Sheet 2 - AI & Cyber Security
📙 Sheet 2: AI & Cyber Security — Clustering & Distance Metrics
MCQ Answers
| # | Question | Answer |
|---|---|---|
| Q1 | Which distance metric is sensitive to outliers? | D) All of the above |
| Q2 | Most appropriate distance metric for text data? | B) Cosine distance |
| Q3 | Which distance metric is NOT symmetric? | D) Mahalanobis distance |
| Q4 | Can decision trees be used for clustering? | B) False |
| Q5 | Best data cleaning strategy before clustering with few data points? | A) 1 only — Capping and flooring of variables |
| Q6 | Minimum number of variables required to perform clustering? | B) 1 |
| Q7 | For two runs of K-Means, is the same result expected? | B) No |
| Q8 | Possible termination conditions in K-Means? | D) All of the above |
| Q9 | Possible reasons for producing two different dendrograms in agglomerative clustering? | E) All of the above |
| Q10 | Metrics for finding dissimilarity between clusters in hierarchical clustering? | D) 1, 2 and 3 (Single-link, Complete-link, Average-link) |
📝 Study sheets compiled for AI for Cyber Security 2026