AIF-C01 Fundamentals of AI and ML Practice Question
A data scientist at a retail company is tasked with building a model to predict customer churn. The dataset contains 100,000 records with features such as age, purchase history, customer support interactions, and a binary label indicating whether the customer churned in the past. The team needs a model that can be deployed for real-time inference with low latency. They have limited time and want to use a built-in algorithm from Amazon SageMaker that is optimized for classification tasks. Which approach should they take?
⚠ Common exam trap
It's easy for candidates to confuse unsupervised algorithms (PCA, K-Means) or domain-specific algorithms (BlazingText for text) with general-purpose supervised classification algorithms, overlooking that XGBoost is the only built-in SageMaker algorithm among the options designed for tabular classification with real-time inference needs.
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
✓
Use Amazon SageMaker XGBoost algorithm
Amazon SageMaker's built-in XGBoost algorithm is optimized for classification tasks like binary churn prediction, supports real-time inference with low latency via SageMaker endpoints, and can handle the dataset size of 100,000 records efficiently. It is a supervised learning algorithm that directly uses the binary label for training, making it the correct choice for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon SageMaker PCA algorithm
Why it's wrong here
PCA is for dimensionality reduction, not for building a classification model.
- ✓
Use Amazon SageMaker XGBoost algorithm
Why this is correct
XGBoost is a built-in algorithm for classification and works well with tabular data.
- ✗
Use Amazon SageMaker K-Means algorithm
Why it's wrong here
K-Means is an unsupervised clustering algorithm, not for supervised classification.
- ✗
Use Amazon SageMaker BlazingText algorithm
Why it's wrong here
BlazingText is designed for text data, not tabular customer churn data.
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