MLA-C01 tests whether you can choose and operate AWS machine learning services inside a working pipeline. MLA-C02 keeps that foundation, then adds foundation models, RAG, agents, embeddings, and GenAI security. If your study material predates the September 2026 beta transition, the problem is not that every page is useless. Some important pages are missing.
The verdict
MLA-C02 is a broader version of the same machine learning engineer exam. AWS has kept the traditional foundation: data preparation, model development, deployment, monitoring, security, SageMaker, and MLOps fundamentals still matter.
The main change is scope. C02 treats generative AI as part of the machine learning engineer's job, adding Amazon Bedrock, foundation model selection and customization, retrieval-augmented generation, agents, vector databases, embeddings, prompt engineering, evaluation, and GenAI cost and security controls.
MLA-C01 remains current in English through September 28, 2026. MLA-C02 beta delivery begins September 29, 2026. AWS describes the update as a response to the broader ML engineer role, which now includes foundation models, agentic AI, and large language model workloads.
Quick changelog
- Added: Bedrock foundation models, fine-tuning, RAG, agents, embeddings, vector databases, prompt engineering, foundation model evaluation, and GenAI monitoring.
- Expanded: Data preparation now includes multimodal data, RAG documents, anonymization, and training-data integrity.
- Reweighted: Model development falls from 26% to 24%. Deployment and orchestration rises from 22% to 24%.
- Renamed: The domains use broader ML and AI language in C02.
- Removed or reduced: SageMaker Neo edge optimization, SageMaker BYOC, EFS and FSx model-training loading, and some infrastructure-monitoring and capacity-troubleshooting wording.
- Unchanged: Data preparation remains 28%. Operations, monitoring, and security remains 24%. The credential remains valid for three years.
Quick Facts
The following table separates confirmed C01 details from C02 beta information.
| Item | MLA-C01 | MLA-C02 |
|---|---|---|
| Status | Current in English through September 28, 2026 | Beta begins September 29, 2026 |
| Price | $150 USD | $75 USD beta pricing |
| Duration | 130 minutes | 170 minutes |
| Questions | 65 total, including 50 scored and 15 unscored | 85 questions |
| Format | Multiple choice and multiple response | AWS's beta overview and current exam guide document the same item-type family |
| Passing score | 720/1000 | AWS has not published a beta passing score |
| Validity | 3 years | The credential remains in the same Associate certification family |
| Testing | Pearson VUE test center or online proctored exam | Beta delivery details follow AWS's published beta information |
| Retake policy | AWS retake policy applies | AWS retake policy applies |
| Domain structure | Four domains | Four renamed and rebalanced domains |
MLA-C01 gives you 130 minutes for 65 questions, including 50 scored questions and 15 unscored questions. The passing score is 720 out of 1000. The current format uses multiple choice and multiple response items.
AWS lists 170 minutes and 85 questions for the MLA-C02 beta. The beta price is $75 USD. AWS has not published a C02 beta passing score or final non-beta commercial details, so do not treat the beta information as a permanent specification.
AWS offers C01 through Pearson VUE testing centers and online proctoring. The AWS retake policy applies, but the exact current waiting interval is not included in the available exam information.
Why AWS changed the exam
AWS says the ML engineer role has broadened. Traditional model training and deployment remain part of the job, but production teams now work with foundation models, retrieval systems, agents, and large language model workloads.
That is why C02 adds Bedrock, RAG, agents, foundation model evaluation, and GenAI security. This is not just a cosmetic rename. Someone who knows SageMaker endpoints but cannot reason about a Bedrock knowledge base or retrieval evaluation has a genuine gap under the updated scope.
The service list also reflects cleanup. AWS removed SageMaker Neo edge optimization and SageMaker BYOC from the comparison, along with EFS and FSx model-training loading and some infrastructure-monitoring and capacity-troubleshooting wording.
The mental model is still straightforward: move from data to model to production, then operate the system when the data, model, cost, performance, or security requirements change. The technology is newer. The engineering loop is familiar.
