MLS-C01 Practice Question: Machine Learning Implementation and Operations
Which TWO approaches can reduce inference latency on a SageMaker real-time endpoint? (Choose 2.)
⚠ Common exam trap
Watch out — candidates often confuse 'reducing latency' with 'increasing throughput' — choosing larger batch sizes or GPU instances, which improve throughput but can increase per-request latency due to batching delays and GPU context switching.
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
✓
Attach an Elastic Inference accelerator
Elastic Inference (EI) accelerators attach a dedicated, low-cost FPGA-based inference accelerator to a SageMaker endpoint, offloading matrix operations from the CPU. This reduces inference latency by accelerating the compute-intensive forward pass of deep learning models without requiring a full GPU instance, making it ideal for real-time, low-latency predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Attach an Elastic Inference accelerator
Why this is correct
Provides GPU acceleration at lower cost.
- ✗
Increase the batch size
Why it's wrong here
Larger batch size increases processing time.
- ✗
Enable SageMaker Model Monitor
Why it's wrong here
Adds overhead, not reduces latency.
- ✗
Use a GPU instance type
Why it's wrong here
GPU may not reduce latency for small requests.
- ✓
Compile the model using SageMaker Neo
Why this is correct
Optimizes model for inference.
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