MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning engineer is deploying a model on SageMaker and needs to ensure that the endpoint can handle a sudden spike in traffic. The engineer expects traffic to increase by 10x during a promotional event. Which scaling strategy should be used?
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
✓
Use scheduled scaling to add instances before the event, combined with dynamic scaling for the remaining duration.
The correct answer. Scheduled scaling allows you to add instances before the expected traffic spike, ensuring capacity is ready when needed. Combined with dynamic scaling (e.g., based on CPU utilization or request count), you can handle unexpected additional load during the event. Option A is wrong because a single large instance is a single point of failure and may not provide sufficient throughput for a 10x spike. Option B is wrong because manual scaling requires human intervention and may not react fast enough. Option C is wrong because relying solely on dynamic scaling may not scale up quickly enough for a sudden 10x increase, as there is a lag in metrics and scaling actions.
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 a single large instance type instead of multiple smaller instances.
Why it's wrong here
A single instance is a single point of failure and may not scale.
- ✗
Manually increase the instance count before the event.
Why it's wrong here
Manual scaling is not automated and may be missed.
- ✗
Use only dynamic scaling based on the average latency metric.
Why it's wrong here
Dynamic scaling may have a lag and cannot pre-empt spikes.
- ✓
Use scheduled scaling to add instances before the event, combined with dynamic scaling for the remaining duration.
Why this is correct
Scheduled scaling pre-warms the endpoint to handle the spike.
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