MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A team is deploying a model using SageMaker real-time endpoint with an ml.m5.large instance. They notice high latency under peak load. They want to reduce latency without increasing instance size. Which THREE actions could help? (Select THREE.)
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
✓
Quantize the model to reduce its size
SageMaker Neo compiles the model for the target hardware, reducing latency. Elastic Inference attaches GPU acceleration to a CPU instance. Model quantization reduces model size and speeds up inference. Increasing instance count does not reduce per-request latency (it increases throughput). Changing to a GPU instance increases instance size.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 835 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A machine learning engineer is deploying a TensorFlow model for real-time inference. The model has high latency on CPU. Which TWO actions can reduce inference latency? (Choose two.)
hard- A.Enable SageMaker Model Monitor
- B.Switch to a multi-model endpoint
- ✓ C.Attach Amazon Elastic Inference to the endpoint
- D.Use a larger instance type with more vCPUs
- ✓ E.Compile the model with SageMaker Neo
Why C: Compiling with SageMaker Neo optimizes the model for the target hardware. Attaching Elastic Inference provides GPU acceleration without moving to a full GPU instance.
Variation 2. A team deploys a model on a SageMaker real-time endpoint using an ml.m5.xlarge instance. The model has high latency due to a large neural network. The team wants to reduce latency without changing the model code. Which option should they use?
hard- A.Increase the instance size to ml.m5.4xlarge
- ✓ B.Attach Amazon Elastic Inference to the endpoint
- C.Use SageMaker Neo to compile the model
- D.Switch to a GPU instance like ml.g4dn.xlarge
Why B: Amazon Elastic Inference attaches a fixed amount of GPU acceleration to an EC2 instance, providing cost-effective acceleration for deep learning inference without needing a full GPU instance.
JA
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.