AI Concepts and Techniques

A machine learning engineer is training a transformer model for machine translation. The model's perplexity on the validation set is 8.5, and the BLEU score is 32. After increasing the number of encoder layers from 6 to 12, perplexity drops to 7.2 but BLEU decreases to 28. What is the MOST likely cause?

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?

A company wants to recommend products to users based on their past purchase history. Which machine learning paradigm is BEST suited for this task?

A developer is using a large language model via an API. They want the model to solve a math problem step by step. Which prompt engineering technique should they use?

A company wants to deploy an LLM-based chatbot that can handle sensitive customer information. Which THREE measures should be implemented to mitigate prompt injection attacks? (Choose 3)

An organization's AI system uses a decision tree model for loan approval. The compliance team requires explanations for each decision. Which property of decision trees makes them suitable for this requirement?

In unsupervised learning, which task involves grouping similar data points together based on feature similarities?

An AI engineer is fine-tuning a transformer-based language model for a domain-specific task. They want to improve the model's factual accuracy and reduce hallucinations. Which THREE strategies should they consider? (Select THREE)

A team is training a recurrent neural network (RNN) with LSTM units to predict stock prices. The validation loss is significantly higher than the training loss. Which action is MOST likely to reduce the gap?

A data scientist is using a linear regression model to predict house prices and observes that the model performs well on training data but poorly on test data. Which regularisation technique is MOST appropriate to reduce overfitting?

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?

A team is developing a sentiment analysis model and obtains the following performance on the test set: accuracy=0.92, precision=0.75, recall=0.80, F1=0.77. The baseline majority-class classifier achieves 0.85 accuracy. Which conclusion is MOST justified?

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?

A bank wants to detect fraudulent transactions in real-time. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraud). Which evaluation metric is MOST appropriate for model performance?

A data scientist is selecting a model for a binary classification task where interpretability is critical because of regulatory requirements. The dataset has 20 features and 10,000 samples. Which model is MOST appropriate?

A developer is using a pre-trained BERT model for a question-answering system. They want to ensure the model can handle out-of-vocabulary words. Which component of the BERT architecture is responsible for this?

A data analyst wants to use a model that provides feature importance scores to understand which factors most influence customer churn. They also need the model to handle both numerical and categorical data with minimal preprocessing. Which algorithm is BEST suited?

A developer is building a natural language processing system to classify customer reviews as positive, neutral, or negative. They have 50,000 labeled reviews. Which model architecture is MOST appropriate for this task?

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?

A generative AI model produces images from text prompts. The outputs are often blurry and lack fine details. Which model type is MOST likely being used, and which improvement would best address this issue?

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

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)

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?

A company is deploying a chatbot using a large language model. They want to mitigate the risk of prompt injection attacks. Which TWO measures should be implemented?

A natural language processing team wants to build a sentiment analysis model for customer reviews. They have 10,000 labeled reviews and 1 million unlabeled reviews. Which approach would MOST effectively leverage the unlabeled data?

A company is deploying an LLM-powered application that answers questions based on internal documents. They want to minimize prompt injection attacks where users trick the model into ignoring instructions. Which THREE measures should they implement? (Select THREE)

A team is fine-tuning a BERT model for a document classification task. They notice the model achieves high F1 scores on the training set but low F1 on the validation set. Which regularization technique would be MOST effective?

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?

A company wants to generate realistic images of new product designs. They have a large dataset of existing product images. Which generative AI approach is MOST suitable for creating novel, high-quality images?

A data scientist is evaluating a binary classification model. The model achieves 95% accuracy on the test set, but the precision is 0.60 and recall is 0.55. The dataset has 90% negative class samples. Which metric should the team focus on to improve the model?

An AI practitioner needs to extract key phrases from a large collection of customer support emails for trend analysis. Which technique is MOST suitable?

A developer is fine-tuning a large language model for a legal document summarization task. They notice that during training, the loss decreases rapidly in the first few epochs but then plateaus with high variance. Which hyperparameter adjustment is MOST likely to help stabilize training?

A data scientist is building a model to predict whether a loan application will default. The dataset has 10,000 labeled examples with 1,000 defaults. Which metric is MOST appropriate for evaluating this highly imbalanced binary classification?

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)

A company wants to build a system that can generate new product images for an online catalog. Which TWO generative AI approaches are most suitable?

A team is deploying a sentiment analysis model for social media posts. The model currently performs well on English text but poorly on code-switched text (e.g., Spanglish). Which approach is MOST effective for improving performance on code-switched data without starting from scratch?

A company wants to use machine learning to recommend products to customers based on their purchase history. Which TWO techniques are appropriate for this task? (Select TWO)

A research team is training a deep learning model for image classification using a small dataset of 1,000 labeled images. They are concerned about overfitting. Which combination of regularisation techniques would be MOST effective?

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?

A team is using a pre-trained BERT model for a sentiment analysis task on product reviews. They want to adapt it to their specific domain with limited labeled data. Which approach is MOST effective?

A data scientist is building a recommendation system for an e-commerce platform. The dataset includes user purchase history, product descriptions, and user demographics. The goal is to recommend products that a user is likely to purchase. Which TWO techniques are most appropriate for this task? (Select TWO.)

Which of the following best describes the difference between narrow AI and general AI?

A team is training a generative adversarial network (GAN) to generate realistic images of furniture. The generator loss decreases sharply while the discriminator loss increases. What is the MOST likely issue and recommended action?

A machine learning team is evaluating a logistic regression model for a binary classification task. The dataset has 1,000 samples and 20 features. Which TWO metrics are most appropriate for evaluating model performance? (Choose 2)

A team is deploying a sentiment analysis model that must achieve high precision and high recall. They have a labeled dataset of 10,000 samples. They want to minimize overfitting. Which THREE actions are most appropriate? (Select THREE.)

A company is deploying a large language model for customer support. They want to reduce the number of off-topic or nonsensical responses while maintaining creativity. Which parameter adjustment would BEST achieve this?

A data scientist needs to select a regression model to predict house prices. The dataset contains many features, some of which are irrelevant. Which TWO algorithms are BEST suited for this scenario, and why? (Select TWO)

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)

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?

A model trained on customer reviews achieves 98% accuracy on the test set. However, when deployed, it performs poorly on real-world data. The data scientist suspects distribution shift. Which action is MOST important to address this?

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?

A startup wants to identify unusual patterns in network traffic to detect potential security breaches. They have a large dataset of normal traffic but very few labeled attacks. Which machine learning approach is MOST suitable?

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

Which type of neural network is BEST suited for processing sequential data such as time series or natural language?

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