Amazon Web Services
Advanced Level

AWS Machine Learning Specialty (MLS-C01) Certification Guide 2026

Specialty certification for machine learning engineers on AWS. Validates ability to design, implement, deploy, and maintain ML solutions using SageMaker and other AWS AI/ML services.

Exam Cost
$300
Pass Rate
55%
Avg. Salary
$145K–$180K
Vendor
Amazon Web Services

Here's what most AWS Machine Learning Specialty (MLS-C01) guides won't tell you: the difference between candidates who pass first time and those who retake isn't intelligence — it's preparation quality. This page gives you the exam blueprint, real salary data ($145K–$180K in 2026), a week-by-week study plan, and the strategy that experienced Data & AI professionals actually use.

Exam Cost
$300
Pass Rate
55%
Avg Salary
$145K–$180K
Validity
3 years

Is the AWS Machine Learning Specialty (MLS-C01) Worth It in 2026?

The AWS Machine Learning Specialty (MLS-C01) generates a documented ROI for professionals in Data & AI — but the size of that ROI depends heavily on where you are in your career and what you do with the credential after passing.

Salary data from Glassdoor (2026) and BLS.gov consistently shows AWS Machine Learning Specialty (MLS-C01) holders earning $145K–$180K — a measurable, documented premium over non-certified peers in equivalent roles
Active job postings in Data & AI explicitly require or strongly prefer the AWS Machine Learning Specialty (MLS-C01) — it's an ATS screening filter that puts your CV in front of a human reviewer before uncertified applicants get there
Enterprise employers and regulated industries prioritise certified candidates in automated screening — the credential filters in, not just out
The AWS Machine Learning Specialty (MLS-C01) validates specific, testable knowledge — not just years on a job title, which hiring managers increasingly treat as unreliable on its own
Many employers reimburse the $300 exam fee entirely through L&D budgets — reducing your personal outlay to zero while you keep the full career benefit

The honest caveat: the AWS Machine Learning Specialty (MLS-C01) validates skills you have — it does not substitute for skills you don't. A credential without underlying competence won't survive technical interview scrutiny at serious employers. The professionals who get the best ROI are those who use it to put a verifiable stamp on genuine hands-on ability — not those who treat passing the exam as the destination.

Compare this cert side-by-side: AWS Machine Learning Specialty (MLS-C01) vs alternatives →

AWS Machine Learning Specialty (MLS-C01) Exam Details 2026

Current exam specifications verified from official Amazon Web Services documentation at aws.amazon.com. Always confirm before registering — format and pricing can change with exam version updates:

SpecificationDetails
Questions65
DurationVaries by level
FormatMultiple choice & multiple response
Passing ScoreVaries (720–750/1000)
Certification Validity3 years
DeliveryPearson VUE / Online Proctored (aws.amazon.com)
LanguagesEnglish + select languages
Exam Fee (2026)$300
Official Sourceaws.amazon.com
💡 Exam fee verified at aws.amazon.com. Retake fees and waiting periods are published in the Amazon Web Services candidate handbook. Many employers reimburse exam fees through L&D budgets — check before paying out of pocket.

Exam Domains — What's Tested

The AWS Machine Learning Specialty (MLS-C01) tests candidates across these knowledge domains. Allocate study time proportional to each domain's exam weighting, published in the official blueprint at aws.amazon.com:

Data engineering for ML
Exploratory data analysis
Modeling techniques
ML implementation & operations
AWS ML services (SageMaker, Rekognition, Comprehend)

Download the current exam blueprint before you start — Amazon Web Services revises content with each new exam version, and outdated study materials frequently cover deprecated topics.

AWS Machine Learning Specialty (MLS-C01) Salary Data 2026

Certified professionals holding the AWS Machine Learning Specialty (MLS-C01) earn $145K–$180K annually based on aggregated data from Glassdoor, ZipRecruiter, LinkedIn Salary Insights, and BLS.gov as of 2026. The salary premium over equivalent non-certified peers in the same role is consistently documented across multiple sources.

ExperienceTypical Range (USD)Notes
3-5 yrs $100K–$135K Credential differentiates at entry — experience gaps are smaller, so certs matter more
5-10 yrs $135K–$175K Core market rate where salary premium over non-certified is best documented
10+ yrs $175K–$230K Leadership & budget ownership adds significant premium beyond technical rates
Major Markets (NY/SF/London) +15–30% above median High-cost-of-living markets consistently pay above national averages for certified roles

Data from BLS.gov, Glassdoor, and LinkedIn Salary Insights. 2026 figures. Individual compensation varies by employer, geography, and total experience.

View the full AWS Machine Learning Specialty (MLS-C01) salary guide →

AWS Machine Learning Specialty (MLS-C01) Prerequisites & Who Should Apply

The AWS Machine Learning Specialty (MLS-C01) is a Advanced-level credential from Amazon Web Services. Formal prerequisites are recommended experience in Data & AI. Here's what realistically determines first-attempt success:

  • Typically 3–5 years of active professional experience in data & ai — often formally required at registration
  • The Amazon Web Services Associate or Intermediate-level certification in this domain, or verifiable equivalent hands-on experience
  • This is not an entry-level exam — scenario and lab questions assume deep operational knowledge from real production environments
  • Formal vendor-authorised training or a rigorous self-study programme covering all exam domains before you register

Difficulty assessment: How hard is the AWS Machine Learning Specialty (MLS-C01)? →

What Is the AWS Machine Learning Specialty (MLS-C01) Certification?

