easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data science team wants to deploy a real-time…
A data science team wants to deploy a real-time inference endpoint on Amazon SageMaker for a model that requires low latency (under 100 ms). The model is a small ensemble of three tree-based models, each about 50 MB. The team expects around 1000 requests per minute, with occasional spikes to 5000 requests per minute. Which instance type and deployment strategy would be MOST cost-effective while meeting the latency requirement?
⚠ Common exam trap
The trap here is that candidates might confuse provisioned concurrency (a Lambda concept) with SageMaker's scaling options, or incorrectly assume Multi-Model endpoints are suitable for ensemble models, leading to choosing B or D without considering the real-time latency constraint.
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
✓
Deploy a single model endpoint on an ml.c5.large instance with Auto Scaling configured using a target tracking policy based on invocations per minute
Deploying a single model endpoint on an ml.c5.large instance with Auto Scaling based on invocations per minute provides the necessary compute capacity for the expected 1000 requests per minute while scaling up to handle spikes up to 5000 requests per minute. The ml.c5.large instance offers sufficient memory (4 GB) and compute for three 50 MB tree-based models, and the target tracking policy ensures low latency by maintaining a buffer of capacity without over-provisioning, keeping inference under 100 ms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Deploy a single model endpoint on an ml.c5.large instance with Auto Scaling configured using a target tracking policy based on invocations per minute
Why this is correct
The ml.c5.large provides sufficient compute for the latency requirement, and Auto Scaling scales out during spikes. This is the most cost-effective approach.
- ✗
Deploy a single model endpoint on an ml.c5.large instance with a Multi-Model endpoint
Why it's wrong here
Multi-Model endpoints are for serving multiple models on the same instance; but the scenario has only one ensemble, so it adds unnecessary complexity without benefit.
- ✗
Use SageMaker batch transform with multiple ml.c5.large instances to process all requests offline
Why it's wrong here
Batch transform is for asynchronous, offline inference, not for real-time low latency needs.
- ✗
Deploy a single model endpoint on an ml.c5.xlarge instance with provisioned concurrency
Why it's wrong here
ml.c5.xlarge is over-provisioned; the cost is higher without need. Provisioned concurrency is not a SageMaker feature.
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