IT, Cloud & Cybersecurity • Certification
AWS Certified Machine Learning - Specialty
Retired AWS ML specialty now succeeded by MLA and AIP.
Researched and maintained by TestPrepPilot Editorial BoardCloud, IT & ML certifications desk · Figures last verified August 2026
The short version
What is the AWS Certified Machine Learning - Specialty and is it worth it?
MLS-C01 was a 180-minute specialty exam with about 64 questions, costing $300 and requiring a 750 score to pass. It retired on 31 March 2026 and is replaced by MLA-C01 and AIP-C01.
- Historically recognised ML credential
- Covered modelling and deployment
- Retired 31 March 2026
- Earned certs remain valid to expiry
- Useful historical reference
What is it?
What exactly is the AWS Certified Machine Learning - Specialty?
Retired AWS ML specialty now succeeded by MLA and AIP.
The AWS Certified Machine Learning - Specialty (MLS-C01) was a long-running AWS credential covering data engineering, exploratory analysis, modelling and ML deployment. AWS set 31 March 2026 as its final test date; it is succeeded by the ML Engineer Associate (MLA-C01) for engineering and the Generative AI Developer Professional (AIP-C01) for GenAI. Credentials earned before retirement remain valid through their original expiry.
The path in brief
- Note MLS-C01 is retired
- Choose MLA-C01 or AIP-C01
- Study SageMaker and pipelines
- Pass at 720/750 as relevant
- Maintain with recertification
Before you book
Who is eligible to sit the AWS Certified Machine Learning - Specialty?
Who it is for: Data scientists and ML engineers (historical credential).
The test itself
What is the format of the AWS Certified Machine Learning - Specialty?
The AWS Certified Machine Learning - Specialty is administered by Remote proctored (Pearson VUE) and runs 180 minutes, contains 65 questions and requires 750 to pass.
| Administered by | Remote proctored (Pearson VUE) |
|---|---|
| Questions | 65 questions |
| Time limit | 180 minutes |
| Pass mark | 750 |
| Exam fee | $300 |
| Format | Multiple choice and multiple response, online proctored |
Content outline
What topics are on the AWS Certified Machine Learning - Specialty?
The AWS Certified Machine Learning - Specialty is weighted across 4 domains; the largest are Modeling (36%), Exploratory Data Analysis (24%), Data Engineering (20%).
Weightings come from the official exam outline. Study time is best allocated in roughly these proportions rather than evenly across domains.
- Data Engineering 20%
Ingestion, transformation
- Exploratory Data Analysis 24%
Feature engineering
- Modeling 36%
Algorithms, training, tuning
- Machine Learning Implementation and Operations 20%
Deployment, Pipelines
Money & time
How much does the AWS Certified Machine Learning - Specialty cost?
For the AWS Certified Machine Learning - Specialty, the exam fee is $300; the all-in cost is typically $300; most candidates spend n/a preparing.
Cost breakdown
| Exam fee (historical) | $300 |
|---|---|
| Typical total | $300 |
Retired 31 March 2026; see MLA-C01 and AIP-C01.
Timeline
- Study n/a (retired)
- Exam 180 minutes (historical)
Typical prep: n/a
The path
How do you register for the AWS Certified Machine Learning - Specialty?
- 1
Create an AWS Certification account5 minutes
Sign in with your AWS Training account or create one at aws.amazon.com/certification; it manages your exam registration, score report and digital badge.
- 2
Choose your exam and level
Pick the credential that matches your role. Most candidates start at Foundational (CLF/AIF) or Associate (SAA, DVA, SOA) and progress to Professional or Specialty.
- Foundational: CLF-C02, AIF-C01
- Associate: SAA-C03, DVA-C02, SOA-C03, DEA-C01, MLA-C01
- Professional: SAP-C02, DOP-C02, AIP-C01
- Specialty: SCS-C03, ANS-C01
- 3
Schedule and pay10 minutes
Book through Pearson VUE at a test centre or online proctored. Foundational and Associate cost $100/$150; Professional and Specialty cost $300.
