Machine Learning & Supervised Learning — NCA-GENL Practice Question
A representative NVIDIA Generative AI LLMs Associate (NCA-GENL) exam question on Machine Learning & Supervised Learning. Work through it below, then read why each option is right or wrong.
Short answer
The correct answer is C. Supervised learning, since the model is trained on labeled input-output pairs to predict categories.
Supervised learning is the correct paradigm when you have labeled training data and want to predict predefined categories. The team has 50,000 labeled tickets mapping to known categories, which is a classic supervised classification task. Unsupervised learning (A) would be used if they needed to discover unknown groupings without labels. Reinforcement learning (B) involves agent-environment interaction with rewards, not classification. Self-supervised learning (D) generates proxy labels from the data itself and is typically used for pre-training, not when explicit labels are available.
The Question
A data science team is building a model to classify customer support tickets into predefined categories such as "billing," "technical," and "account." They have a labeled dataset of 50,000 past tickets. Which machine learning paradigm is most appropriate for this task?
Why C is correct
Supervised learning is the correct paradigm when you have labeled training data and want to predict predefined categories. The team has 50,000 labeled tickets mapping to known categories, which is a classic supervised classification task. Unsupervised learning (A) would be used if they needed to discover unknown groupings without labels. Reinforcement learning (B) involves agent-environment interaction with rewards, not classification. Self-supervised learning (D) generates proxy labels from the data itself and is typically used for pre-training, not when explicit labels are available.
Why the other options are wrong
Option A does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (C) is the only one that fully meets every constraint stated in the question.
Option B does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (C) is the only one that fully meets every constraint stated in the question.
Option D does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (C) is the only one that fully meets every constraint stated in the question.
Key idea: Machine Learning & Supervised Learning
Supervised learning is the correct paradigm when you have labeled training data and want to predict predefined categories. The team has 50,000 labeled tickets mapping to known categories, which is a classic supervised classification task. Unsupervised learning (A) would be used if they needed to discover unknown groupings without labels. Reinforcement learning (B) involves agent-environment interaction with rewards, not classification. Self-supervised learning (D) generates proxy labels from the data itself and is typically used for pre-training, not when explicit labels are available. On the NCA-GENL exam, questions in the "Core ML and AI Knowledge" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.
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