10+ ACG Worldwide Interview Questions and Answers

Developed a predictive model for customer churn using Random Forest algorithm.

• Used Python and scikit-learn library for model development

• Performed data cleaning, feature engineering, and exploratory data analysis

• Tuned hyperparameters using GridSearchCV and evaluated model performance using cross-validation

• Random Forest is an ensemble learning method that builds multiple decision trees and combines their predictions

• Other ML models include logistic regression, support vector mac...read more

Transformers are a type of neural network architecture that utilizes self-attention mechanisms to process sequential data.

• Transformers use self-attention mechanisms to weigh the importance of different input elements, allowing for parallel processing of sequences.

• Unlike RNNs and LSTMs, Transformers do not rely on sequential processing, making them more efficient for long-range dependencies.

• Transformers have been shown to outperform traditional RNNs and LSTMs in tasks such as ...read more

Different types of Attention include self-attention, global attention, and local attention.

• Self-attention focuses on relationships within the input sequence itself.

• Global attention considers the entire input sequence when making predictions.

• Local attention only attends to a subset of the input sequence at a time.

• Examples include Transformer's self-attention mechanism, Bahdanau attention, and Luong attention.

PCA is a dimensionality reduction technique that uses eigenvalues to find the principal components of a dataset.

• PCA is used to reduce the dimensionality of a dataset by transforming the data into a new coordinate system.

• Eigenvalues represent the amount of variance captured by each principal component.

• Higher eigenvalues indicate that the corresponding principal component explains more variance in the data.

• Eigenvalues are used to rank the importance of the principal components ...read more

System design tradeoffs involve balancing various factors to optimize performance and efficiency.

• Consider scalability, reliability, latency, and cost when designing systems

• Tradeoffs may involve sacrificing one aspect for the benefit of another

• Examples include choosing between consistency and availability in distributed systems

Data scientists analyze data to gain insights, machine learning (ML) involves algorithms that improve automatically through experience, and artificial intelligence (AI) refers to machines mimicking human cognitive functions.

• Data scientists analyze large amounts of data to uncover patterns and insights.

• Machine learning involves developing algorithms that improve automatically through experience.

• Artificial intelligence refers to machines performing tasks that typically require ...read more

GPT is a generative model while BERT is a transformer model for natural language processing.

• GPT is a generative model that predicts the next word in a sentence based on previous words.

• BERT is a transformer model that considers the context of a word by looking at the entire sentence.

• GPT is unidirectional, while BERT is bidirectional.

• GPT is better for text generation tasks, while BERT is better for understanding the context of words in a sentence.

Chatbots use natural language processing and machine learning to interact with users and provide automated responses.

• Chatbots use natural language processing (NLP) to understand and interpret user input.

• They use machine learning algorithms to learn from past interactions and improve responses.

• Chatbots can be rule-based, where responses are pre-programmed, or AI-based, where they learn and adapt over time.

• Examples include chatbots like Siri, Alexa, and customer service bots on...read more

Fractals are used in data science for analyzing complex and self-similar patterns.

• Fractals are useful for analyzing data with repeating patterns at different scales.

• They are used in image compression, signal processing, and financial market analysis.

• Fractal analysis can help in understanding the underlying structure of data and making predictions.