NCA-GENL Core Machine Learning and AI Knowledge Practice Question
A data science team is building a model to predict whether a customer will churn based on historical account activity. They have a large dataset with labeled outcomes (churned or not churned). Which type of machine learning is most appropriate for this task?
⚠ Common exam trap
The trap here is assuming that any large dataset requires unsupervised learning, but the presence of labeled outcomes clearly indicates a supervised classification problem.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Supervised learning with a classification algorithm
The task is to predict a binary outcome (churn or no churn) using historical data where the outcome is known. This is a classic supervised learning problem, and classification algorithms are designed to learn from labeled examples to predict discrete categories. Unsupervised, reinforcement, and semi-supervised methods do not directly leverage the available labels for prediction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Unsupervised learning with clustering
Why it's wrong here
Unsupervised learning is used when data has no labels, and the goal is to discover inherent structures such as groups or clusters. Here, the churn outcome is explicitly labeled, so clustering would ignore the known target and fail to directly predict churn. It is not the appropriate method for this supervised prediction task.
- ✗
Reinforcement learning with a reward function
Why it's wrong here
Reinforcement learning involves an agent interacting with an environment to maximize cumulative reward through trial and error. This scenario provides static historical data with labels, not a dynamic environment where actions lead to rewards. Therefore, reinforcement learning is not suitable for predicting customer churn from a fixed dataset.
- ✓
Supervised learning with a classification algorithm
Why this is correct
The dataset contains labeled examples where the outcome (churned or not churned) is known. Supervised learning uses these labels to learn a mapping from input features to the target class. Classification algorithms are specifically designed for discrete outcomes like churn, making this the correct approach for predicting a binary category.
- ✗
Semi-supervised learning with a small labeled set
Why it's wrong here
Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data. The scenario states the team has a large dataset with labeled outcomes, so there is no need to rely on unlabeled data. Using semi-supervised methods would unnecessarily complicate the approach when fully labeled data is available.
About these practice questions
This NCA-GENL question is part of Courseiva's 367-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCA-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCA-GENL exam.