Where the weights changed
| Domain | MLA-C01 | MLA-C02 | Change |
|---|---|---|---|
| Data Preparation for ML, now Data Preparation for ML and AI | 28% | 28% | No change |
| ML Model Development, now ML Model and Foundation Model Development | 26% | 24% | Down 2 points |
| Deployment and Orchestration of ML Workflows, now ML and AI Workflow Orchestration | 22% | 24% | Up 2 points |
| ML Solution Monitoring, Maintenance, and Security, now Operating, Monitoring, and Securing ML and AI Solutions | 24% | 24% | No change |
The weight change is modest. The content change is not.
Domain 1 remains the largest section, so data work deserves the most study time. C02 adds diverse multimodal data, embeddings, advanced text preprocessing, vector databases, RAG document preparation, anonymization, and data integrity for foundation model fine-tuning.
Domain 2 gets slightly smaller but considerably broader. Model development now includes Bedrock foundation model selection, fine-tuning and customization, prompt engineering, retrieval optimization, human evaluation, NLP metrics, LLM-as-a-judge approaches, and RAG monitoring.
Domain 3 grows from 22% to 24%. The additions include foundation model deployment, external-model deployment, agents. They also include Bedrock knowledge bases, retrieval pipelines, agent state, GPU scaling, prompt management, agent deployment and versioning, prompt testing, and RAG refresh pipelines.
Domain 4 stays at 24%, with additions such as agent coordination monitoring, Bedrock evaluations, foundation model and agent cost monitoring, CI/CD vulnerability checks, foundation model credential selection, and Bedrock Guardrails.
The four domains, in practical terms
Domain 1, Data Preparation for Machine Learning (ML) (28%)28%
The official C01 scope covers collecting and storing data, transforming and preprocessing it, and validating data quality and bias. Expect services such as Amazon S3, AWS Glue, Amazon Athena, SageMaker Data Wrangler, and SageMaker Feature Store to appear as parts of a larger design rather than isolated definitions.
The question is rarely “what does Glue do?” The useful distinction is why Glue fits a particular ingestion or transformation requirement, when Athena is enough for querying data in place, and when a feature belongs in Feature Store rather than ordinary storage. Data quality and bias matter too. A pipeline that produces a technically valid dataset can still be unsuitable if it introduces leakage, inconsistent features, or unfair outcomes.
Candidates repeatedly describe the exam as scenario-based service selection rather than pure ML theory. That makes this domain deceptively broad. You need to connect ingestion, storage, transformation, feature engineering, and validation in the correct order.
For C02, this domain keeps its 28% weight but adds multimodal data, embeddings, vector databases, advanced text preprocessing, RAG document preparation, anonymization, and foundation model fine-tuning data. Do not study C01 data preparation as if tabular data were the whole topic.
Domain 2, ML Model Development (26%)26%
MLA-C01 covers algorithm and framework selection, SageMaker training, Automatic Model Tuning, model metrics, Model Registry, responsible AI, and model evaluation. The exam wants you to select a modeling approach that satisfies the scenario, train and tune it correctly, and interpret the resulting performance.
The hard distinction is between a technically plausible answer and the answer that satisfies every requirement. A model can have a strong metric and still be the wrong choice because of latency, interpretability, data volume, cost, or deployment constraints.
Pass reports emphasize reading every distractor carefully. Select-two and select-three wording is a recurring trap. Two answers can sound correct until you check whether both satisfy the exact requirement.
C02 renames this area ML Model and Foundation Model Development and reduces it to 24%. It adds Bedrock foundation model selection, fine-tuning and customization, prompt engineering, retrieval optimization, human evaluation, NLP metrics, LLM-as-a-judge approaches, and RAG monitoring. Candidates preparing for C02 need to understand evaluation as a system activity, not just model accuracy.
Domain 3, Deployment and Orchestration of ML Workflows (22%)22%
C01 covers real-time, batch, asynchronous, and serverless endpoints, compute selection, autoscaling, containers, SageMaker Pipelines, CodePipeline, CodeBuild, CodeDeploy, CI/CD, and Step Functions.
