MLS-C01 Modeling Practice Question
A company is deploying a machine learning model for real-time fraud detection. The model must have low latency (under 100 ms) and high throughput. The data scientist trains a gradient boosting model and deploys it to a SageMaker endpoint with a single ml.c5.xlarge instance. During load testing, the endpoint exceeds the latency threshold. Which change is MOST likely to reduce latency?
⚠ Common exam trap
Test-takers frequently assume scaling up instance size or adding automatic scaling will fix latency, but latency is a per-request metric that depends on model complexity, not just infrastructure parallelism or throughput.
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
✓
Replace the model with a simpler model, such as logistic regression
Replacing the gradient boosting model with a simpler model like logistic regression reduces the computational complexity per inference. Gradient boosting involves traversing many decision trees, each requiring multiple conditional checks and arithmetic operations, while logistic regression is a single linear transformation. This directly lowers CPU utilization per request, reducing latency under the same instance resources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Replace the model with a simpler model, such as logistic regression
Why this is correct
A simpler model has lower inference latency, meeting the 100 ms requirement.
- ✗
Use a larger instance type, such as ml.c5.4xlarge
Why it's wrong here
A larger instance may reduce latency but is not the most efficient solution; algorithmic optimization is better.
- ✗
Switch to batch transform for inference
Why it's wrong here
Batch transform is not suitable for real-time inference.
- ✗
Enable automatic scaling on the endpoint
Why it's wrong here
Automatic scaling adds instances to handle load but does not reduce per-request latency.
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