- 4
Prepare with official resources2-12 weeks
Use the free Exam Guide, AWS Skill Builder Official Practice Questions and Builder labs. Budget 2-12 weeks depending on level.
- 5
Sit the exam
Exams are scored 100-1000 against a fixed pass mark (700 Foundational, 720 Associate, 750 Professional/Specialty). Results appear immediately.
The fine print
What are the retake and renewal rules for the AWS Certified Machine Learning - Specialty?
- Proctoring & delivery
- Delivered by Pearson VUE at a test centre or online with a live proctor. A government photo ID is required and the online environment is scanned before the session.
- Retake policy
- If you do not pass, you must wait 14 days before retaking the same exam. Each attempt is paid; there is no limit on attempts.
- Score reporting
- Exams are scored on a 100-1000 scale. Foundational pass at 700, Associate at 720, Professional and Specialty at 750. A score report shows section-level feedback under a compensatory model (no section minimum).
- Recertification
- All AWS certifications are valid for three years. Recertify by passing the latest version of the same exam, or by earning a higher-level credential that automatically refreshes lower ones (for example, DevOps Pro refreshes CloudOps Associate).
- Refunds & rescheduling
- Cancellation and reschedule rules follow Pearson VUE; refunds are generally not given for no-shows, and reschedules must occur before the cut-off window.
Context
AWS certification levels
| Level | Duration | Fee | Pass score | |
|---|---|---|---|---|
| Foundational (CLF-C02, AIF-C01) | 90 min | $100 | 700 | |
| Associate (SAA/DVA/SOA/DEA/MLA) | 130 min | $150 | 720 | |
| Professional (SAP/DOP/AIP) | 180 min | $300 | 750 | |
| Specialty (SCS/ANS) | 170 min | $300 | 750 |
What it pays
How much does this credential pay?
Expect data-scientist pay at a May 2024 median of $112,590 (BLS) — the AWS ML Specialty is the advanced machine-learning certification, and ML-engineering roles sit in the fastest-growing segment of the data occupations
The direct answer is that the AWS Certified Machine Learning – Specialty certification is an advanced-level credential for ML and data roles, and the occupations it serves — data scientists (SOC 15-2051), at a May 2024 median annual wage of $112,590, and the software-developer roles ML engineers sit within — place ML-specialist holders in the well-compensated tier of the data and software fields, with ML engineers typically earning above the occupation medians in many markets, though BLS has no ML-specific code. The honest framing is that the certification does not set pay — ML salaries are set by market, employer, and experience — but the credential signals AWS machine-learning competence (data engineering, exploratory data analysis, modeling, and ML implementation on AWS) that employers list for AWS ML roles, and it sits in one of the fastest-growing segments of technology: BLS projects about 30 percent growth for data scientists from 2024 to 2034, much faster than average. The exam itself is an advanced specialty exam: 65 questions with 180 minutes, scored pass/fail on a 100-1000 scale with a passing score of 750, and AWS recommends 2 years of hands-on ML experience on AWS before sitting. The practical read: the ML Specialty is the flagship AWS ML certification, typically pursued by data scientists, ML engineers, and data engineers with AWS experience (the SAA-C03 or a comparable foundation is a common precursor), and its value is the AWS-ML employability signal in a high-demand field. For candidates deciding whether to pursue it, the exam rewards applied AWS ML knowledge, and the strongest preparation combines the AWS official training with hands-on SageMaker practice.
| Measure | Figure | Source / note |
|---|---|---|
| Median annual wage, data scientists | $112,590 | BLS Occupational Outlook Handbook, May 2024 (SOC 15-2051) |
| Lowest 10 percent | less than $70,000 | BLS OOH, May 2024 |
| Highest 10 percent | more than $167,000 | BLS OOH, May 2024 |
| Projected growth, 2024-34 | +30% | BLS Employment Projections |
| The certification itself | Advanced credential | 65 questions, 180 minutes, passing score 750/1000 |
Job growth.BLS projects about 30% growth for data scientists from 2024 to 2034, much faster than average.
