MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to deploy a real-time inference endpoint for a computer vision model. The endpoint receives bursts of traffic with up to 500 requests per second, but the load is unpredictable. Which scaling strategy is MOST cost-effective while maintaining low latency?
⚠ Common exam trap
Many candidates confuse provisioned concurrency (Option B) as a cost-effective burst solution, but it is actually designed for serverless functions with predictable traffic and incurs costs for idle capacity, making it unsuitable for high-throughput, unpredictable bursts.
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
✓
Configure automatic scaling with a target tracking policy and add a buffer to handle bursts
Amazon SageMaker's automatic scaling with a target tracking policy dynamically adjusts the number of instances based on a target metric (e.g., InvocationsPerInstance), which handles unpredictable bursts cost-effectively. Adding a buffer (e.g., a higher target value or a cooldown period) ensures low latency by pre-scaling before traffic spikes, avoiding cold starts and over-provisioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually provision enough instances to handle peak load
Why it's wrong here
Manual provisioning wastes resources during low traffic.
- ✗
Use provisioned concurrency on SageMaker Serverless Inference
Why it's wrong here
SageMaker Serverless Inference does not support provisioned concurrency; it scales automatically but may have cold starts.
- ✗
Use a multi-model endpoint to reduce the number of instances
Why it's wrong here
Multi-model endpoints help with memory utilization but do not solve unpredictable bursts.
- ✓
Configure automatic scaling with a target tracking policy and add a buffer to handle bursts
Why this is correct
Autoscaling with a target tracking policy adjusts instances based on demand, and a buffer helps absorb sudden spikes.
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Senior Network & Security Engineer · founder of Courseiva
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