Amazon Web Services certification practice
Practise AWS Certified Machine Learning Engineer Associate MLA-C01 across all 4 official exam domains — ML Model Development, Data Preparation for Machine Learning, Deployment and Orchestration of ML Workflows, ML Solution Monitoring, Maintenance, and Security — with original exam-style questions, full explanations, and wrong-answer analysis. AWS certification exams usually test architecture trade-offs: service selection, resilience, cost, security, networking and operations. Courseiva helps you practise those decisions through original scenario-based questions.
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
New here? Start with this
Recommended: 25-question diagnostic
Covers all 4 domains · takes ~5 min · no account needed · shows your weak areas immediately
Key facts for the AWS Certified Machine Learning Engineer Associate MLA-C01 exam.
Exam cost
$150 USD / ~£120
Duration
130 minutes
Question count
65 questions
Passing score
720 / 1000
Validity
3 years
Delivered by
Pearson VUE
Online proctored
Yes — available
Retake policy
100% free — no account required
Everything on Courseiva for MLA-C01 is free — no subscription, no trial, no email wall.
Courseiva provides free AWS Certified Machine Learning Engineer Associate MLA-C01 (MLA-C01) practice questions with original exam-style questions, detailed explanations, topic-based practice, mock exams, readiness tracking, and study analytics. Courseiva is an independent platform and is not affiliated with Amazon Web Services.
14 days between attempts; third attempt requires 90-day wait
Question formats
The AWS Certified Machine Learning Engineer – Associate (MLA-C01) is a newer Associate-level certification that validates practical skills in deploying and operating ML workloads on AWS. Unlike the Specialty exam which emphasises data science theory, MLA-C01 focuses on ML engineering: packaging models, deploying to SageMaker endpoints, building MLOps pipelines, monitoring model performance, and applying responsible AI practices. The certification is aimed at ML engineers and MLOps practitioners who operationalise models in production environments.
Official prerequisite
None formal; AWS recommends 1+ year of ML engineering or data science experience on AWS.
Recommended experience
Familiarity with SageMaker pipelines, model deployment, Python, and basic ML concepts; experience with MLOps tooling is beneficial.
1+ year of hands-on ML engineering on AWS, particularly SageMaker.
Should you skip this exam?
Complete beginners to ML — build foundational ML and AWS knowledge first.
Courseiva study tip
Focus on SageMaker MLOps pipelines, model deployment patterns, monitoring with Model Monitor, and responsible AI tooling on AWS. Less algorithm theory than the Specialty exam.
UK salary range
£55,000–£85,000
US salary range
$115,000–$155,000
Job roles that cite MLA-C01
What certification to take next
The exam costs $150 USD (approximately £120 GBP), in line with other AWS Associate-level exams.
You need a scaled score of 720 out of 1000 to pass.
The exam has 65 questions with 130 minutes to complete them.
MLA-C01 focuses on ML engineering and MLOps — deploying, monitoring, and operationalising models. MLS-C01 is broader and tests deeper ML theory, algorithm selection, and data science skills.
Key topics include data preparation and feature engineering, model training and optimisation with SageMaker, deployment and inference, MLOps pipeline automation, monitoring and observability, and responsible AI practices.
The certification is valid for 3 years from the date you pass the exam.
It is ideal for ML engineers and MLOps practitioners who deploy and manage ML models on AWS and want to formalise their skills at the Associate level.
See how MLA-C01 stacks up against related certifications.
Courseiva Exam Intelligence
Data confidence: BuildingDerived from Courseiva's continuously updated practice dataset: 835 exam-style questions. Updated 2 July 2026.
Question difficulty mix
Average time per question: 91s
4
Exam domains
official blueprint domains covered by the bank
835
Questions
exam-style questions in the MLA-C01 bank
Practice modes
Choose a mode that matches your current study phase. Use topic practice early on and mock exams closer to your exam date.
All MLA-C01 Questions
Browse the full question bank organised by domain and difficulty.
Practice Test
Start a 20-question session with a full explanation after each answer.
Mock Exam
Simulate the real exam with a timed, full-length question set.
