MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using SageMaker to host a model that performs real-time fraud detection. The model receives high request volumes with occasional spikes. The company wants to ensure that the endpoint can handle spikes without throttling while minimizing cost. Which scaling strategy should be used?
⚠ Common exam trap
Watch out — candidates often confuse simple scaling (step adjustments) with target tracking, assuming any metric-based policy works, but target tracking is specifically designed for maintaining a utilization target and is the only option that handles irregular spikes without manual or scheduled intervention.
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 a target tracking scaling policy with a target value of 70% for the SageMakerVariantInvocationsPerInstance metric.
A target tracking scaling policy with the SageMakerVariantInvocationsPerInstance metric is the correct choice because it automatically adjusts the instance count to maintain a target utilization (e.g., 70%), handling spikes without manual intervention while minimizing cost by scaling down during low traffic. This is the recommended approach for real-time endpoints with variable traffic, as it aligns with AWS best practices for dynamic scaling.
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 target tracking scaling policy with a target value of 70% for the SageMakerVariantInvocationsPerInstance metric.
Why this is correct
Automatically scales based on utilization.
- ✗
Use a simple scaling policy with a step adjustment based on the InvocationsPerInstance metric.
Why it's wrong here
May not react quickly to spikes.
- ✗
Manually adjust the instance count based on monitoring dashboards.
Why it's wrong here
Not automated, may miss spikes.
- ✗
Use a scheduled scaling action to add instances during peak hours.
Why it's wrong here
Spikes are unpredictable.
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