Applied AI · Expert tier
AWS Certified Machine Learning - Specialty (MLS-C01)
AWS Certified Machine Learning - Specialty (MLS-C01) is a expert-tier Applied AI credential from Amazon Web Services. The exam runs $300 and covers data engineering for ml: feature engineering, data preparation, and pipeline design. Most candidates prepare for around 150 hours and pair the credential with a portfolio of platform-aligned work.
Exam fee
$300
Prep time
150h
Duration
180 min
Level
Expert
Where this credential sits
AWS Certified Machine Learning - Specialty (MLS-C01) sits at the expert tier. The credential targets practitioners with multi-year platform depth who are consolidating senior or staff-track recognition.
What this certification covers
- Data engineering for ML: feature engineering, data preparation, and pipeline design
- Exploratory data analysis and modeling approaches across classical and deep learning
- ML model deployment, monitoring, and lifecycle management on AWS
- ML implementation across SageMaker, AWS Glue, EMR, Kinesis, and adjacent services
- Security, cost, and performance considerations for production ML workloads
- Operationalization, including model versioning, A/B testing, and shadow deployment
Who should pursue this
- ML engineers consolidating production AWS ML experience into a senior credential
- Data scientists moving into engineering roles on AWS infrastructure
- Solutions architects with an ML-heavy customer portfolio
Prerequisites
- No formal prerequisites
- AWS recommends 1-2 years of hands-on experience developing, architecting, or running ML workloads on AWS
- Strong Python plus SQL fluency and working knowledge of classical ML and deep learning
Cost breakdown
Exam
$300
Training
Free to $2000
Recertification
Every 3y
Total first-year cost runs from $300 (exam-only with free self-study) up to $2300 with paid instructor-led training. Recertification every 3 years.
Pricing reflects publicly listed vendor pricing as of April 2026. Verify current pricing at the Amazon Web Services certification page before registering.
Difficulty assessment
Study hours
150h
Exam format
Multiple choice and multiple response
Passing score
Approximate 750/1000 scaled score
Heavy preparation load at roughly 150 hours of focused study. Most candidates with active cloud and ML engineering background prepare across 8 to 14 weeks. Candidates without prior platform experience should expect closer to 16 weeks. Pass rates are not officially published by Amazon Web Services, so plan against the study-hour estimate rather than guessing at exam difficulty from forum threads.
Full exam format: Multiple choice and multiple response. Total seat time: 180 minutes.
Roles this credential supports
Alternatives and adjacent credentials
- AWS Certified Machine Learning Engineer Associate (newer Associate-tier alternative)
- Google Cloud Professional Machine Learning Engineer (vendor-equivalent for GCP-centric teams)
- Azure AI Engineer Associate (Microsoft equivalent for Azure-centric teams)
AWS Certified Machine Learning - Specialty (MLS-C01) questions and answers
What is the AWS Certified Machine Learning - Specialty (MLS-C01)?
AWS Certified Machine Learning - Specialty (MLS-C01) is a expert-tier Applied AI credential from Amazon Web Services. The exam runs $300 and covers data engineering for ml: feature engineering, data preparation, and pipeline design. Most candidates prepare for around 150 hours.
How much does the AWS Certified Machine Learning - Specialty (MLS-C01) cost?
The exam itself is $300 as of April 2026. Training adds Free to $2000, with free self-study options available for most cloud-platform credentials. Recertification every 3 years. Verify current pricing at the Amazon Web Services website before registering.
Who should pursue the AWS Certified Machine Learning - Specialty (MLS-C01)?
ML engineers consolidating production AWS ML experience into a senior credential. Data scientists moving into engineering roles on AWS infrastructure The credential is most useful when paired with a real portfolio of work on the platform.
How does the AWS Certified Machine Learning - Specialty (MLS-C01) compare to alternatives?
Practitioners typically choose between AWS Certified Machine Learning - Specialty (MLS-C01) and AWS Certified Machine Learning Engineer Associate (newer Associate-tier alternative) or Google Cloud Professional Machine Learning Engineer (vendor-equivalent for GCP-centric teams). Pick the credential matched to your target employer's primary cloud or governance stack. Studying for one credential builds 60 to 70 percent of the conceptual ground for the others.
Is the AWS Certified Machine Learning - Specialty (MLS-C01) worth it for a job search?
Practitioner-tier credentials carry hiring weight when the resume is otherwise platform-aligned. The AWS Certified Machine Learning - Specialty (MLS-C01) validates that the candidate has worked at production depth on Amazon Web Services tooling, not read about it.
Cert-prep add-on for this credential
Methodology
This guide reflects DecipherU's standard certification intelligence workflow grounded in official certifying body documentation, publicly listed exam pricing, and independent practitioner sourcing. All details verified against the Amazon Web Services certification page on 2026-05-22.
Certification details are sourced from official certifying body websites. Verify current pricing, exam format, and requirements directly with the certifying organization before making decisions. DecipherU is not affiliated with any certifying body.