MLS-C01 Modeling Practice Question
A company is deploying a machine learning model for real-time fraud detection. The model must have extremely low latency (<10 ms) and high throughput. Which THREE design choices meet these requirements? (Choose 3.)
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that batch processing services like Batch Transform can be used for real-time inference, but the key distinction is that Batch Transform is designed for offline, asynchronous workloads and cannot meet low-latency requirements.
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 GPU instances (e.g., ml.p3) for the endpoint.
GPU instances like ml.p3 provide massively parallel compute capability that accelerates matrix operations common in deep learning models, enabling inference latencies under 10 ms. For real-time fraud detection, the GPU's high throughput and low latency are essential for processing thousands of transactions per second without bottlenecks.
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 GPU instances (e.g., ml.p3) for the endpoint.
Why this is correct
GPU accelerates inference, reducing latency.
- ✗
Use one endpoint per model to avoid interference.
Why it's wrong here
Multiple endpoints increase cost and may not improve latency.
- ✗
Use SageMaker Batch Transform for real-time predictions.
Why it's wrong here
Batch transform is for offline, not real-time.
- ✓
Use SageMaker multi-model endpoints to host multiple models on the same instance.
Why this is correct
Multi-model endpoints improve resource utilization and reduce latency for multiple models.
- ✓
Use SageMaker Elastic Inference to attach GPU acceleration to a CPU instance.
Why this is correct
Elastic Inference provides cost-effective GPU acceleration.
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