AWS AI Certifications 2026: Complete Guide to AI Practitioner, ML Engineer & Generative AI Developer

If you're learning AI or starting your tech career, earning an AWS AI certification can be a powerful accelerator. The portfolio now spans foundational AI literacy, hands-on machine learning engineering, and advanced generative AI development—so you can pick the level that fits your goals and grow from there. In this complete guide, we'll break down every current AWS AI certification, what's changing in 2026, how to prepare efficiently, what it costs, and how to choose the right path for you.

As of Monday, February 23, 2026, AWS's AI certification lineup includes AWS Certified AI Practitioner (Foundational), AWS Certified Machine Learning Engineer – Associate, and AWS Certified Generative AI Developer – Professional (in beta), plus the closely related Data Engineer – Associate. The older Machine Learning – Specialty is retiring on March 31, 2026. We'll unpack each one and show you how to turn your study time into a career boost.

What's in the AWS AI Certification Portfolio (2026)

AWS has shaped a role-based path that mirrors how modern AI teams work—start with literacy, level up to applied engineering, then specialize in generative AI.

AWS Certified AI Practitioner (AIF-C01) – Foundational

For students, business analysts, PMs, and early-career technologists who need AI/ML and generative AI fluency, responsible AI concepts, and how AWS services fit together. See the official exam guide for scope and objectives.

AWS Certified Machine Learning Engineer – Associate (MLA-C01)

For hands-on builders who train, deploy, and operate ML on AWS. Emphasizes SageMaker-powered MLOps, pipelines, registries, monitoring, and security.

AWS Certified Generative AI Developer – Professional (AIP-C01)

For experienced developers delivering production-grade GenAI apps: retrieval-augmented generation (RAG), agents, guardrails, and optimization. Currently in beta with defined format and duration.

AWS Certified Machine Learning – Specialty (MLS-C01) – Retiring

Last day to take the exam is March 31, 2026. If you earn it by then, it remains valid for three years.

Closely Related: AWS Certified Data Engineer – Associate (DEA-C01)

Not branded "AI," but immensely useful for robust data foundations, pipelines, and governance that power ML and GenAI.

Actionable takeaway:

If you're brand new to AI and cloud, aim for AI Practitioner. If you already script, ship, or support models on AWS, aim for ML Engineer – Associate. If you're building GenAI apps in production or preparing to, target Generative AI Developer – Professional (or join beta when available in your region).

Certification Comparison at a Glance

Note: MLS-C01 (Machine Learning – Specialty) retires March 31, 2026 and is not included above. If you're considering it, schedule your exam immediately.

Why These Certifications Matter (Purpose and Unique Value)

AWS restructured its AI certification path to match real roles on AI teams:

AWS Certified AI Practitioner (AIF-C01)

Unique value: Gives you a common language for AI/ML and GenAI—with responsible AI, governance, and security baked in—so you can evaluate use cases, talk to engineers, and make informed decisions. Great finishing move after a cloud fundamentals course.

AWS Certified ML Engineer – Associate (MLA-C01)

Unique value: It's laser-focused on getting models into production with SageMaker AI. You'll cover data prep at scale, training/tuning, deployment patterns (real-time, serverless, batch), MLOps automation (Pipelines, Model Registry), monitoring/drift, and IAM/security.

AWS Certified Generative AI Developer – Professional (AIP-C01)

Unique value: Goes deep on production GenAI: Bedrock, RAG patterns and vector stores, Knowledge Bases, Agents, safety/guardrails, validation, and cost/perf optimization under real constraints.

Machine Learning – Specialty (MLS-C01)

Unique value: A rigorous, end-to-end ML credential covering modeling depth and operations. It's being sunset to streamline the path into associate and professional, role-based badges.

Data Engineer – Associate (DEA-C01)

Unique value: Modern AI hinges on clean, governed, timely data. This exam validates the ingestion, storage, transformation, and governance skills your ML/GenAI workloads will depend on.

Actionable takeaway:

If your day-to-day is more "decide and communicate," go AI Practitioner. If it's "build and operate," go ML Engineer – Associate. If it's "compose FMs into apps and scale safely," go GenAI Developer – Professional.

