The answer is to deploy the model to a multi-model endpoint, use SageMaker Inference Recommender, and enable auto-scaling with a target tracking policy. Multi-model endpoints reduce costs by hosting multiple LLMs on a single instance, sharing the underlying infrastructure without sacrificing isolation, which directly addresses the need to optimize SageMaker inference costs for large language models. SageMaker Inference Recommender then runs load tests against your model to recommend the most cost-effective instance type and configuration that meets your latency and throughput requirements, eliminating over-provisioning. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your ability to balance cost and performance under the "Optimize Inference" domain—a common trap is selecting serverless inference for LLMs, which can spike costs unpredictably due to cold starts and per-request billing. Remember the mnemonic "MIRROR": Multi-model, Inference Recommender, and auto-scaling on Request—three pillars to keep your LLM inference lean and fast.
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.
An organization is deploying a large language model on SageMaker and needs to optimize inference costs while maintaining low latency. Which three strategies should they consider? (Select THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SageMaker Inference Recommender to find optimal instance and configuration.
A is correct because SageMaker Inference Recommender runs load tests against your model to recommend the most cost-effective instance type and configuration (e.g., instance count, container parameters) that meets your latency and throughput requirements. This eliminates guesswork and ensures you are not over-provisioning or under-provisioning resources, directly optimizing inference costs.
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 Inference Recommender to find optimal instance and configuration.
Read the scenario before looking for a memorised answer.
✗
Enable SageMaker Model Parallelism for inference.
Why it's wrong here
Model Parallelism is designed for training, not inference.
✓
Use SageMaker Elastic Inference to attach GPU acceleration.
Why this is correct
Elastic Inference provides GPU acceleration at lower cost than full instances.
Related concept
Read the scenario before looking for a memorised answer.
✓
Deploy the model to a multi-model endpoint.
Why this is correct
Multi-model endpoints share resources among multiple models, reducing cost.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SageMaker Batch Transform for real-time requests.
Why it's wrong here
Batch Transform is for batch processing, not real-time inference.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between training parallelism (Model Parallelism) and inference optimization, leading candidates to incorrectly select Model Parallelism for inference cost savings.
Detailed technical explanation
How to think about this question
SageMaker Inference Recommender uses a combination of instance family benchmarks and your model's characteristics (e.g., input size, model architecture) to run a series of inference requests, measuring metrics like p99 latency and cost per inference. It then outputs a recommendation that balances performance and cost, often suggesting Graviton-based instances for CPU workloads or G5 instances for GPU-accelerated models. Elastic Inference (option C) attaches a fraction of a GPU to a CPU instance, reducing idle GPU costs for models that do not require full GPU utilization, while multi-model endpoints (option D) share a single container across multiple models, amortizing infrastructure costs across models with variable traffic patterns.
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
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: Use SageMaker Inference Recommender to find optimal instance and configuration. — A is correct because SageMaker Inference Recommender runs load tests against your model to recommend the most cost-effective instance type and configuration (e.g., instance count, container parameters) that meets your latency and throughput requirements. This eliminates guesswork and ensures you are not over-provisioning or under-provisioning resources, directly optimizing inference costs.
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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