The central question is operational fit. A real-time endpoint is not automatically better than a batch transform job. An asynchronous endpoint may fit large payloads and long processing times, while a serverless option can suit a different traffic pattern. The correct answer depends on the stated latency, throughput, scaling, and maintenance requirements.
Community materials repeatedly focus on endpoint types, autoscaling, pipelines, CloudFormation, and deployment tradeoffs. Memorizing endpoint names will not carry you through the scenario. You need to explain why one deployment pattern fits the workload and why the alternatives do not.
C02 raises this domain to 24% and adds foundation model deployment, external-model deployment, agents, Bedrock knowledge bases, retrieval pipelines, agent state, GPU scaling, prompt management, agent deployment and versioning, prompt testing, and RAG refresh pipelines. AWS also explicitly removes SageMaker Neo edge optimization and SageMaker BYOC from the C02 comparison.
Domain 4, ML Solution Monitoring, Maintenance, and Security (24%)24%
C01 covers SageMaker Model Monitor, CloudWatch, drift and performance monitoring, retraining, IAM, VPCs, encryption, auditability, responsible AI, and endpoint security.
Monitoring is more than checking whether an endpoint is running. You need to distinguish infrastructure health from model performance, detect changes in data or predictions, and connect a monitoring signal to an appropriate maintenance action. Security questions can involve permissions, network placement, encryption, and auditability at the same time.
The failure reports are consistent on this point: the live exam felt harder than some practice sets, and careful interpretation mattered more than memorizing service names. A service can be relevant to the architecture and still fail the security or operational requirement in the question.
C02 retains the 24% weight and renames the domain Operating, Monitoring, and Securing ML and AI Solutions. New material includes agent coordination monitoring, Bedrock evaluations, foundation model and agent cost monitoring, CI/CD vulnerability checks, foundation model credential selection, and Bedrock Guardrails. Some infrastructure-monitoring and capacity-troubleshooting wording is removed.
What stayed the same
The C02 update does not make traditional ML irrelevant. The core workflow remains:
- Ingest and prepare data.
- Engineer features and validate quality.
- Select, train, tune, and evaluate a model.
- Deploy it through an appropriate endpoint or workflow.
- Monitor performance, drift, cost, and security.
- Retrain or revise the system when conditions change.
SageMaker is still central. So are S3, Glue, Athena, Feature Store, pipelines, CI/CD, CloudWatch, IAM, VPC controls, encryption, and auditability.
Multiple experience threads say the same thing: the exam rewards understanding why a service fits all the requirements, not memorizing a service catalogue. That remains true whether the question concerns an asynchronous endpoint or a retrieval pipeline.
Reported C01 scores include 752, 758, and 793, but there is no meaningful community average or reliable pass-rate figure in the available evidence. Treat individual scores as examples, not a prediction.
What trips candidates up
Treating practice scores as proof of readiness
Some candidates report that the live exam felt harder than AWS Skill Builder or other practice sets. Practice questions are useful for finding gaps, but a score is not the goal. Review every wrong answer and every correct answer that depended on guessing.
Reading the service name instead of the requirement
A question may mention SageMaker, Bedrock, or a pipeline, but the decisive detail could be latency, cost, data sensitivity, scaling, retrieval quality, or deployment mode. Read the requirement before choosing the familiar service.
Missing select-two and select-three wording
Several options can be technically valid. The question is asking for the combination that satisfies the whole scenario. Count the required selections, then test each option against every stated constraint.
Studying deleted C02 content
Do not over-invest in SageMaker Neo edge optimization, SageMaker BYOC, EFS and FSx model-training loading, or the infrastructure-monitoring and capacity-troubleshooting wording that AWS removed from the C02 comparison.
Using C01 material for C02 GenAI topics
Old material does not cover Bedrock RAG, agents, embeddings, vector databases, foundation model evaluation, or GenAI cost and security topics in enough depth. C01 material remains useful, but it is not a complete C02 plan.
