- A
Use SageMaker processing job with a script to load the model and run inference.
Why wrong: Processing jobs are for data preprocessing and postprocessing, not for running inference; inference is more efficiently done via batch transform.
- B
Create a real-time endpoint and send all data as a large batch.
Why wrong: Real-time endpoints are designed for low-latency inference per request, not for high-volume batch processing; they also incur ongoing costs.
- C
Use multiple ml.c5.4xlarge instances in a batch transform job with custom partitioning.
Why wrong: Using CPU instances for a GPU-based model will be slower and may not meet the 8-hour requirement; multiple instances increase cost.
- D
Use SageMaker batch transform with a single ml.p3.2xlarge instance.
A single GPU instance can handle the workload within 8 hours, minimizing cost. Batch transform is designed for high-throughput inference.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 company uses Amazon SageMaker to train and deploy machine learning models. They need to run batch predictions on 10 TB of data stored in Amazon S3 every night. The model is a PyTorch neural network that fits in GPU memory. The predictions are not time-sensitive, but the job must complete within 8 hours. Which approach would be the MOST cost-effective?
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 SageMaker batch transform with a single ml.p3.2xlarge instance.
Option D is the most cost-effective because SageMaker batch transform with a single ml.p3.2xlarge instance provides GPU acceleration for the PyTorch neural network, which fits in GPU memory, and can process 10 TB of data within 8 hours. Batch transform automatically handles data partitioning and inference, eliminating the need for custom orchestration, and the single instance avoids the overhead and cost of multiple instances. The ml.p3.2xlarge offers a balance of GPU compute and cost, making it ideal for non-time-sensitive nightly batch jobs.
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.
- ✗
Use SageMaker processing job with a script to load the model and run inference.
Why it's wrong here
Processing jobs are for data preprocessing and postprocessing, not for running inference; inference is more efficiently done via batch transform.
- ✗
Create a real-time endpoint and send all data as a large batch.
Why it's wrong here
Real-time endpoints are designed for low-latency inference per request, not for high-volume batch processing; they also incur ongoing costs.
- ✗
Use multiple ml.c5.4xlarge instances in a batch transform job with custom partitioning.
Why it's wrong here
Using CPU instances for a GPU-based model will be slower and may not meet the 8-hour requirement; multiple instances increase cost.
- ✓
Use SageMaker batch transform with a single ml.p3.2xlarge instance.
Why this is correct
A single GPU instance can handle the workload within 8 hours, minimizing cost. Batch transform is designed for high-throughput inference.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that multiple CPU instances are more cost-effective than a single GPU instance for batch inference, but the trap here is that GPU acceleration dramatically reduces processing time and instance count for neural networks, making a single GPU instance cheaper overall than a cluster of CPU instances.
Detailed technical explanation
How to think about this question
SageMaker batch transform automatically shards input data from S3 and distributes inference across the specified instance, handling model loading and batching internally. The ml.p3.2xlarge instance features a single NVIDIA V100 GPU with 8 GB of memory, which is sufficient for the model that fits in GPU memory, and can process large volumes of data efficiently using optimized CUDA kernels. In practice, for a 10 TB dataset, the batch transform job will read data in parallel from S3, run inference in mini-batches on the GPU, and write results back to S3, all within the 8-hour window, while a CPU-based approach would require many more instances to achieve the same throughput, increasing cost.
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
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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: Use SageMaker batch transform with a single ml.p3.2xlarge instance. — Option D is the most cost-effective because SageMaker batch transform with a single ml.p3.2xlarge instance provides GPU acceleration for the PyTorch neural network, which fits in GPU memory, and can process 10 TB of data within 8 hours. Batch transform automatically handles data partitioning and inference, eliminating the need for custom orchestration, and the single instance avoids the overhead and cost of multiple instances. The ml.p3.2xlarge offers a balance of GPU compute and cost, making it ideal for non-time-sensitive nightly batch jobs.
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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Last reviewed: Jun 30, 2026
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