Who Can Take Them? (Eligibility and Recommended Background)

There are no formal prerequisites to sit AWS exams. AWS does publish recommendations so you can self-select the right level.

• AIF-C01: Ideal with up to ~6 months of exposure to AI/ML on AWS and basic cloud knowledge.

• MLA-C01: About 1+ year implementing ML on AWS (roles like backend engineer, DevOps, data engineer, data scientist using SageMaker).

• AIP-C01: About 2+ years building production apps plus ~1 year of implementing GenAI solutions.

• MLS-C01: About 2+ years developing/architecting ML or deep learning on AWS (retiring Mar 31, 2026).

• DEA-C01: About 2–3 years data engineering and 1–2 years hands-on AWS.

Actionable takeaway:

Use AWS's recommended background to choose the right starting point—then calibrate your study load accordingly.

Exam Structure, Topics, and Scoring

Here's what to expect in content and format. Always verify your target exam's page for the latest numbers.

AWS Certified AI Practitioner (AIF-C01)

Focus: Core AI/ML and GenAI fundamentals (foundation models, prompts, safety), responsible AI, security/compliance/governance, and basic AWS service awareness.

Format: 50 scored + 15 unscored items; scaled score 100–1000; passing score 700. Typical duration ~90 minutes; available via Pearson VUE test centers or online proctoring (check your region's options).

Actionable study move:

Build a one-page crib sheet of "which AWS service when" for common AI/ML/GenAI scenarios. It helps you answer scenario questions fast.

AWS Certified Machine Learning Engineer – Associate (MLA-C01)

Domains and weights:

• Data preparation (28%)

• Model development (26%)

• Deployment and orchestration (22%)

• Monitoring, maintenance, and security (24%)

Format: 50 scored + 15 unscored (65 total); scaled score 100–1000; passing score 720; 130 minutes.

Actionable study move:

Build an end-to-end SageMaker project: feature engineering → training → hyperparameter tuning → Model Registry → CI/CD to endpoints → batch/real-time inference → monitoring with alerts. You'll internalize most of the exam's muscle memory in one project.

AWS Certified Generative AI Developer – Professional (AIP-C01) – Beta

Domains and weights:

• Foundation model integration and data/compliance (31%)

• Implementation and integration (26%)

• AI safety, security, and governance (20%)

• Operations efficiency and optimization (12%)

• Testing, validation, and troubleshooting (11%)

Format (beta): 85 questions; 205 minutes; languages English and Japanese during beta. Scaled scoring 100–1000; AWS lists 750 as the professional/specialty passing standard when GA.

Actionable study move:

Ship two production-style Bedrock builds:

• A RAG app with Knowledge Bases, vector embeddings, and guardrails

• An Agent workflow that orchestrates tools and enforces safety

Measure latency, cost per request, and quality (evaluation criteria). Practice trade-offs.

AWS Certified Machine Learning – Specialty (MLS-C01) – Retiring

Domains and weights: Data engineering (20%); Exploratory Data Analysis (24%); Modeling (36%); Implementation and operations (20%).

Format: 65 questions; 180 minutes. Last day to take the exam is March 31, 2026.

Actionable study move:

If you're set on MLS-C01, schedule it soon. If timings won't work, pivot to MLA-C01 now and layer AIP-C01 after you gain GenAI delivery experience.

AWS Certified Data Engineer – Associate (DEA-C01)

Domains and weights: Ingestion/transform (36%); Data store management (26%); Operations/support (22%); Security/governance (16%).

Format/scoring: Scaled 100–1000; passing score 720.

Actionable study move:

Build a governed data lake with S3, Glue, Lake Formation, and demonstrate lineage, access controls, and incremental pipelines that feed ML/GenAI apps.

Costs, Vouchers, Retakes, and Validity

Budget and rules influence your plan—here's what to know.

Standard exam fees:

• Foundational: $100

• Associate: $150

• Professional and Specialty: $300

Local currencies apply.

AIP-C01 beta pricing (Professional level): $150 during beta.

Passing score examples:

AIF‑C01: 700; MLA‑C01: 720; AIP‑C01: 750 (professional benchmark when GA). See each exam guide for the precise standard.

