hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Deploying a real-time inference endpoint for a…
A company is deploying a real-time inference endpoint for a natural language processing model using Amazon SageMaker. The model requires GPU acceleration and must handle variable traffic patterns, including sudden spikes. The team wants to minimize costs while maintaining low latency during spikes. Which endpoint configuration strategy should they use?
⚠ Common exam trap
Test-takers frequently assume serverless endpoints support GPU acceleration, but SageMaker serverless endpoints are CPU-only, making Option B invalid despite its cost-saving appeal.
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 multi-model endpoint on a GPU instance with Auto Scaling based on invocation count.
A multi-model endpoint on a GPU instance with Auto Scaling based on invocation count allows multiple models to share a single GPU, maximizing utilization and reducing cost. Auto Scaling based on invocation count dynamically adjusts the number of instances to handle traffic spikes while maintaining low latency, as it scales out quickly when the invocation count exceeds a threshold.
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 GPU instance with provisioned concurrency.
Why it's wrong here
Provisioned concurrency keeps resources warm but is expensive for variable traffic.
- ✗
Use a serverless endpoint with GPU support.
Why it's wrong here
SageMaker serverless inference does not support GPU instances.
- ✗
Use a single GPU instance in multiple Availability Zones with an Application Load Balancer.
Why it's wrong here
Multi-AZ improves availability but does not optimize cost for variable traffic.
- ✓
Use a multi-model endpoint on a GPU instance with Auto Scaling based on invocation count.
Why this is correct
Multi-model endpoints share instances across models, and Auto Scaling adjusts capacity for spikes.
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