Source: BLS OOH — Data Scientists
Your odds
What are the pass rates?
AWS publishes no pass rates — the ML Specialty is 65 questions in 180 minutes with a passing score of 750/1000, and AWS reports pass/fail rather than cohort data
The core fact is that AWS does not publish pass rates for the Machine Learning – Specialty exam, and none is available from an independent source, because AWS reports candidate pass/fail rather than cohort pass percentages. What is published is the exam structure: the exam contains 65 multiple-choice and multiple-response questions with 180 minutes allowed, delivered at Pearson VUE testing centers or online proctored, with a passing score of 750 on the 100-1000 scale; the exam covers four domains — data engineering, exploratory data analysis, modeling, and ML implementation and operations — weighted across the paper. AWS recommends two years of hands-on ML experience on AWS before sitting, and the exam assumes familiarity with SageMaker and the AWS ML stack. Because AWS publishes the exam guide and the official practice exam, the preparation is studying the exam guide and calibrating with the official practice set; the classic failure mode is under-practising the SageMaker-specific items, which reward applied AWS knowledge. The certification is valid for three years and renewable by recertification. The honest advice is to treat the exam guide as the syllabus, complete the AWS official training or an equivalent course, and practise with SageMaker hands-on before booking, since the exam rewards applied AWS ML knowledge.
Read this before quoting the number.AWS publishes no pass rates; the passing score is 750/1000 and certification is valid 3 years.
Your schedule
How long should I study for it?
120-180 study hours over 10-14 weeks of focused study
Plan for roughly 10 to 14 weeks and 120 to 180 hours of study for the ML Specialty, structured around the four domains — data engineering, exploratory data analysis, modeling, and ML implementation — with hands-on SageMaker practice as the highest-yield study tool. The exam’s weight favours the modeling and data-engineering content, so allocate study time there first. The most effective sequence is: first, obtain the exam guide and take the official practice exam to baseline; second, complete the AWS official training or an equivalent ML course; third, build hands-on SageMaker experience — pipelines, training jobs, and deployment; fourth, study the four domains with the exam guide; and fifth, take the official practice exam again and book. The highest-yield habit is the hands-on SageMaker work: the exam rewards applied AWS ML knowledge, and candidates who build the workflows score well above those who only read. Budget the modeling and data-engineering domains the most time, since they carry the largest shares. After passing, note the three-year validity and plan the recertification.
- Weeks 1-345
Baseline + training
- Get the exam guide and take the practice exam
- Complete AWS ML training
- Log weak domains
- Weeks 4-755
SageMaker hands-on
- Build SageMaker pipelines and training jobs
- Practise deployment and inference
- Study data engineering
- Weeks 8-1150
Domains + practice
- Study modeling and EDA content
- Take the official practice exam again
- Re-study weak domains
- Final weeks20
Exam readiness
- Light exam-guide review
- Confirm exam logistics
- Book the exam
Adjusting the pace
AWS AI Practitioner (entry).The entry-level AI credential, a common precursor for candidates new to AWS AI.
ML Engineer – Associate.The newer associate-level ML credential, complementary to the specialty.
How to study
How do I prepare most effectively?
The strategy that reliably passes the ML Specialty is "build hands-on SageMaker experience, weight modeling and data engineering, and calibrate with the official practice exam," and the single biggest error is studying ML theory without AWS-specific practice, which the exam punishes. A second proven approach is mastering the SageMaker-specific items — the exam rewards knowing which SageMaker feature fits each scenario, so build the workflows hands-on. Third, weight the modeling and data-engineering domains, the largest question shares. Fourth, use the official practice exam as the calibration tool and re-study until your score is comfortable. Fifth, confirm the recommended 2-years-of-experience baseline is met or that you have strong AWS foundations, since the exam assumes it. Finally, plan the three-year recertification, since AWS credentials expire and recertification is part of the credential’s lifecycle.
