AIF-C01 Fundamentals of AI and ML Practice Question
A company is deploying a machine learning model for real-time fraud detection. The model must make predictions with latency under 10 milliseconds. The data scientist trained a gradient boosting model that achieves high accuracy but has inference latency of 50 milliseconds. The team has access to a larger instance type with more CPU cores. Which approach should the data scientist take to reduce inference latency while maintaining accuracy?
⚠ Common exam trap
AWS often tests the misconception that model optimization (pruning or depth reduction) is the only way to reduce latency, ignoring that hardware scaling (more CPU cores) can meet latency requirements without sacrificing accuracy.
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
✓
Deploy the model on a larger instance with more CPU cores.
Increasing the number of CPU cores allows the gradient boosting model to parallelize tree evaluation across multiple cores, reducing inference latency. Since the model is already trained and accurate, this hardware scaling directly addresses the 50 ms bottleneck without altering the model's structure or accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to batch inference and run predictions every 100 milliseconds.
Why it's wrong here
Batch inference increases latency and is not suitable for real-time fraud detection.
- ✓
Deploy the model on a larger instance with more CPU cores.
Why this is correct
More CPU cores allow parallel computation, reducing inference latency without changing the model.
- ✗
Reduce the maximum tree depth and retrain the model.
Why it's wrong here
Reducing tree depth may reduce latency but can also decrease model accuracy.
- ✗
Apply post-training pruning to remove redundant trees.
Why it's wrong here
Pruning reduces model size but may harm accuracy and is not the best way to reduce latency for a single prediction.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.