The AWS Machine Learning Specialty (MLS-C01) is a Advanced-level professional credential issued by Amazon Web Services. Specialty certification for machine learning engineers on AWS. Validates ability to design, implement, deploy, and maintain ML solutions using SageMaker and other AWS AI/ML services.

In 2026, the AWS Machine Learning Specialty (MLS-C01) continues to command genuine hiring authority in Data & AI. It appears consistently as a required or preferred qualification in job descriptions at large enterprises, government agencies, consulting firms, and high-growth technology companies worldwide — not as a courtesy requirement, but as an active screening criterion that determines which CVs reach a human reviewer.

Who Is This Certification For?

Data scientists and ML engineers with 1+ year of ML experience on AWS.

Target Roles — 2026

Based on active job market data, the AWS Machine Learning Specialty (MLS-C01) delivers the strongest ROI for professionals targeting:

ML Engineer Data Scientist AI/ML Architect SageMaker Specialist

Employers Who Actively Hire AWS Machine Learning Specialty (MLS-C01) Holders

Organisations that regularly post Data & AI roles requiring or preferring AWS Machine Learning Specialty (MLS-C01) credentials include: Google, Microsoft, Amazon, Meta, Databricks, Snowflake, Palantir, McKinsey Analytics, JPMorgan, Goldman Sachs. Primary hiring industries: Technology, Financial Services, Consulting, Healthcare, Media. Cloud data certifications appear in 52% of senior data engineer postings (2026).

10-Week AWS Machine Learning Specialty (MLS-C01) Study Plan for Working Professionals

Structured for 1–2 hours on weekdays and 3–4 hours on weekends — the most realistic schedule for full-time professionals. Non-negotiable rule: don't advance to the next week until mock exam scores are consistently above 75%. Premature advancement is the most common reason candidates sit the exam under-prepared and pay the retake fee.

  • Weeks 1–2Download the official AWS Machine Learning Specialty (MLS-C01) exam blueprint from aws.amazon.com (it's free). Map each domain by weight — highest-percentage domains need proportionally more of your time. Block a realistic daily schedule: 1–2 hours on weekdays, 3–4 hours on weekends. Professionals who pre-schedule their study sessions pass at measurably higher rates than those who fit it in ad-hoc.
  • Weeks 3–4Work through core domains using vendor-authorised training or a well-reviewed course (Udemy, A Cloud Guru, official Amazon Web Services training, or Linux Foundation). Take chapter-end quizzes and log every wrong answer in a dedicated revision doc — that document becomes your most valuable study asset in weeks 7–9.
  • Weeks 5–6Shift to active question practice. Aim for 150+ questions per week from quality test banks — official Amazon Web Services practice exams, Whizlabs, or Udemy practice tests. Review each wrong answer immediately while the context is fresh. Don't batch reviews to end-of-week — it kills retention.
  • Weeks 7–8Take 3 full-length timed mock exams under real exam conditions — no notes, no phone, strict timer. Scoring below 75%? Add a week here and return specifically to your weakest domains. Don't book the real exam until you're consistently hitting 78%+ across multiple separate attempts.
  • Week 9Targeted revision only — work exclusively from your wrong-answer log and flagged weak topics. Stop re-reading full chapters. For each wrong answer, understand precisely why the correct answer is right — not just what it is. This is the highest-ROI study activity available to you at this stage.
  • Week 10Light review in the first 2–3 days only. Confirm your exam booking, check your ID requirements, and test your proctoring software if sitting online. Sleep properly the night before — genuine readiness beats last-minute cramming every single time. You've done the work. Trust it.
📚 Recommended resources: Official Amazon Web Services study guide at aws.amazon.com · Whizlabs · Udemy practice tests · Official vendor-authorised training. The official materials define what the exam tests. Everything else is preparation for how it's asked.

View the full AWS Machine Learning Specialty (MLS-C01) learning roadmap →

Exam Strategy — AWS Machine Learning Specialty (MLS-C01) 2026

Preparation determines whether you're ready. Strategy determines how effectively you perform on the day. These are the techniques that separate first-attempt passers:

  • Read the complete question before touching the options — exam writers hide the trap in qualifiers like "MOST cost-effective," "BEST practice," or "FIRST step." Miss those words and you'll pick the wrong answer on a question you actually know
  • Eliminate obviously wrong options first, then choose from the remaining two using Amazon Web Services best-practice logic — not necessarily what you'd do in your specific job, which may deviate from official methodology
  • Flag difficult questions and move on immediately — never let one question consume time allocated to five others you could answer confidently. You can return to flagged items at the end
  • In scenario-based questions, identify your assumed role first (architect, admin, security engineer, manager) — it changes which option is the intended correct answer
  • When two answers both look correct, the one most aligned with Amazon Web Services's official documentation is almost always the intended answer — even where real-world practice sometimes differs
  • Don't second-guess answers unless you recall a specific fact that changes the answer — first instinct is statistically more reliable on questions you prepared for

Critical context: the AWS Machine Learning Specialty (MLS-C01) tests Amazon Web Services's recommended methodology — not necessarily the way your specific workplace operates. When two answers both look plausible, the one most aligned with Amazon Web Services's official documentation is almost always the intended correct choice. Your organisation's practice may differ. The exam doesn't care.

