MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company operates a real-time fraud detection system using an Amazon SageMaker endpoint. The model is a gradient boosting model trained on historical transaction data. The endpoint is deployed on an ml.c5.2xlarge instance with auto-scaling enabled based on average latency. Recently, during a flash sale event, the endpoint started returning HTTP 503 errors. The CloudWatch metrics show that the CPU utilization is at 70%, and the average latency has increased from 50 ms to 200 ms. The auto-scaling policy is configured to add one instance when average latency exceeds 100 ms for 5 consecutive minutes, and remove one instance when latency drops below 50 ms for 5 minutes. The current number of instances is 2. The flash sale lasted 30 minutes. What should the company do to prevent this issue in future flash sales?
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
✓
Pre-warm the endpoint by setting a minimum number of instances that can handle the expected peak load before the flash sale
The auto-scaling policy is reactive and requires a 5-minute evaluation period, which is too slow to handle the rapid traffic spike during a flash sale. Pre-warming the endpoint by setting a minimum number of instances to handle the expected peak load ensures capacity is available immediately. Option A (request throttling) would reject excess requests and cause errors, not prevent them. Option B (changing instance type) may help handle more load per instance but still suffers from the same reactive scaling delay, and it may be more expensive. Option D (simpler model) could reduce latency but may compromise model accuracy and does not address the scaling issue directly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable request throttling to drop excess requests
Why it's wrong here
Throttling still results in errors for dropped requests.
- ✗
Change the instance type to ml.c5.4xlarge to handle higher load
Why it's wrong here
This increases capacity but still relies on reactive auto-scaling during the event.
- ✓
Pre-warm the endpoint by setting a minimum number of instances that can handle the expected peak load before the flash sale
Why this is correct
This ensures capacity is available from the start.
- ✗
Change the model to a simpler model with lower latency
Why it's wrong here
Model complexity is not the cause; the issue is scaling speed.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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