Build SageMaker hands-on
The exam rewards applied AWS ML knowledge.
Weight modeling and data engineering
The largest question shares.
Master the which-service items
Know which AWS ML feature fits each scenario.
Calibrate with the official practice exam
Practice scores are the honest readiness signal.
Plan the 3-year recertification
AWS credentials expire; plan the renewal.
What to buy
Which study resources are worth paying for?
ML Specialty preparation runs $200 to $1,000 depending on the package, with the AWS official training and exam guide as the core investment. The comparison below separates the exam fee, official resources, and the major prep options, with prices current to this review. The buying rule is to budget for the AWS training and a practice-exam package, since the exam rewards the applied and calibrated approach.
| Resource | Price | Format | Best for |
|---|---|---|---|
| ML Specialty exam registration | $300 | Pearson VUE or online proctored | The exam itself |
| AWS exam guide | Free | Official PDF | The authoritative syllabus |
| AWS official practice exam | ~$40 | Online practice set | Readiness calibration |
| AWS training / third-party courses | ~$200-800 | Online courses | Structured review |
| AWS Free Tier SageMaker practice | Free tier | Hands-on service usage | Applied practice |
Prices approximate and dated to this review; the certification is valid 3 years and requires recertification.
Avoid these
What mistakes do candidates most often make?
The most common mistake is studying machine-learning theory without AWS-specific practice, which the exam punishes because its items are built around the AWS ML stack — candidates who know general ML but have never touched SageMaker consistently lose the which-service and which-feature items. A second recurring error is under-weighting the modeling and data-engineering domains, which carry the largest question shares, in favour of the more familiar EDA material. Third, candidates routinely confuse the SageMaker features — Automatic Model Tuning, Model Monitor, Clarify, and Ground Truth each serve a distinct purpose, and the exam’s scenario items reward knowing exactly which one fits the described situation. Fourth, many candidates book the exam without taking the official practice set, converting a calibratable exam into a gamble; the practice set mirrors the question style, and your score on it is the honest readiness signal. Fifth, candidates with less than the recommended two years of hands-on AWS ML experience find the applied items beyond them, so the preparation should include real SageMaker builds, not just reading. Finally, some candidates ignore the three-year validity and let the credential lapse, or skip the recertification planning that keeps an advanced AWS credential current in a fast-moving field.
✕Theory without AWS practice
✓Build SageMaker workflows hands-on.
✕Under-weighting modeling
✓The modeling domain is the largest share.
✕Confusing SageMaker features
✓Master which feature fits each scenario.
✕Skipping practice calibration
✓Take the official practice exam and read the score.
✕Missing the 3-year renewal
✓Plan recertification before expiry.
What you'll face
What question types will I see?
The ML Specialty contains 65 multiple-choice and multiple-response questions in 180 minutes, scored pass/fail at 750/1000, covering four domains: data engineering, exploratory data analysis, modeling, and ML implementation and operations. Item formats include direct knowledge and applied scenario items about which AWS service or approach fits. The samples below are editor-written illustrations of the published exam guide, not live exam items.
Data collection, storage, and transformation on AWS.
EDA and data visualisation.
Model selection, training, and tuning.
Deployment, monitoring, and MLOps.
Try these
Q1Which AWS service is the fully managed platform for building, training, and deploying ML models?
Answer:A
Amazon SageMaker is the fully managed ML platform covering the build-train-deploy lifecycle.
Q2Which approach is most appropriate for tuning a model’s hyperparameters automatically?
Answer:A
SageMaker Automatic Model Tuning automates hyperparameter optimisation.
Q3Which SageMaker feature monitors models for data drift after deployment?
Answer:A
SageMaker Model Monitor detects data drift and quality issues in production models.
Samples are editor-written illustrations of the published exam guide, not live exam items.
The big day
What should I expect on exam day?
