hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company operates an e-commerce platform that…
A company operates an e-commerce platform that uses a machine learning model to recommend products to users. The model is deployed on an Amazon SageMaker endpoint with automatic scaling enabled based on average CPU utilization. The model was trained on historical data and is updated weekly. Recently, the platform experienced a flash sale event that caused a sudden spike in traffic. During the event, the endpoint's latency increased dramatically, and many requests timed out. After the event, the team reviews the CloudWatch metrics and notices that the CPU utilization never exceeded 70%, and the scaling policy was triggered but instances took several minutes to become available. The team wants to prevent similar issues in future flash sales. Which course of action would be MOST effective?
⚠ Common exam trap
A common mix-up: candidates assume reactive scaling (lowering thresholds or using predictive scaling) can handle sudden spikes, but the exam tests your understanding that provisioning latency is the bottleneck, and only proactive scheduled scaling can eliminate that delay for known events.
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
✓
Implement scheduled scaling to add capacity ahead of known flash sales.
Scheduled scaling allows you to proactively add capacity ahead of known traffic events like flash sales, eliminating the cold-start delay that occurs when reactive scaling policies (like those based on CPU utilization) must launch new instances. During the flash sale, the scaling policy was triggered but instances took minutes to become available, causing timeouts; scheduled scaling pre-warms the endpoint by adjusting the desired instance count before the traffic spike hits.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use predictive scaling based on historical traffic patterns.
Why it's wrong here
Predictive scaling requires long-term data and may not capture flash sales accurately.
- ✗
Lower the CPU utilization threshold for the scaling policy to 40%.
Why it's wrong here
Lowering threshold might cause premature scaling but does not solve the delay in instance provisioning.
- ✗
Switch to larger instance types to handle higher CPU loads.
Why it's wrong here
Larger instances may still have provisioning delays and increase cost.
- ✓
Implement scheduled scaling to add capacity ahead of known flash sales.
Why this is correct
Scheduled scaling pre-warms instances, avoiding cold start delays.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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