A data science team receives a large set of customer transaction records with no labels indicating which customers belong to which segment and no flags marking which transactions are fraudulent. The team wants to use unsupervised machine learning to group customers into naturally occurring segments based on purchasing behavior, and separately to identify transactions that deviate sharply from typical patterns without being told in advance what fraud looks like. Which two techniques should the team apply? (Select TWO.)
Select all correct answers, then click Submit.
Short Explanation
Look closely at what the team actually has: a pile of transactions with no segment tags and no fraud flags attached anywhere. That absence of labels is the whole ballgame. When you want to find natural groupings in data that was never sorted for you, you reach for a technique that looks at similarity and clusters similar customers together on its own. When you want to catch the transactions that just don't look like the rest, without anyone ever telling the system what fraud looks like in advance, you reach for a technique built to learn what normal looks like and flag whatever strays far from it. Both of these goals share the same DNA: no answer key, just patterns waiting to be discovered. The trap here is reaching for techniques that sound related but actually require a labeled answer key to work, like sorting transactions into fraud or not-fraud buckets using examples someone already tagged, or predicting a specific dollar figure someone will spend, since forecasting a number was never even part of what this team was asked to do.
Full Explanation
The correct answers are A and B. Clustering is the unsupervised technique that groups customers into naturally occurring segments purely from similarity in their purchasing behavior, with no predefined segment labels required, which matches the team's first goal. Anomaly detection is the unsupervised technique that learns what typical transaction patterns look like and flags records that deviate sharply from that norm, without needing any transactions pre-labeled as fraudulent, which matches the team's second goal. Option C is incorrect because classification is a supervised technique that requires a training set of transactions already labeled fraud or not-fraud by analysts, and the scenario explicitly states no such fraud flags exist in the data. Option D is incorrect because regression is also a supervised technique used to predict a specific continuous numeric value, such as next month's spending, and it addresses a forecasting goal that was never mentioned in the scenario, rather than the segmentation or anomaly-detection goals actually described. Because both stated goals involve discovering structure in unlabeled data rather than predicting a known target, clustering and anomaly detection are the appropriate pair.