MLS-C01
The AWS Certified Machine Learning - Specialty (MLS-C01) validates your ability to design, build, train, tune, and deploy machine-learning solutions on AWS. It is one of the toughest exams in the AWS catalog — it expects real ML judgment across the full pipeline, not just service recall, with a heavy focus on Amazon SageMaker. A pass signals to employers and clients that you can ship production ML on AWS. Note: AWS retired this exam on March 31, 2026 and replaced it with the ML Engineer - Associate (MLA-C01); this page documents MLS-C01 for those who hold or are recertifying it.
Pay Only After You Pass
No upfront fee — you settle only after your verified passing result. We advertise guaranteed results — 100% pass guaranteed or money back.
How the AWS Machine Learning - Specialty exam is built — at a glance
65
65 total questions — 50 scored and 15 unscored pretest items that don't affect your result and aren't flagged. You won't know which are which, so treat every question as if it counts.
180
Three hours of seat time — roughly 2.7 minutes per question. The long, scenario-heavy stems make time management one of the real challenges of this exam.
750
A scaled score of 750 out of 1000 is needed to pass. Scoring is compensatory, so a strong total carries you — there's no per-domain minimum to clear.
Every question is either multiple choice (one correct answer out of four) or multiple response (two or more correct answers out of five or more). There is no penalty for wrong answers, so never leave a question blank. Unlike adaptive admissions tests, MLS-C01 is a fixed-form exam — difficulty does not shift based on your answers, and you can flag and revisit any question.
Your raw score across the 50 scored questions is converted to a scaled score between 100 and 1000, with 750 as the cut. AWS reports a simple pass/fail plus your scaled score and a per-domain performance breakdown (meets / needs improvement) to help you target a retake. Results typically post to your AWS Certification account within a few days of test day.
Four domains — bars show each domain's share of your scored content
Creating data repositories, building ingestion and ETL pipelines, and transforming data for ML — using S3, Kinesis, Glue, Data Pipeline, and Athena to land the right data in the right shape.
Sanitizing and preparing data, feature engineering, handling missing values and outliers, and analyzing and visualizing data for ML — the unglamorous work that decides whether a model can succeed.
The heaviest domain. Framing business problems as ML problems, choosing algorithms, training and tuning models, hyperparameter optimization, and evaluating with the right metrics (precision, recall, F1, AUC, RMSE).
Building performant, scalable, secure ML solutions: SageMaker deployment, endpoints and auto-scaling, monitoring and retraining, plus AI services like Rekognition, Comprehend, Transcribe, and Polly.
Modeling and Exploratory Data Analysis together make up 60% of your scored content, so the exam rewards genuine ML fundamentals far more than memorized service limits. Amazon SageMaker — its built-in algorithms, training jobs, and hosting options — threads through every domain and is the single most important service to know cold.
Two ways to sit the exam — and what to expect on test day
You sit the exam in a quiet, monitored room at a Pearson VUE center. Staff verify your government-issued ID, store your belongings, and watch the room throughout. No personal scratch paper is allowed — a provided on-screen or erasable noteboard is used instead. The exam fee is $300 USD.
You take the exam from a private room at home, monitored by a live proctor through OnVUE. You'll run a system test in advance, then complete a webcam room scan and ID check before the exam unlocks. The fee is the same $300 USD. No physical scratch paper is permitted — only the built-in digital whiteboard.
A valid, unexpired government photo ID with a name matching your AWS Certification account exactly. The proctor captures your photo before the exam begins.
For OnVUE: a clear desk, no second monitor, no phone or notes within reach, and a 360° webcam scan of the room. No one else may enter during the session.
Stay in frame and on-camera the whole time. There are no scheduled breaks. Talking aloud, leaving the seat, or losing connection can flag or void the session.
Built for practitioners doing real ML work on AWS
No required exams — but AWS recommends real experience
Difficulty: MLS-C01 is widely rated one of the hardest AWS exams. It blends data-engineering breadth with genuine ML depth — you're expected to pick the right algorithm and metric, diagnose overfitting, and reason about SageMaker deployment under long, scenario-based questions. Most candidates invest well beyond the prep of an associate exam, which is exactly the kind of pressure point our help is designed to remove.
The AWS Machine Learning - Specialty is a high-stakes, deeply technical exam standing between you and a credential employers respect. Exam Assist pairs you with a vetted AWS ML specialist and works on a pay-after-you-pass model — so the risk sits with us, not you. No upfront fee, guaranteed results: Exam Assist handles the sitting end to end, and you settle only after the verified result.
Tell us your target date, delivery method (Pearson VUE center or OnVUE online), and where you are in your prep. Takes a couple of minutes over WhatsApp, Telegram, or Discord.
We review your timeline and goal and tell you plainly whether it's realistic — before any money is discussed. If it isn't a fit, we say so.
Exam Assist handles the sitting end to end. You're matched with an AWS ML specialist who maps the work around the four domains, the SageMaker-heavy modeling content, and the OnVUE environment — discreetly and confidentially.
You only pay once your passing result is confirmed in your AWS Certification account. No verified pass, nothing owed.
See the full pay-after-you-pass AWS service, pricing model, and how matching works.
Straight answers about the AWS Machine Learning - Specialty exam
Service, guides, and sibling AWS exams
Pair with a vetted AWS specialist on a results-first arrangement. No upfront fee — settle only after a verified passing result.
Explore the service GuideHow the foundational, associate, professional, and specialty tracks fit together — and where ML credentials sit on the path.
Read the guide AWSThe closest sibling on the data side — analytics pipelines, lakes, and warehouses on AWS, sharing much of the data-engineering ground.
View exam AWSThe most popular AWS certification and a common foundation built before tackling specialty exams like Machine Learning.
View exam Google CloudThe Google Cloud counterpart for ML practitioners — useful if you're comparing cloud ML credentials across providers.
View exam Get StartedShare your exam, target date, and timeline. We'll respond with an honest feasibility answer — no upfront fee.
Start nowGet expert AWS Machine Learning help with no upfront fee — you settle only after your verified passing result. Honest feasibility answer first, results-first arrangement always.