The preparation path
Start with the official blueprint
Use the official MLA-C01 exam guide as the scope checklist for the current exam. If you are preparing for the beta, compare it with AWS's MLA-C01 and MLA-C02 comparison document.
AWS Skill Builder's official AWS Certified Machine Learning Engineer, Associate Exam Prep Plan is the right starting point for structured preparation. Its official pretest, practice questions, exam preparation path, and labs can help you identify gaps. AWS-authored Skill Builder labs covering SageMaker, Amazon Bedrock, RAG, and generative AI evaluation are especially relevant to C02 preparation. Select the MLA-C02 plan for the beta, not an older C01 plan.
The official material has one predictable weakness: it gives you the provider's scope and terminology, but it does not replace repeated scenario practice. You still need to explain why a distractor fails.
Build the traditional ML foundation
For C01, work through a small sequence:
- Ingest a dataset into S3, transform it with Glue or Athena, and inspect data quality issues.
- Create features and compare Feature Store decisions with ordinary feature storage.
- Train and tune a SageMaker model, then register and evaluate it.
- Compare real-time, batch, asynchronous, and serverless deployment patterns.
- Add monitoring, IAM, VPC controls, encryption, and a retraining trigger.
For C02, add a second sequence:
- Prepare documents for retrieval and identify how chunking, metadata, embeddings, and vector storage affect results.
- Compare foundation model selection and customization choices in Bedrock.
- Build a simple RAG flow and test retrieval quality separately from generated-answer quality.
- Sketch an agent workflow with state, tool access, versioning, and monitoring.
- Track evaluation, cost, credentials, and guardrail decisions as part of the design.
A lab does not need to become a production platform. It needs to force a choice between two plausible services and make you defend that choice, such as why Glue fits the ingestion requirement.
Use practice questions as diagnosis
Use CertCompanion's MLA-C01 practice exam after you understand the blueprint. Start with the 30 free questions, then review every wrong answer and every correct answer that depended on guessing. Aim for 80% to 90% on mixed practice sets before scheduling.
The target is not recognition. You should be able to explain why the correct option satisfies the requirements and why each distractor fails. Keep a short error log, and turn recurring mistakes into flashcards or architecture notes.
For official preparation material, use the AWS certification hub and the AWS Skill Builder MLA-C02 Exam Prep Plan. Confirm the exam version before beginning a course, lab, or practice assessment.
How much preparation time?
Community timing is directional, not a planning guarantee. One intensive report described about 56 hours across a week. Other reports described roughly two weeks or about one month.
| Background | Estimated hours | Notes |
|---|---|---|
| Experienced AWS and ML professional | Some candidates report about one month, but comparable evidence is limited | The work is usually filling service, deployment, and exam-format gaps |
| AWS-familiar candidate with some ML experience | Community reports commonly describe roughly two weeks to one month | Time depends on transferable SageMaker and production experience |
| First certification or newer AWS user | A little over one month appears in the available first-certification report | AWS service vocabulary, ML operations, and architecture decisions all need building |
These ranges come from self-reported candidate experiences. They are not population estimates. C02 candidates should add time for Bedrock, RAG, agents, foundation model evaluation, and GenAI security because those topics are not a small appendix.
Exam-day tactics
MLA-C01 gives you about two minutes per question. Read the scenario and answer choices carefully, especially words such as “most,” “least,” “must,” “without,” and “select two.”
If a question is consuming too much time, mark it and continue. A long scenario can otherwise take time away from questions you could answer confidently. Return later with the full requirement in mind.
Expect the live exam to feel harder than at least some practice sets. The most upvoted advice across pass reports is consistent: do not study heavily immediately before the exam if fatigue increases anxiety, and do not let a familiar service name substitute for reading the requirement.
The exam is available through a Pearson VUE testing center or online proctoring. AWS's current retake policy applies; confirm the waiting interval shown during scheduling because operational policies can change independently of the blueprint.
