hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A team is deploying a model that requires…
A team is deploying a model that requires low-latency inference for real-time predictions. They are using a SageMaker endpoint with a single instance. During testing, they observe high latency. Which change would most effectively reduce latency?
⚠ Common exam trap
Candidates often confuse scaling up (larger instance) with scaling out (multiple instances) or assume that Elastic Inference always reduces latency, but Elastic Inference adds network latency and is better for cost savings on large models, not for minimizing per-request latency.
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
✓
Switch to a larger instance type
Switching to a larger instance type (Option D) directly increases the compute and memory resources available to the SageMaker endpoint, which reduces inference latency by allowing the model to process requests faster. Since the team is using a single instance, scaling up is the most straightforward way to handle the computational load and meet real-time latency requirements.
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 multi-model endpoint
Why it's wrong here
Multi-model endpoints may increase latency due to model loading overhead.
- ✗
Add Elastic Inference
Why it's wrong here
Elastic Inference adds GPU acceleration but may not reduce latency as effectively as upgrading the instance.
- ✗
Enable SageMaker Batch Transform
Why it's wrong here
Batch Transform is for asynchronous batch processing, not real-time.
- ✓
Switch to a larger instance type
Why this is correct
Correct: Larger instances provide more CPU/GPU for faster inferences.
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