MLS-C01 Modeling Practice Question
A data scientist uses SageMaker Autopilot to automatically build a binary classification model. The dataset has 50 features and 100,000 rows. After the experiment, Autopilot provides multiple candidate models. Which candidate should the data scientist select to minimize inference latency for real-time predictions?
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
✓
The model that is a linear learner
SageMaker Autopilot explores various algorithms including linear models, tree-based ensembles, and neural networks. For real-time inference with low latency, simpler models like linear or logistic regression or shallow decision trees are preferred. XGBoost with many trees or deep neural networks increase latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The model with the smallest memory footprint
Why it's wrong here
Memory footprint is important but not the only factor; model complexity affects latency.
- ✗
The model with the highest validation accuracy
Why it's wrong here
Highest accuracy may come from a complex model with higher latency.
- ✗
The model with the lowest validation loss
Why it's wrong here
Lowest loss doesn't guarantee low latency.
- ✓
The model that is a linear learner
Why this is correct
Linear models are fast for inference due to simple computations.
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