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Compare ML engineering instruction and version-specific exam preparation. Confirm language, course scope, starting knowledge and selected costs.

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ML work instruction and exam preparation

For building and operating ML systems, compare the named engineering course. For the English beta, use C02 objectives to identify the additional foundation-model, retrieval and agent skills you need. Course attendance and exam readiness are different.

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Machine Learning Engineering on AWS

Course outline reference · Observed 2026-10-08

Official 2025 PDF: intermediate, three catalogue days. It describes data preparation, model training and tuning, deployment, security, pipelines and drift monitoring, with seven named lab activities.

Its recovered outline shows no Bedrock, RAG or agentic instruction. Complete MLA-C02 alignment and the selected delivered revision remain unverified.

2025 outline recommends basic ML concepts, working Python and common data science libraries, and cloud/AWS familiarity. Git experience is beneficial, not required. Recommended course background, not a mandatory prior-certification gate or universal beginner fit.

Selected tuition, seat, contact time, lab entitlement, usage charges and feedback are unknown. Lab titles do not establish access to exercises.

Read scope, background and purchase limits

Official AWS 2025 PDF, physical pages 1–3: intermediate, three catalogue days, presentations, labs, demonstrations and group exercises. Seven lab titles describe data preparation, processing, model training, tuning, traffic shifting, pipelines/model registry and drift monitoring; exercise instructions and personal entitlement were not inspected. No Bedrock, retrieval-augmented generation or agentic instruction is shown; complete MLA-C02 mapping is not established. Selected delivered revision, seller/seat, tuition/taxes, contact hours, personal effort, practice permissions/usage/reset/support, feedback and voucher remain unknown.

Official AWS 2025 PDF outline; selected delivered edition and complete MLA-C02 alignment unknown. Selected tuition, seat, contact time, permitted practice and feedback remain unverified; a dated outline is not a current offer.

MLA-C02 current exam objectives

Exam objective reference · Observed 2026-10-08

Public C02 objectives cover both traditional ML and generative AI. This is an exam reference, not a course, a beta pass predictor or personal readiness assessment. Non-English C01 preparation still uses its own objectives.

Read scope, background and purchase limits

Current MLA-C02 public exam objective reference includes traditional ML and foundation models, retrieval and agents. Exam scope does not establish taught course coverage. Standard guide format and threshold must not be applied as a promise to the longer beta.

This public exam outline is a scope reference, not purchased instruction or practice access. Exam registration, courses and permitted practice have separate costs.

AWS MLA-C02 official preparation portal

Official research reference · Observed 2026-10-08

The issuer links this C02 preparation portal. Its destination returned a script-only shell; individual lessons, effort, price and practice access were not recovered. Do not assume the full plan is free.

Read scope, background and purchase limits

Issuer-linked MLA-C02 preparation portal returned a browser-script-only shell. Individual lesson outlines, effort, price, account/lab entitlement and feedback were not recovered; pointer identity is not a free training package.

This issuer preparation-portal pointer is not a recovered course or an all-free package. Individual products, price and personal practice access remain unverified.

AWS's catalogue separately lists C01 exam preparation. Do not relabel it as C02 instruction. The credential's experienced target profile is separate from the engineering course's recommended background.

For official instruction, match the exact seller and delivering organization in the AWS Training Partner directory. Delivery, digital training and resale are distinct capabilities; a logo or similarly named company is insufficient. For the same official course and revision, compare lab entitlement, instructor feedback, timetable, recordings, access expiry and total cost.

Official course evidence · checked 2026-10-15 UTC

Course outline, practice and provider options

Machine Learning Engineering on AWS

Intermediate work instruction · 3 catalogue days · 2025 copyright outline · preparation p1; modules/labs pp2–3

Certification skills describe assessment. This outline describes teaching; ask the seller which revision, exercises and support you receive.

Published course modules (13 topics)
  1. Course orientation
  2. ML on AWS and responsible use
  3. ML problem analysis
  4. Data processing and storage
  5. Feature preparation
  6. Modeling choices
  7. Model training
  8. Evaluation and tuning
  9. Deployment strategies
  10. ML resource security
  11. MLOps and automated delivery
  12. Model and data-quality monitoring
  13. Course review

Recommended course background: AWS recommends ML fundamentals, working Python and NumPy/Pandas/Scikit-learn, plus AWS/cloud familiarity. Git is beneficial rather than required. This recommended course background is separate from the exam target profile.