Validity and recertification:

Certifications are valid for three years. If you don't pass, there's a 14‑day wait before a retake.

Savings after you pass:

You receive a 50% discount voucher toward your next AWS exam and a digital badge via Credly.

Actionable budgeting tip:

Chain your exams to maximize the 50% voucher. For example: pass AIF‑C01 → use 50% off MLA‑C01 → pass MLA‑C01 → use 50% off AIP‑C01.

The ROI Question: Do AWS AI Certifications Pay Off?

Certifications are only one part of your portfolio (projects and internships matter too), but the data shows real benefits—both in career advancement and salary.

What you can earn with AWS AI certifications (2026 data):

• AWS Certified AI Practitioner (AIF‑C01): Entry-level AI roles typically pay $88,000–$117,000. As you build experience, AI Engineer roles with these skills average $140,000–$165,000.

• AWS Certified ML Engineer – Associate (MLA‑C01): Certified AWS ML Engineers earn $120,000–$160,000 on average, with senior roles pushing past $200,000. ZipRecruiter (Feb 2026) reports $128,769 as the national average, with top earners hitting $178,000+.

• AWS Certified Generative AI Developer – Professional (AIP‑C01): As a newer, production-focused credential, GenAI specialists are already commanding $140,000–$184,000+, with demand growing rapidly.

• AWS Certified Data Engineer – Associate (DEA‑C01): Data engineers with AWS cloud expertise typically earn $110,000–$150,000 depending on experience and location.

Beyond salary, Pearson VUE's 2025 Value of IT Certification report found that 63% of candidates received or expected a promotion after certifying, 32% reported salary increases, and 82% gained confidence to pursue new roles—consistent with multi‑year findings.

CIO's review of industry studies notes employers see meaningful productivity and problem‑solving gains from certified staff—especially where skills must be validated quickly for strategic projects like GenAI.

AWS's expanded, role-based AI portfolio signals strong enterprise demand for production GenAI and MLOps skills, reinforcing the value of these specific credentials.

Actionable career move:

Pair your certification with a public portfolio: short write-ups of 2–3 AWS projects (with architecture diagrams and metrics). Hiring managers weigh proof of impact highly.

What You'll Actually Do With These Skills (Real-World Scenarios)

AI Practitioner (AIF‑C01)

Evaluate AI/ML/GenAI fit for business problems, scope responsible AI requirements, and select the right AWS services for prototypes. You'll partner effectively with engineers and compliance teams.

ML Engineer – Associate (MLA‑C01)

Build repeatable pipelines with SageMaker Pipelines; track models in Model Registry; deploy to serverless real-time or batch endpoints; monitor drift and bias with Model Monitor/Clarify; enforce IAM policies and VPC controls.

GenAI Developer – Professional (AIP‑C01)

Ship Bedrock-backed applications:

• Retrieval-Augmented Generation with Knowledge Bases and vector stores

• Multi-step Agents that orchestrate tools safely

• Guardrails to enforce privacy, toxicity, and policy constraints

• Evaluation loops and cost/latency/quality tuning in production

Data Engineer – Associate (DEA‑C01)

Ingest data (Kinesis/DMS), transform with Glue, govern with Lake Formation, and deliver curated layers to fuel ML training jobs and GenAI retrieval corpora—securely and cost‑effectively.

Actionable practice idea:

Build a mini "customer support copilot":

• DEA‑style pipelines to curate knowledge

• MLA‑style MLOps to productionize a classifier for routing

• AIP‑style Bedrock RAG + guardrails to answer questions

• Instrument metrics: first‑token latency, cost per 1K tokens, grounding accuracy

The Best Study Strategy (Step-by-Step)

Step 1: Print the exam guide

Highlight objectives, in‑scope/out‑of‑scope services, and sample questions. Every hour of study should map to an objective.

Step 2: Use AWS Skill Builder Exam Prep Plans

Follow the curated path: foundational videos → hands-on labs → official question sets → pretests. Add SimuLearn for scenario-based practice.

Step 3: Build one anchor project

A single, end-to-end build (SageMaker pipeline or Bedrock RAG+Agent) compresses weeks of theory into a few high-retention sessions. Commit architecture diagrams and trade‑off notes to a repo.

