MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company needs to deploy a large language model (LLM) on SageMaker with the Triton Inference Server to maximize GPU utilization and reduce latency. They have an NVIDIA A100 GPU. Which SageMaker inference option supports Triton?
⚠ Common exam trap
Many exam-takers confuse SageMaker Batch Transform with real-time endpoints, assuming Triton can be used for batch processing, but Triton is specifically designed for real-time, low-latency inference and is not supported in Batch Transform jobs.
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
✓
SageMaker real-time endpoint using a Triton Inference Server container
SageMaker real-time endpoints support the Triton Inference Server through a pre-built container that integrates with NVIDIA A100 GPUs, enabling dynamic batching and concurrent model execution to maximize GPU utilization and reduce latency. Triton is designed for high-throughput inference on GPU hardware, making it the correct choice for this scenario.
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