AI For Beginners 5

Question 1

What is 'feature engineering' in a machine learning workflow?

Accepted Answer

The process of creating or transforming input variables to improve model performance

Answer

Feature engineering transforms raw data into informative representations that help models learn patterns more effectively.

Question 2

Which algorithm is best described as building an ensemble of decision trees trained on random data subsets?

Accepted Answer

Random Forest

Answer

Random Forest trains many decision trees on bootstrap samples and aggregates their predictions to reduce variance and improve accuracy.

Question 3

In AI model deployment, what is an API endpoint used for?

Accepted Answer

Exposing model inference capabilities as a network-accessible interface

Answer

An API endpoint accepts input data over a network, runs it through the model, and returns predictions to the calling application.

Question 4

What is 'gradient descent' designed to minimize?

Accepted Answer

A loss function measuring prediction error

Answer

Gradient descent iteratively moves model parameters in the direction that most steeply decreases the loss function.

Question 5

Which of the following best describes an AI 'pipeline'?

Accepted Answer

A sequence of data processing and modeling steps chained together end-to-end

Answer

An AI pipeline connects steps like data ingestion, preprocessing, model training, evaluation, and deployment into a reproducible workflow.

AI - Engineer Practice Test