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MLA-C01 Practice Question: Is deploying a large language model on SageMaker…
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.)
⚠ Common exam trap
AWS often tests the distinction between training parallelism (Model Parallelism) and inference optimization, leading candidates to incorrectly select Model Parallelism for inference cost savings.
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 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.
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.
Why this is correct
Inference Recommender provides cost-performance recommendations.
- ✗
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.
- ✓
Deploy the model to a multi-model endpoint.
Why this is correct
Multi-model endpoints share resources among multiple models, reducing cost.
- ✗
Use SageMaker Batch Transform for real-time requests.
Why it's wrong here
Batch Transform is for batch processing, not real-time inference.
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