Quick 25
A 25-question warm-up — ideal for a short daily study session.
Flashcards
Reinforce key terms, commands, and protocol facts with flashcards.
Domain Practice
Focus exclusively on one exam domain to target your weakest area.
50-Question Set
Mid-length practice set covering all domains.
Exam guide
Start with a short practice session, review each missed answer, then return to the topics that caused mistakes.
AWS Certified Machine Learning Engineer Associate MLA-C01 spans 4 official exam domains (ML Model Development, Data Preparation for Machine Learning, Deployment and Orchestration of ML Workflows, and more). Questions test whether you can apply each objective in a realistic scenario, not just recall a definition.
Related practice questions
Use these internal links to move from the exam guide into focused topic practice pages.
Practise MLA-C01 questions linked to ML Model Development.
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
Practise MLA-C01 questions linked to MLA-C01 scenario.
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice directory
Every practice set is a unique selection of questions — numbered tests, domain-focused sessions, and topic-specific quizzes.
Showing 8 of 56 tests. See all 56 MLA-C01 practice tests →
Official blueprint
Target weak areas by practising questions from a single domain. Each domain page shows all questions, difficulty breakdown, and a focused practice session.
Practise MLA-C01 questions in this domain →
Practise MLA-C01 questions in this domain →
Practise MLA-C01 questions in this domain →
Practise MLA-C01 questions in this domain →
Deep-dive questions
The most-searched MLA-C01 questions — each with a detailed explanation, worked example, and full answer breakdown.
Learn the concepts
16 book-style chapters cover every MLA-C01 objective in plain English. Read the theory here, then test it with practice questions.
Chapter 1
Introduction to Machine Learning on AWS
12 min read
Chapter 2
Data Preparation Foundations: S3, Glue, and Data Wrangling
12 min read
Chapter 3
Data Transformation and Feature Engineering with SageMaker
12 min read
Chapter 4
Data Labeling and Quality Assurance with SageMaker Ground Truth
14 min read
Chapter 5
Model Selection and Training Frameworks on SageMaker
12 min read
Chapter 6
Hyperparameter Tuning and Model Optimization
12 min read
Chapter 7
Model Evaluation and Validation Techniques
12 min read
Chapter 8
Building and Automating ML Pipelines with SageMaker Pipelines
12 min read
Free study guide
The Courseiva MLA-C01 study guide covers exam format, the official domain blueprint with weights, a structured study plan, and domain-specific tips for each section. Use it alongside practice questions to build both conceptual depth and exam confidence.
Sample questions
Original exam-style questions — not copied from any exam or dump site. Open a question for the full answer breakdown.
A team uses SageMaker Clarify to monitor bias drift in production. They schedule weekly analysis. After a month, Clarify reports a significant increase in a bias metric. What shoul…
A team is deploying a machine learning model for real-time fraud detection. The model must have inference latency under 10 ms and handle up to 1000 requests per second. The model i…
A company wants to deploy a PyTorch model on SageMaker for real-time inference. Which two steps are required? (Select TWO.)
A machine learning team is preparing data for a binary classification model. The target variable has a severe class imbalance (95% negative, 5% positive). The team wants to train a…
A media company uses SageMaker to host a real-time video recommendation model. The model is deployed on a single ml.c5.xlarge endpoint. During a major live event, traffic surges to…
A company uses Amazon SageMaker to train and deploy a machine learning model. After deployment, they notice that the model's accuracy drops significantly over time due to changes i…
Related certifications
Studying for MLA-C01? These related Amazon Web Services certifications share concepts and are common next steps.
AWS Data Engineer Associate
DEA-C01 · 1,711 practice questions
AWS Security Specialty
SCS-C02 · 1,674 practice questions
AWS Machine Learning Specialty
MLS-C01 · 1,672 practice questions
AWS Database Specialty
DBS-C01 · 1,663 practice questions
AWS Solutions Architect Professional
SAP-C02 · 1,660 practice questions
AWS Advanced Networking Specialty
ANS-C01 · 1,621 practice questions
Official booking and top-rated study resources for the MLA-C01.
Ways to study MLA-C01