2

Artificial Intelligence

Unit 2: Advanced Concepts of Modeling in AI

253+ practice questions available

Sample Practice Questions

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1
Easy

What does 'FN' stand for in model evaluation?

AFull Network
BFeature Normalisation
CFalse Negative — a positive case that the model incorrectly predicted as negative
DFinal Node
2
Easy

What is meant by 'model accuracy'?

AThe physical precision of the AI hardware
BThe proportion of total predictions that are correct
CThe speed at which the model makes predictions
DThe size of the model's file
3
Medium

Which of the following is the correct order of the AI Project Cycle stages?

ADeployment → Data Collection → Problem Scoping → Modelling → Evaluation
BProblem Scoping → Data Collection → Data Exploration → Data Preparation → Model Building → Model Evaluation → Deployment
CModel Building → Data Collection → Problem Scoping → Deployment → Evaluation
DData Collection → Problem Scoping → Evaluation → Deployment → Modelling
4
Hard

What is the 'Simpson's Paradox' and how can it affect AI model evaluation?

AA paradox where the model achieves perfect accuracy
BA statistical phenomenon where a trend appears in several sub-groups of data but disappears or reverses when the groups are combined — an aggregate metric can hide group-level disparities in model evaluation
CA paradox in which the model runs faster than expected
DA paradox discovered by Homer Simpson in data science
5
Hard

What is the key property of CONFORMAL PREDICTION that distinguishes it from standard model prediction?

AIt trains the model using a conformity score to maximise accuracy on the training set
BIt provides statistically valid prediction intervals or sets with a guaranteed coverage probability, without assuming a specific data distribution
CIt is a technique for combining multiple models into an ensemble with a confidence threshold
DIt adjusts model probabilities using Platt scaling to improve calibration

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