MLS-C01 Practice Question: Machine Learning Implementation and Operations
A team is deploying a machine learning model to production using Amazon SageMaker. They want to automatically scale the endpoint based on the incoming request volume, and they also need to ensure that the endpoint can handle sudden bursts of traffic without dropping requests. Which scaling policy should they use?
⚠ Common exam trap
Candidates often confuse 'target tracking' with 'scheduled scaling' or 'simple scaling', assuming any scaling policy works for bursts, but only target tracking (or step scaling with proper alarms) can dynamically adjust to sudden, unpredictable spikes without dropping requests.
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
✓
Target tracking scaling policy based on the number of invocations
A target tracking scaling policy based on the number of invocations allows the endpoint to automatically adjust the number of instances to maintain a target metric value (e.g., invocations per instance). This policy can proactively scale out to handle sudden bursts by adding instances before the request queue grows, preventing dropped requests. SageMaker's built-in scaling metric, 'SageMakerVariantInvocationsPerInstance', is ideal for this use case as it directly correlates with traffic volume.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Scheduled scaling policy for peak hours
Why it's wrong here
Scheduled scaling is for predictable traffic, not for sudden bursts.
- ✓
Target tracking scaling policy based on the number of invocations
Why this is correct
Target tracking automatically adjusts capacity to maintain a target metric and can handle bursts.
- ✗
Simple scaling policy based on average latency
Why it's wrong here
Simple scaling is reactive and may not respond quickly to bursts.
- ✗
Manual scaling by monitoring CloudWatch alarms
Why it's wrong here
Manual scaling requires human intervention and is not automatic.
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