The ML Specialty is taken at a Pearson VUE testing center or online proctored: 65 multiple-choice and multiple-response questions with 180 minutes, pass/fail at 750 on the 100-1000 scale, with results typically available within a few days. The core rules: bring valid photo ID for center delivery; for online proctoring complete the environment check. The exam is closed-book. After passing, the certification is valid for three years. The most useful exam-day habits: pace at about 2.5 to 3 minutes per question, flag and return to the scenario items, and answer every question (no penalty for guessing). If you do not pass, AWS requires a 14-day wait for the first retake. After the exam, plan the three-year recertification and consider the complementary ML Engineer – Associate credential for the ML career path.
Bring
- Valid photo ID (center) or proctoring-ready environment (online)
- AWS account credentials
- Arrival 15-30 minutes early
Leave at home
- Phone, smartwatch, and study materials
- Personal notes in the testing room
How the day runs
Rules in the room
- 65 questions, 180 minutes
- 750/1000 passing score
- Center or online proctored
- Valid 3 years, recertify
Afterwards.Passing the ML Specialty earns the AWS Certified Machine Learning – Specialty credential, the advanced AWS ML certification, valid 3 years.
Reference
What are the key facts about the AWS Certified Machine Learning - Specialty?
AWS Certified Machine Learning - Specialty is a certification credential; awarded by Amazon Web Services; the exam fee is $300; typical preparation is n/a; holders typically earn $140,000-$185,000.
| Credential | AWS Certified Machine Learning - Specialty |
|---|---|
| Abbreviation | AWS ML Specialty |
| Type | Certification |
| Profession | IT, Cloud & Cybersecurity |
| Specialty | Amazon Web Services |
| Awarded by | Amazon Web Services |
| Difficulty | Hard |
| Typical prep time | n/a |
| All-in cost | $300 |
| Typical salary range | $140,000-$185,000 |
| Delivery | Retired (31 March 2026) |
| Level | Specialty |
| Replacement | ML Engineer Associate (MLA-C01), GenAI Developer Pro (AIP-C01) |
| Validity | Valid 3 years, then recertification |
| Scope | National / Multi-state |
| Also known as | MLS-C01 |
Real questions
Frequently asked questions about the AWS Certified Machine Learning - Specialty
Can I still take MLS-C01?
No. The final test date was 31 March 2026. AWS directs candidates to the ML Engineer Associate (MLA-C01) and Generative AI Developer Professional (AIP-C01).
Is my MLS-C01 still valid?
If you passed before retirement, it remains valid through its original three-year expiry and is still recognised by employers.
Should ML engineers take MLA-C01?
Yes. MLA-C01 covers building, deploying and operating ML pipelines with SageMaker and is the default AWS ML credential going forward.
Is the study material still useful?
Largely yes. SageMaker, data preparation and deployment concepts remain central to current AWS ML exams.
Which AWS cert should I start with?
Cloud Practitioner (CLF-C02) for general cloud literacy or AI Practitioner (AIF-C01) for AI/ML context. Role-based candidates often go straight to Solutions Architect Associate (SAA-C03).
Do AWS certifications expire?
Yes, after three years. You recertify by passing the current version or a higher credential that refreshes it.
Are the exams hands-on?
AWS exams are scenario-based multiple choice and multiple response, not live-terminal. They test applied judgement rather than memorisation.
How are questions scored?
Each exam has 50 scored questions plus 15 unscored items used for future research; the 100-1000 scaled score maps difficulty across forms.
In short
Is the AWS Certified Machine Learning - Specialty worth it?
- Retired AWS ML specialty (31 March 2026)
- Covered data engineering, modelling, operations
- Replaced by MLA-C01 and AIP-C01
- Earned credentials remain valid to expiry
Same awarding body
What other credentials does Amazon Web Services award?
Worth comparing
How does the AWS Certified Machine Learning - Specialty compare with similar credentials?
Trust
Where does this information come from?
Everything above is taken from the awarding body's own published material. Fees, question counts and domain weights are revised regularly — check the official page before you pay.
- Amazon Web Services Awarding body
- Official AWS Certified Machine Learning - Specialty exam page Exam page
Research confidence: high · Last reviewed 2026-08