Published practice and lab work
  • Prepare data with Data Wrangler/EMR, then run SageMaker Processing workflows.
  • Train, tune and deploy models, including a traffic-shifting exercise.
  • Use pipelines/model registry and monitor drift; seven lab descriptions are published.

Edition and access: The 2025 outline shows no Bedrock, RAG or agentic instruction; it does not establish complete MLA-C02 coverage. Confirm the exam language/version and delivered course edition. New Horizons shows December 1–3 at 6:30 AM–2:30 PM PST while its 2026 structured instance says 9:30 AM–5:30 PM PST; verify the timetable and seat.

Start with AWS classroom instruction and AWS Skill Builder digital learning. A subscription, exam review, standalone course and course with Jam are different products. Check the exact entitlement and account requirements.

New Horizons’ AWS catalog calls it a Select Training Partner. This is a provider statement; confirm the selected legal seller and region with AWS. A generic partner claim or Marketplace listing does not verify every course package.

New Horizons

CourseID 3606024E: live virtual/private ML work instruction. Exam voucher inclusion is not stated; confirm it separately from tuition and do not treat this as complete C02 exam preparation.

Check the exact course reference

Dated instruction prices

These prices describe named packages at the recorded dates. Confirm the checkout total, teaching format, lab license, feedback, vouchers and cancellation terms. A calendar is not a confirmed seat. A practical AWS course is separate from exam registration and complete blueprint coverage.

1 dated option shown. Prices are in USD; check the date and package before using one in a budget.

Live instructor-led courses

New Horizons

AWS Certified Machine Learning Engineer - Associate

$2,025 per course

Machine Learning Engineering on AWS (CourseID 3606024E)

Delivery
virtual live
Exam
Inclusion to confirm
Buyer
Commercial
Observed

USD 2,025 per course, three advertised catalogue days of virtual instructor-led instruction. Private-group quote is separate. Described labs do not establish selected account/license, usage charges, expiry, recordings or individual reviewed work. Confirm delivered revision, contact hours, tax, cancellations and seat. Voucher inclusion unknown. The 2025 issuer outline does not establish full MLA-C02/GenAI coverage; confirm language, exam version and course edition. Visible Dec 1-3 at 6:30 AM-2:30 PM PST conflicts with structured Dec 1-3, 2026 at 9:30 AM-5:30 PM PST; confirm complete timetable.

Check this offer on New Horizons ↗

Browse all dated training prices. Compare the exact course and complete package rather than treating a format price gap as like-for-like savings.

Research, practice and the private brief are optional. A request permits Learnerside to reply; provider sharing needs separate consent naming the recipient.

Certification details change. Always confirm final pricing, availability, and credential terms on the official provider page linked in the sources below before you pay for anything.

Decide which exam you can actually take

AWS began English MLA-C02 beta delivery on September 29, 2026. The English MLA-C01 exam ended September 28. AWS still offers C01 in Japanese, Korean, and Simplified Chinese until C02 general availability. The credential has not been retired; this is an exam-version transition. On its credential page, AWS calls the new version MLA-C02 but displays ME1-C02 as the beta booking code. Confirm both the version and bookable code in your AWS Certification Account before buying an exam or class.

  • You need an English exam soon and already build ML systems on AWS: Read the C02 guide, then consider the beta only if its current scope and longer format fit your preparation. The beta is not a shortcut to an assured pass or immediate result.
  • You need Japanese, Korean, or Simplified Chinese: C01 remains the listed exam for those languages during beta. Use C01 objectives and confirm the actual appointment before buying version-specific materials.
  • You can wait or are new to ML engineering: You can compare instruction for a small ML workflow without buying an exam first. Do not buy an old C01 course on the assumption that its labs cover C02's foundation-model, retrieval, and agentic-workflow additions.

AWS sources disagree on the C02 general-availability date: its September 1 announcement says January 14, 2027, while the current credential page still marks GA registration and delivery TBD. Treat January 14 as an announced plan, not a confirmed appointment. Check the booking system and AWS page before relying on it or the corresponding C01 retirement date.

Who is this for, and how difficult is it?

