is the MOST likely cause?

Accepted Answer

The model is overfitting the training data. The described pattern—decreasing training perplexity alongside increasing validation perplexity—is the classic signature of overfitting. The model is memorizing the training data rather than learning generalizable patterns, causing its performance on unseen validation data to degrade after a certain point in training.

Answer

The temperature parameter is set too high

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The batch size is too small

Answer

The learning rate is too low

Question 3

A product team wants a system that can generate high-quality synthetic images of furniture in different room settings for an online catalog. The images must be photorealistic and vary in style. Which generative AI approach is BEST suited for this task?

Accepted Answer

Diffusion model. Diffusion models are the best choice because they iteratively denoise random noise to produce high-quality, photorealistic images with diverse styles. Unlike GANs, they avoid mode collapse and training instability, and they generate more detailed and varied outputs than VAEs, making them ideal for furniture catalog images in different room settings.

Answer

Variational autoencoder (VAE)

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Generative adversarial network (GAN)

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Recurrent neural network (RNN)

Question 4

An AI practitioner needs to measure the performance of a binary classification model for disease detection, where the cost of false negatives is very high. Which metric should be prioritized?

Accepted Answer

Recall. Recall (true positive rate) minimises false negatives, which is critical when missing a positive case is dangerous.

Answer

Precision

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F1-score

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Accuracy

Question 5

An AI engineer is designing a system to detect unusual patterns in network traffic that may indicate a security breach. The system should learn from normal traffic patterns and flag deviations. Which machine learning approach is MOST appropriate?

Accepted Answer

Unsupervised anomaly detection. Unsupervised anomaly detection is the most appropriate approach because the system must learn 'normal' traffic patterns from unlabeled data and then flag deviations without requiring pre-labeled examples of attacks. This aligns with the core requirement of detecting unknown or novel security breaches, which supervised methods cannot handle due to the lack of labeled attack data.

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Reinforcement learning with reward shaping

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Supervised classification using logistic regression

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Semi-supervised learning with a small labeled set

Question 6

A machine learning engineer is training a neural network for image classification. The training loss decreases slowly and the model accuracy improves only marginally each epoch. Which hyperparameter adjustment is MOST likely to accelerate convergence?

Accepted Answer

Increase the learning rate. The training loss decreasing slowly and accuracy improving marginally each epoch indicates that the learning rate is too small, causing the optimizer to take very small steps toward the minimum of the loss function. Increasing the learning rate allows the optimizer to take larger steps per update, which accelerates convergence. Option C is correct because adjusting the learning rate directly addresses the step size in gradient descent.

Answer

Add more hidden layers

Answer

Increase the batch size

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Decrease the number of epochs

Question 7

An AI system that can perform any intellectual task that a human being can is referred to as:

Accepted Answer

Artificial General Intelligence (AGI). Artificial General Intelligence (AGI) refers to a hypothetical AI system that possesses the ability to understand, learn, and apply intelligence across a wide range of tasks at a level comparable to a human being. It contrasts with narrow AI, which is specialized for specific tasks. The definition in the question matches AGI exactly.

Answer

Machine learning

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Narrow AI

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Deep learning

Question 8

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. Option E, a convolutional neural network (CNN), is correct because CNNs apply learned spatial filters over pixel grids and are the standard architecture for per-frame object detection and feature extraction in video surveillance, efficiently capturing spatial hierarchies in each image. Option D, an RNN or LSTM, is correct because recurrent architectures model temporal dependencies across sequential frames, allowing the system to learn movement patterns and object trajectories over time; LSTMs in particular mitigate vanishing gradients for longer sequences. Together, a CNN front end for spatial detection plus an RNN/LSTM back end for temporal tracking forms a classic video-analysis pipeline. Option A, a Transformer encoder only, is not the intended pairing here since it lacks the convolutional spatial inductive bias for frame-level detection and, used alone, does not provide the recurrent temporal modeling this scenario calls for. Option B, a GAN, is for generative adversarial training to synthesize or enhance data, not for detection and tracking. Option C, a VAE, is a generative model for learning latent representations and reconstruction, not a supervised detector-tracker component.

Answer

Transformer encoder only

Answer

Generative adversarial network (GAN)

Answer

Variational autoencoder (VAE)

Question 9

A healthcare startup is building a diagnostic support system using a large language model. The system must provide accurate, evidence-based answers and avoid generating harmful or fabricated information. Which THREE techniques should be implemented to achieve this? (Choose 3)

Accepted Answer

Retrieval-Augmented Generation (RAG). Option A (Retrieval-Augmented Generation, RAG) is correct because grounding the LLM's responses in an external, authoritative medical knowledge base (e.g., PubMed, clinical guidelines) at inference time supplies verifiable evidence and sharply reduces hallucination compared to relying on parametric memory alone. Option C (chain-of-thought prompting) is correct because eliciting intermediate reasoning steps improves the model's accuracy on complex diagnostic questions and makes its conclusions auditable, which supports evidence-based clinical decision support. Option E (fine-tuning on medical textbooks and guidelines) is correct because domain-specific supervised fine-tuning adapts the model's weights to accurate, curated medical content and terminology, raising factual reliability for the healthcare domain. Option B does not belong because disabling output filtering removes safety guardrails and increases the risk of harmful content, the opposite of the stated requirement. Option D does not belong because raising the temperature increases sampling randomness and creativity, which promotes fabricated or inconsistent answers rather than accurate, evidence-based ones.