Frequently Asked Questions — AWS Machine Learning Specialty (MLS-C01) 2026

The AWS Machine Learning Specialty (MLS-C01) exam costs $300 when booked directly through Amazon Web Services at aws.amazon.com. Always verify the current price on the official vendor site before paying — fees occasionally change with exam version updates, and third-party sites sometimes list outdated figures. Retake fees apply if you don't pass on your first attempt; the waiting period and retake cost are published at aws.amazon.com. One thing worth checking before you pay: many employers cover certification exam fees through their training and development budgets. Ask your HR or L&D team — full reimbursement is common for in-demand credentials like this one.
The AWS Machine Learning Specialty (MLS-C01) first-attempt pass rate is approximately 55%. That figure is context-dependent — candidates who follow a structured study plan and complete 300+ practice questions under timed conditions consistently outperform those who study longer but less deliberately. The most reliable self-assessment benchmark: if you're scoring consistently above 78–80% on full-length practice exams under timed conditions, you're statistically ready. Don't book the real exam until you've hit that threshold across at least three separate mock attempts on different days.
AWS Machine Learning Specialty (MLS-C01) holders earn $145K–$180K according to current data from Glassdoor, ZipRecruiter, and BLS.gov — a consistent, documented salary premium over non-certified peers in equivalent roles. The credential appears in significant volumes of active Data & AI job postings, making it a real hiring filter, not just a resume decoration. For career changers and those targeting salary increases, the ROI relative to the $300 exam fee is typically strong — especially when employers reimburse the cost. The honest caveat: the certification delivers maximum value when paired with genuine hands-on experience. It validates skills you have; it does not substitute for skills you don't.
Most candidates need 8–12 weeks of focused preparation, averaging 1–2 hours per day. Those with direct hands-on professional experience in Data & AI typically need 6–8 weeks. Career changers entering with limited practical exposure may need 12–16 weeks. Quality of study time matters far more than raw hours — active question practice with immediate review of wrong answers consistently outperforms passive video watching or reading. Use the 10-week study plan on this page as your baseline and compress or extend based on where your mock exam scores land.
For the AWS Machine Learning Specialty (MLS-C01) at Advanced level, Amazon Web Services recommends hands-on professional experience in Data & AI alongside foundational domain knowledge. Specific experience requirements and any formal prerequisites are published in the official exam guide at aws.amazon.com. Verify there before registering — requirements can shift with exam version updates. The Advanced level is calibrated for practitioners who actively work in the field, not those learning the domain from scratch.
The AWS Machine Learning Specialty (MLS-C01) qualifies you for roles including: ML Engineer, Data Scientist, AI/ML Architect, SageMaker Specialist. These positions command salaries of $145K–$180K depending on geography, experience level, and employer size. In major markets — New York, London, San Francisco, Sydney, Singapore — senior-level roles frequently reach or exceed the top of that range. The credential carries most weight at larger organisations and in regulated industries where employers use certifications as an active hiring screen. At entry level, it differentiates your CV in ways a matching job title alone cannot.
Most Amazon Web Services certifications require renewal every 2–3 years depending on the credential. Renewal typically involves earning continuing education credits (PDUs, CPEs, or SEUs depending on the vendor), passing a renewal assessment, or passing a higher-level exam in the same track — which usually renews lower credentials automatically. Visit aws.amazon.com for the specific current renewal requirements for AWS Machine Learning Specialty (MLS-C01). Set a calendar reminder 6 months before your certification expires — that gives you enough lead time to complete any CPE requirements without a stressful last-minute scramble.
The vast majority of successful candidates pass while employed full time. The 10-week study plan on this page is specifically structured for working professionals with 1–2 hours available on weekdays and 3–4 hours on weekends. Daily consistency outperforms irregular marathon sessions — shorter daily sessions retain information measurably better over a multi-week preparation window. If your current role actively involves Data & AI work, preparation time naturally shortens because you're reinforcing study material through real-world application every day. The binding constraint is not time — it's getting mock exam scores above 78% before you sit.

AWS Machine Learning Specialty (MLS-C01) Learning Path & Next Steps

The AWS Machine Learning Specialty (MLS-C01) sits within the Amazon Web Services certification track for Data & AI. Here's the full progression and where this credential fits:

You are here AWS Machine Learning Specialty (MLS-C01) Advanced

Also in Data & AI:

Microsoft Azure Data Fundamentals (DP-900) Google Cloud Professional Data Engineer Microsoft Azure AI Fundamentals (AI-900) IBM Data Science Professional Certificate All Data & AI →