AWS's C02 target profile describes at least one year using SageMaker AI, Bedrock, and related services for ML engineering, plus at least one year in a related technical role. It expects both traditional ML and generative-AI experience. This is recommended readiness background, not a formal prior-certification requirement stated on the checked AWS pages. The credential does not, by itself, prove that someone can operate a production ML system.

An optional fictional work sample: a team needs to detect faulty products from invented sensor records. Sketch data validation, a model choice, evaluation, deployment, a failure response, drift monitoring and access controls. Explain whether a foundation model is useful and what remains to test in an authorized isolated lab. Use invented data and a diagram; no cloud account or spending is needed for this paper exercise. It is a Learnerside exercise, not an AWS exam item or a pass predictor. You can ask for instruction immediately.

What the current C02 guide covers

These are percentages of scored C02 content, not an estimate of how many questions a particular beta candidate will see. AWS's C01-to-C02 comparison shows that the domain structure remains four parts, but foundation models, retrieval-augmented generation, agents, data protection, and AI operations appear more explicitly in C02. Do not use C01's 26% model-development and 22% deployment weights for C02.

C02 content domainScored weightWork to compare with instruction
Data Preparation for ML and AI28%Can you prepare varied data, validate quality, and protect sensitive inputs?
ML Model and Foundation Model Development24%Can you choose, tune, and evaluate an approach against quality, latency, and cost needs?
Deployment and Orchestration of ML and AI Workflows24%Can you deploy repeatably and explain a retrieval or agent workflow and its failure handling?
Operating, Monitoring, and Securing ML and AI Solutions24%Can you observe model behavior and spending, restrict access, and respond to failures?

Use the full official guide for detailed tasks and service scope. The guide describes 50 scored and 15 unscored questions and a 720 scaled-score threshold for the standard exam, but it expressly qualifies its pass/fail discussion for beta. Do not apply that 65-question format or a promised 720 beta pass mark to the 85-question beta.

Exam cost versus training cost

As checked September 29, AWS lists the English C02 beta at 170 minutes, 85 multiple-choice or multiple-response questions, and USD 75. It lists the remaining-language C01 at 130 minutes, 65 questions, and USD 150. Both are offered through Pearson VUE at a test center or online with proctoring. The C02 general-availability price is not verified here. These issuer list prices are exam fees, not class prices or guaranteed regional checkout totals; taxes, currency, retakes, lab usage, and training can add cost. AWS says beta results are typically available within five business days, not instantaneously.

You can compare the public C02 guide and the AWS exam-prep path. The credential page also points to official practice questions, a pretest, digital courses, labs, and a practice exam. Access and price of individual Skill Builder resources were not verified; some labs or AWS services may cost money even when a reading resource is free. Any later cloud practice needs separate permission, usage-cost controls and cleanup; it is optional.

Paid instruction is worth considering when you need hands-on feedback on data pipelines, deployment, monitoring, or team scheduling, not just a repeat of the guide. No dated C02 provider seat, course authorization, tuition, voucher, recording, refund, or beta-specific attempt term has been verified for this page. Ask any seller to name the legal seller and delivery organization, C02 versus C01 courseware revision, dates and time zone, learner-operated labs, AWS usage charges, access period, instructor feedback, exam voucher and retake terms, and what happens if the exam changes again. Compare the total per learner, not a teaser tuition figure.

Roles, pay, and renewal

AWS names ML engineer, MLOps engineer, LLMOps engineer, data engineer, backend developer, and data scientist as related roles. These are work contexts, not jobs granted by a certificate. Occupation-level wage data cannot isolate MLA holders or prove a certification salary premium; this page makes no salary promise.

AWS lists three-year credential validity for both versions. Its recertification table lists passing the latest Machine Learning Engineer Associate exam or Generative AI Developer Professional exam as three-year renewal paths for an active credential. A 50% exam discount may exist in an individual AWS Certification Account; do not deduct it from a quote unless you have it and its terms apply.

Next action

You can ask about ML instruction now. For version-specific exam preparation, choose your language and likely month, verify the actual code and availability, and compare the C02 guide or the still-applicable C01 guide for your language. If guided practice would close those gaps, describe your timing and format or draft an employer approval brief. A request does not authorize sharing your details with a provider; that requires separate consent naming the recipient.

Sources and limits

Learnerside is independent of AWS. This page paraphrases issuer material; it does not verify a provider offer or promise an exam result, job, salary, or future exam date.