- A
Use a GPU instance (ml.p3.2xlarge) and optimize the model with SageMaker Neo compilation.
Why wrong: GPU instance costs more and requires code changes to utilize GPU; Neo compilation may not be sufficient if the model is not GPU-friendly.
- B
Attach an Elastic Inference accelerator (e.g., ml.eia2.medium) to the existing CPU endpoint.
Elastic Inference provides cost-effective acceleration for XGBoost and other models without code changes.
- C
Use SageMaker Neo to compile the model for CPU with INT8 quantization.
Why wrong: Quantization may reduce latency but not as much as hardware acceleration; Neo compilation alone does not guarantee latency reduction without hardware support.
- D
Migrate the model to AWS Lambda with a custom runtime and use AVX instructions.
Why wrong: Lambda has a 15-minute timeout and limited CPU options; not suitable for real-time inference with large models.
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 is deploying a deep learning model for real-time inference using Amazon SageMaker. The model is a CPU-intensive XGBoost model that performs well with CPU. However, the team wants to minimize latency further by using hardware acceleration. They are considering Amazon Elastic Inference (EI) or moving to a GPU instance. The model is not optimized for GPU, so significant code changes would be required. Which approach is the MOST cost-effective way to reduce latency without changing the model code?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Attach an Elastic Inference accelerator (e.g., ml.eia2.medium) to the existing CPU endpoint.
Option B is correct because Amazon Elastic Inference (EI) allows you to attach a low-cost GPU-powered acceleration to an existing SageMaker CPU endpoint without any code changes. Since the XGBoost model is CPU-optimized and not GPU-native, EI provides hardware acceleration for the inference computation (specifically matrix operations) while keeping the model execution on the CPU, thus reducing latency without requiring model modifications.
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 a GPU instance (ml.p3.2xlarge) and optimize the model with SageMaker Neo compilation.
Why it's wrong here
GPU instance costs more and requires code changes to utilize GPU; Neo compilation may not be sufficient if the model is not GPU-friendly.
- ✓
Attach an Elastic Inference accelerator (e.g., ml.eia2.medium) to the existing CPU endpoint.
Why this is correct
Elastic Inference provides cost-effective acceleration for XGBoost and other models without code changes.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker Neo to compile the model for CPU with INT8 quantization.
Why it's wrong here
Quantization may reduce latency but not as much as hardware acceleration; Neo compilation alone does not guarantee latency reduction without hardware support.
- ✗
Migrate the model to AWS Lambda with a custom runtime and use AVX instructions.
Why it's wrong here
Lambda has a 15-minute timeout and limited CPU options; not suitable for real-time inference with large models.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume GPU instances are always the best for hardware acceleration, but the question explicitly states the model is not GPU-optimized and requires significant code changes, making Elastic Inference the only viable option that reduces latency without code modifications.
Detailed technical explanation
How to think about this question
Amazon Elastic Inference works by attaching a separate EI accelerator (e.g., ml.eia2.medium) to a SageMaker endpoint, which offloads the computationally expensive tensor operations (like matrix multiplications) from the CPU to a dedicated GPU accelerator, while the model logic remains on the CPU. This is particularly effective for XGBoost models because the inference path involves both CPU-bound decision tree traversal and matrix operations (e.g., in feature transformations), and EI accelerates only the latter without requiring any code changes. In real-world scenarios, teams often use EI to reduce inference latency by 30-50% for CPU-based models while keeping costs lower than full GPU instances.
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 — study guide chapter
Learn the concepts, then practise the questions
- →
Deployment and Orchestration of ML Workflows practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
507 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Attach an Elastic Inference accelerator (e.g., ml.eia2.medium) to the existing CPU endpoint. — Option B is correct because Amazon Elastic Inference (EI) allows you to attach a low-cost GPU-powered acceleration to an existing SageMaker CPU endpoint without any code changes. Since the XGBoost model is CPU-optimized and not GPU-native, EI provides hardware acceleration for the inference computation (specifically matrix operations) while keeping the model execution on the CPU, thus reducing latency without requiring model modifications.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More MLA-C01 practice questions
- A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality…
- A data scientist trained a logistic regression model on a dataset with 100 features. After training, the training accura…
- A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices shou…
- A company is using SageMaker to train a neural network for image classification. The training job is taking too long. Th…
- A team is developing a model to predict customer churn. The dataset has 10,000 samples with 20 features. The target vari…
- A data engineer is processing a large dataset in Amazon S3 with AWS Glue ETL. The dataset contains timestamps in multipl…
Last reviewed: Jun 24, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.