After you pass
AWS positions the updated credential around a broader ML engineer role spanning traditional machine learning, generative AI, foundation models, and agentic AI. Roles that list this certification include ML engineer, MLOps engineer, LLMOps engineer, data engineer, backend software developer, and data scientist.
There is no reliable certification-specific salary range in the available evidence. Avoid treating generic machine learning salary figures as a direct return on the certification. The credential can support a role transition, but the practical value comes from being able to operate the systems represented by the exam.
The certification is valid for three years for C01. AWS lists the deeper AWS Certified Machine Learning, Specialty credential as a logical follow-on for professionals who want more advanced machine learning coverage.
Frequently asked questions
Is MLA-C01 being replaced by MLA-C02?
Yes, for the English exam transition described by AWS. MLA-C01 remains current through September 28, 2026, and MLA-C02 beta delivery begins September 29, 2026. C02 keeps the four-domain structure but broadens the scope to include foundation models, RAG, agents, embeddings, and GenAI operations.
Is MLA-C02 harder than MLA-C01?
There is no measured difficulty comparison in the available evidence. C02 has a broader scope and adds GenAI systems, so candidates using only C01 material will face missing topics. The traditional ML engineering workflow remains relevant, which reduces the size of the change for people with current AWS and production ML experience.
Do I need to study Bedrock for MLA-C01?
Not for the C01 scope described in the official guide. Bedrock, RAG, agents, foundation models, embeddings, and related evaluation and security topics belong to the C02 expansion. Follow the blueprint for the exam you will actually take.
Are the MLA-C01 domain weights still useful?
Yes, for C01. Its weights are 28%, 26%, 22%, and 24%. C02 changes them to 28%, 24%, 24%, and 24%. Use the matching blueprint. Mixing the two creates a bad study plan.
Should I discard my old MLA-C01 study guide?
No. Keep the sections covering SageMaker, data preparation, model training, deployment, monitoring, security, and MLOps. Add the C02 topics that the old guide does not cover, and remove time spent on C02-deleted areas such as SageMaker Neo edge optimization and SageMaker BYOC.
What changed in the MLA-C02 exam format?
AWS lists 85 questions and 170 minutes for the C02 beta. C01 has 65 total questions, including 50 scored and 15 unscored, with 130 minutes. AWS has not published the C02 beta passing score or final non-beta pricing.
Is the certification valid for three years?
C01 has a three-year validity period. AWS presents C02 as the next exam in the same AWS Certified Machine Learning Engineer, Associate credential family, but the available brief does not provide a separate C02 validity statement.
How many hours should I study for MLA-C01?
Candidate reports range from about 56 hours in one intensive week to roughly two weeks or one month. These are self-reported experiences, not a population estimate. Your starting point in AWS, machine learning, SageMaker, and deployment will determine the actual gap.
Recent candidate threads
Real posts from people preparing for or recently sitting the MLA-C01. Read these for the unfiltered version of what the exam felt like:
- “Cleared AWS MLA-C01 after a humbling first attempt,” r/AWSCertifications, 21 comments
- “Passed MLA-C01,” r/AWSCertifications, 5 comments
- “Finally passed the AWS Certified Machine Learning Engineer Associate,” r/AWSCertifications, 13 comments
- “Passed MLA-C01 on my first attempt. Way harder than I was expecting,” r/AWSCertifications, 9 comments
- “My prep journey and what actually helped,” r/AWSCertifications, 6 comments
Threads pulled from the Reddit communities most active for AWS certifications.
The practical conclusion
MLA-C02 is not a clean break from MLA-C01. It is the same production ML foundation with a wider definition of what an ML engineer operates. The biggest study risk is using C01 material as if it covers Bedrock, RAG, agents, and foundation model evaluation. It does not.
Use the official comparison to mark additions and deletions. Keep the SageMaker and MLOps fundamentals. Then build enough small systems to explain the tradeoffs under time pressure. The update is substantial, but it is coherent.
Check the current scope and build your MLA-C01 practice plan at CertCompanion.
Checked against official AWS MLA-C01 exam documentation and current candidate reports. Last verified 2026-09-22.