Step 4: Close the gaps with targeted reading

For MLA‑C01: endpoints, Pipelines, Model Registry, Model Monitor/Clarify, and security/IAM.

For AIP‑C01: RAG patterns, vector databases, Knowledge Bases, Agents, guardrails, and evaluation/observability.

Step 5: Simulate exam conditions

Block 90–130–205 minutes (per exam) with no interruptions. Practice question pacing; review rationales; repeat weak areas.

Step 6: Book the exam strategically

Schedule within 1–2 weeks after your strongest mock. Momentum matters.

Actionable timebox:

• AIF‑C01: 2–4 weeks part-time

• MLA‑C01: 6–10 weeks with one full project

• AIP‑C01: 8–12+ weeks with two production‑style builds

Which Certification Should You Take First? (Decision Guide)

You're early career, PM/BA, or leadership:

Start with AIF‑C01 to build AI/ML/GenAI literacy and governance instincts. Then choose ML Engineer – Associate if you'll build/operate, or Data Engineer – Associate if you'll own pipelines.

You're a software engineer moving into AI:

Aim for ML Engineer – Associate to master MLOps fundamentals; then take GenAI Developer – Professional to prove production GenAI skills.

You're an experienced ML practitioner:

Decide quickly whether to sit the ML – Specialty before Mar 31, 2026. Otherwise, focus on ML Engineer – Associate and then GenAI Developer – Professional for the current, role‑based path.

Actionable planning tip:

Use "voucher chaining" to reduce total costs: pass AIF → 50% off MLA → 50% off AIP.

Policies to Know: Retakes, Validity, Scheduling

• Validity: Certifications are valid for 3 years.

• Retakes: If you don't pass, there's a 14‑day waiting period before your next attempt.

• Testing options: Pearson VUE test centers or online proctoring (check availability on your exam's landing page).

• Pricing: Foundational $100; Associate $150; Professional/Specialty $300. Watch for betas like AIP‑C01 at reduced cost.

Actionable scheduling tip:

Book morning slots if possible—most candidates test better earlier in the day and you'll have fewer work interruptions.

FAQs

Q1: Is there a prerequisite path (e.g., must I take AI Practitioner before ML Engineer – Associate)?

A1: No. AWS has no formal prerequisites. You can sit any exam. Use the recommended experience in each exam guide to self-select where to start.

Q2: How long are the exams and how many questions do they have?

A2: Typical formats:

• AIF‑C01: 50 scored + 15 unscored; around 90 minutes.

• MLA‑C01: 50 scored + 15 unscored (65 total); 130 minutes.

• AIP‑C01 (beta): 85 questions; 205 minutes.

• MLS‑C01: 65 questions; 180 minutes.

Always confirm your exam's landing page before scheduling.

Q3: What score do I need to pass?

A3: AWS uses scaled scoring (100–1000). Passing standards are listed in each exam guide (e.g., AIF‑C01 700; MLA‑C01 720; professional-level exams commonly 750). Check your exam guide for the exact standard.

Q4: How long do AWS certifications stay valid?

A4: Three years from the date you pass. AWS offers a 50% voucher after you pass to help you recertify or pursue your next exam.

Q5: What if I want the Machine Learning – Specialty?

A5: The final testing date is March 31, 2026. If you can't sit by then, pivot to ML Engineer – Associate (current role-based equivalent for operations) and later GenAI Developer – Professional for advanced GenAI.

Q6: How much can I earn with an AWS AI certification?

A6: Salaries vary by role and experience, but AWS-certified ML Engineers typically earn $120,000–$160,000, with senior roles exceeding $200,000. AI Practitioner holders in entry-level AI roles average $88,000–$117,000, while GenAI specialists are commanding $140,000–$184,000+. Location, industry, and project experience all influence your final number.

Conclusion

AWS now offers a clear pathway for AI learners and builders: start with AI literacy (AIF‑C01), move into production ML engineering (MLA‑C01), and graduate to advanced generative AI development (AIP‑C01). If your work is more data‑centric, add DEA‑C01 to strengthen pipelines and governance. Combine certifications with a visible project portfolio, and you'll stand out in internships, junior roles, and early promotions.