MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is deploying a machine learning model using SageMaker. The model is a PyTorch model that requires GPU for inference. The company wants to minimize costs while ensuring low latency. Which instance type should be used for the SageMaker endpoint?
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
✓
ml.g4dn.xlarge
Ml.g4dn.xlarge is a GPU instance with an NVIDIA T4, optimized for inference, and is more cost-effective than ml.p3.2xlarge while still providing low latency for PyTorch models. Option A (ml.m5.large) is wrong as it is a CPU instance without GPU support. Option B (ml.p3.2xlarge) is wrong because although it has a GPU, it is more expensive and not necessary for low-latency inference; ml.g4dn.xlarge offers similar performance at lower cost. Option C (ml.c5.2xlarge) is also a CPU instance and unsuitable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ml.m5.large
Why it's wrong here
Incorrect. ml.m5.large is a CPU-only instance and cannot run GPU-required inference for PyTorch models.
- ✗
ml.p3.2xlarge
Why it's wrong here
Incorrect. While ml.p3.2xlarge has a GPU, it is more expensive than ml.g4dn.xlarge and does not provide additional benefit for low-latency inference in this context.
- ✗
ml.c5.2xlarge
Why it's wrong here
Incorrect. ml.c5.2xlarge is a CPU instance and lacks GPU support, making it unsuitable for PyTorch inference requiring GPU.
- ✓
ml.g4dn.xlarge
Why this is correct
Correct. ml.g4dn.xlarge provides a cost-effective GPU instance (NVIDIA T4) suitable for PyTorch inference with low latency, balancing cost and performance.
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