MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data science team needs to deploy a PyTorch model for real-time inference with low latency. The model requires GPU acceleration. Which SageMaker endpoint configuration should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a real-time endpoint using an ml.p3.2xlarge instance
Option D is correct because real-time SageMaker endpoints with GPU instances like ml.p3.2xlarge are specifically designed for low-latency, synchronous inference with GPU acceleration. PyTorch models requiring GPU must use instance types that support NVIDIA CUDA, and the ml.p3 family provides the necessary GPU compute for real-time predictions.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Create a multi-model endpoint using ml.m5.large instances
Why it's wrong here
Multi-model endpoint can use GPU but adds unnecessary complexity; a single model endpoint is simpler.
✗
Create a serverless endpoint with memory set to 6144 MB
Why it's wrong here
Serverless endpoints do not support GPU.
✗
Create a batch transform job using an ml.c5.xlarge instance
Why it's wrong here
Batch transform is not real-time and uses CPU.
✓
Create a real-time endpoint using an ml.p3.2xlarge instance
Why this is correct
Real-time endpoints support GPU instances for low-latency inference.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse batch transform jobs or serverless endpoints with real-time inference, overlooking the explicit GPU requirement and the need for persistent, low-latency compute resources.
Detailed technical explanation
How to think about this question
SageMaker real-time endpoints provision persistent instances that remain active to serve inference requests, ensuring sub-second latency. The ml.p3.2xlarge instance features a single NVIDIA V100 GPU with 8 GB of GPU memory, which is ideal for PyTorch models that leverage CUDA kernels for tensor operations. In contrast, serverless endpoints auto-scale from zero but incur cold starts and lack GPU support, while batch transforms process data in chunks without real-time responsiveness.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a real-time endpoint using an ml.p3.2xlarge instance — Option D is correct because real-time SageMaker endpoints with GPU instances like ml.p3.2xlarge are specifically designed for low-latency, synchronous inference with GPU acceleration. PyTorch models requiring GPU must use instance types that support NVIDIA CUDA, and the ml.p3 family provides the necessary GPU compute for real-time predictions.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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