easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company uses Amazon SageMaker to train and…
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?
⚠ Common exam trap
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.
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.
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.
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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.