Answer

Disabling output filtering to speed up generation

Answer

Increasing the temperature parameter to encourage creativity

Question 10

A machine learning team is splitting a dataset for a binary classification problem. They want to ensure robust evaluation and avoid data leakage. Which TWO practices should they follow? (Choose 2)

Accepted Answer

Split into training, validation, and test sets. Option B is correct because splitting the dataset into separate training, validation, and test sets allows the model to be tuned on the validation set while the test set remains untouched for an unbiased final evaluation, which is essential for robust assessment of a binary classifier. Option D is correct because shuffling the data before splitting ensures that the training, validation, and test sets are representative of the overall distribution and prevents ordering bias or temporal artifacts from skewing the split, which helps avoid leakage when the data is not inherently ordered. Option A is not correct because normalising the entire dataset before splitting leaks information from the validation and test sets into the training process, since the scaling parameters would be computed using all data. Option C is not correct because including validation data in the training set removes the independent validation signal needed for hyperparameter tuning and model selection, undermining robust evaluation. Option E is not correct because reusing the same split for all experiments can lead to overfitting to that particular split and does not provide the variability needed to assess generalization reliably.

Answer

Normalise the entire dataset before splitting

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Include validation data in the training set for more data

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Use the same split for all experiments

Question 11

A data scientist is building a model to predict credit default using historical loan data. The dataset contains 100,000 records with 50 features, including income, debt-to-income ratio, and loan amount. The target variable is binary (default vs. no default). The goal is to maximize interpretability while maintaining high accuracy. Which algorithm is MOST appropriate?

Accepted Answer

Logistic regression. Logistic regression is a linear model that is highly interpretable (coefficients indicate direction and magnitude of feature impact) and performs well on binary classification with many features, especially when the goal is to balance interpretability with accuracy. It avoids the overfitting risk of a single decision tree and the black-box nature of ensembles.

Answer

Random forest

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Gradient boosting machine

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Decision tree

Question 12

A team is training a deep learning model for image classification. The training loss decreases steadily but the validation loss plateaus after 20 epochs and then starts to increase. Which action is MOST likely to improve generalization?

Accepted Answer

Implement early stopping. Early stopping halts training when validation loss stops improving, preventing overfitting. Increasing learning rate would worsen divergence; adding more layers increases capacity and overfitting; reducing batch size may help optimization but not directly address overfitting.

Answer

Add more convolutional layers

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Increase the learning rate

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Reduce the batch size

Question 13

Which machine learning paradigm is best suited for training a model to play a game by learning from its own actions and rewards, without labeled data?

Accepted Answer

Reinforcement learning. Reinforcement learning is designed for agents that learn by interacting with an environment and receiving rewards or penalties, with no labeled dataset required. The model improves its policy through trial and error, maximizing cumulative reward over time — exactly the paradigm for game-playing agents like AlphaGo and DQN.

Answer

Unsupervised learning

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Semi-supervised learning

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Supervised learning

Question 14

A company is deploying a text generation model for customer service emails. They want to ensure the model's responses are factual and based on internal knowledge bases. Which technique is most effective?

Accepted Answer

Use Retrieval-Augmented Generation (RAG). Retrieval-Augmented Generation (RAG) is the most effective technique because it combines a pre-trained language model with a retrieval system that fetches relevant documents from an internal knowledge base at inference time. This ensures the model's responses are grounded in factual, up-to-date information from the knowledge base, reducing hallucinations.

Answer

Fine-tune the model on historical customer service emails

Answer

Write a detailed system prompt

Answer

Set the temperature to 0

Question 15

Which neural network architecture is specifically designed to handle sequential data and mitigate the vanishing gradient problem?

Accepted Answer

Long Short-Term Memory (LSTM) network. LSTM (Long Short-Term Memory) is a type of RNN designed with gating mechanisms to prevent vanishing gradients in long sequences. CNNs are for spatial data; vanilla RNNs suffer from vanishing gradients; transformers use attention but are not specifically designed to mitigate vanishing gradients (they use residual connections).

Answer

Convolutional Neural Network (CNN)

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Transformer

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Vanilla Recurrent Neural Network (RNN)