AI0-001 AI Models and Data Engineering Practice Question
A logistics company uses a machine learning model to predict delivery times based on historical data including distance, traffic, weather, and driver performance. The model is deployed as a REST API using Flask and run on a single server. Recently, the model has been returning predictions with high latency (over 2 seconds) during peak hours when the API receives 500 requests per second. The server has 8 CPU cores and 32 GB RAM. The model is a gradient boosting model (XGBoost) with 500 trees. The engineer wants to reduce inference latency to under 500ms without retraining the model. Which action is most effective?
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
✓
Prune the model by reducing the number of trees to 100 and limit tree depth
Pruning the model by reducing the number of trees (e.g., from 500 to 100) directly reduces the number of decision paths evaluated during inference, which decreases latency. This does not require retraining; pruning can be applied post-training. Option B (linear regression) would require retraining and likely lose accuracy. Option C (horizontal scaling) improves throughput but does not reduce per-request latency; it may help if the server is overloaded, but pruning is more effective for latency. Option D (increasing RAM) does not address the CPU-bound tree traversal bottleneck.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Prune the model by reducing the number of trees to 100 and limit tree depth
Why this is correct
Pruning reduces computational load and latency while often maintaining adequate accuracy.
- ✗
Replace XGBoost with a linear regression model
Why it's wrong here
Linear regression is faster but would require retraining and likely significant accuracy loss.
- ✗
Scale horizontally by deploying additional servers behind a load balancer
Why it's wrong here
Horizontal scaling improves throughput but may not reduce latency for each request if the model itself is slow.
- ✗
Increase server RAM to 128 GB
Why it's wrong here
More RAM does not speed up CPU-bound model inference.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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