MLS-C01 Modeling Practice Question
A machine learning engineer is deploying a model for real-time inference using Amazon SageMaker. The model is a large ensemble that requires 8 GB of memory and 4 vCPUs. The expected traffic is 100 requests per second with a 200 ms latency requirement. Which instance configuration should they choose?
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.c5.2xlarge (8 vCPU, 16 GB)
(ml.c5.2xlarge) is correct because it offers 8 vCPUs and 16 GB memory, meeting both the compute and memory requirements while being cost-effective for CPU-based inference. Option A (ml.t2.medium) is wrong because it has only 2 vCPUs and 4 GB, insufficient for the 8 GB memory need. Option C (ml.p3.2xlarge) is wrong because it includes a GPU, which is unnecessary and overkill for this CPU-bound workload, and costs more. Option D (ml.m5.large) is wrong because it has only 2 vCPUs and 8 GB memory, lacking the required vCPUs and barely meeting memory.
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.t2.medium (2 vCPU, 4 GB)
Why it's wrong here
Insufficient memory.
- ✓
ml.c5.2xlarge (8 vCPU, 16 GB)
Why this is correct
Adequate memory and vCPUs for the workload.
- ✗
ml.p3.2xlarge (8 vCPU, 61 GB GPU)
Why it's wrong here
Overkill and expensive; GPU not needed.
- ✗
ml.m5.large (2 vCPU, 8 GB)
Why it's wrong here
Insufficient vCPUs for 100 req/s.
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