MLA-C02 is the new version of the AWS Certified Machine Learning Engineer - Associate exam. It replaces MLA-C01 and adds generative AI, foundation models and agentic workflows. MLA-C02 beta testing began 29 September 2026, with general availability on 14 January 2027.
AWS Certified Machine Learning Engineer – Associate (MLA-C02) Practice Tests
Exam Quick Facts – AWS Certified Machine Learning Engineer – Associate (MLA-C02)
The AWS Certified Machine Learning Engineer – Associate is AWS's associate-level credential for professionals who build, train, deploy, and operationalize machine learning solutions on AWS — with a heavy emphasis on Amazon SageMaker and the broader AWS ML stack. It validates hands-on ability to move a model from raw data to a monitored production endpoint, not just theoretical ML knowledge.
Detail Info
Exam Code MLA-C02
Exam Title AWS Certified Machine Learning Engineer – Associate
Vendor Amazon Web Services (AWS)
Exam Cost $150 USD
Duration 130 minutes
Number of Questions 65 questions (50 scored + 15 unscored pretest)
Question Types Multiple choice, multiple response, ordering, matching, and case-study style scenarios
Passing Score 720 out of 1000 (scaled score)
Delivery Pearson VUE test center or online proctored Ready to test your MLA-C02 knowledge with exam-realistic questions and detailed explanations? Start your study4pass AWS MLA-C02 practice tests now and build the confidence to pass on your first attempt.
AWS MLA-C02: Complete Guide for Candidates
Career Value & Business Impact of AWS MLA-C02
Demand. Demand for ML engineers and MLOps professionals who can actually implement end-to-end pipelines — not just prototype in a notebook — keeps climbing. A growing share of AI/ML job postings explicitly call out AWS ML credentials as preferred or required, particularly at organizations standardized on AWS and SageMaker. What sets MLA-C02 apart from more theory-heavy certifications is that it signals hands-on ability to build, train, tune, deploy, and monitor models in production.
Career growth. A common path looks like: data analyst, software engineer, or cloud engineer → ML engineer or MLOps engineer → senior ML engineer, ML platform engineer, or ML solutions architect. MLA-C02 validates the practical skills that move someone along that path — SageMaker workflows, data preparation, feature engineering, model evaluation, deployment, and monitoring — which is exactly what hiring managers and promotion committees look for. On compensation, AWS ML-certified professionals often report salaries roughly in the $120K–$170K+ range depending on role, experience, and region, with some candidates who already have ML or data engineering experience seeing premiums in the $15K–$25K range for holding the certification. Figures like these vary widely by market, so treat them as directional rather than a guarantee.
Small business & remote work. For smaller teams, MLA-C02 skills translate directly into cost-effective ML solutions — managed services like SageMaker and serverless inference let a small team run production ML without a large ops footprint, and solid MLOps practices mean models can be deployed and monitored reliably without a dedicated platform team. The work also travels well: ML engineering and MLOps are highly compatible with remote roles, freelancing, and consulting, and a globally recognized AWS credential is a fast way to establish credibility with international clients, especially ones already running AWS-centric environments.
Technical Fundamentals Covered in MLA-C02
Data Preparation and Feature Engineering
- Data ingestion from S3, databases, and streaming sources
- Data cleaning, transformation, and feature engineering with AWS Glue, SageMaker Processing, and SageMaker Data Wrangler
- Handling imbalanced data, encoding, scaling, and splitting datasets for training, validation, and testing
Model Training and Tuning
- Choosing between built-in SageMaker algorithms and custom containers
- Training jobs, hyperparameter tuning, and automatic model tuning in SageMaker
- Distributed training concepts and when they're worth the added complexity
- Evaluating models with the right metrics for the problem type — accuracy, precision, recall, F1, ROC-AUC, RMSE, and similar, depending on classification, regression, or forecasting tasks
Model Deployment and Inference
- Real-time vs. batch vs. asynchronous inference
- Deploying models as SageMaker endpoints, configuring auto scaling, and managing variants for A/B testing
- Using SageMaker Serverless Inference for variable or low-traffic workloads
- Integrating models into applications through API Gateway, Lambda, and SDKs
MLOps and Operationalization
- CI/CD for ML using SageMaker Pipelines, CodePipeline, and CodeBuild
- Model registry, versioning, and approval workflows
- Monitoring data drift, model performance, and resource utilization with SageMaker Model Monitor and CloudWatch
- Rollback strategies and retraining pipelines
These four areas map directly to the official MLA-C02 exam domains — data engineering, ML workflow, deployment, monitoring, and MLOps — so a candidate who's comfortable across all four is well positioned to pass.
Governance, Security, and Pricing for ML on AWS
Governance and compliance. Expect scenarios touching AWS Organizations and multi-account strategies for ML workloads, tagging conventions for datasets, models, and endpoints to support cost allocation and governance, and baseline concepts around data lineage, model documentation, and auditability — especially relevant in regulated industries.
Security best practices. Know the Shared Responsibility Model as it applies specifically to ML workloads — what AWS manages versus what you manage. You'll also need IAM roles and policies for SageMaker, S3, and related services built on least privilege; encryption at rest and in transit (S3 encryption, KMS, TLS for endpoints); network security fundamentals like VPC configuration, private subnets, VPC endpoints, and restricting endpoint access; and a working understanding of data privacy, PII handling, and compliance considerations across an ML pipeline.
Pricing models and cost management. This means understanding how SageMaker pricing works across training, hosting, processing, and Data Wrangler; choosing instance types and scaling strategies that balance performance against cost; leaning on managed and serverless options to cut operational overhead; and using Cost Explorer, Budgets, and tags to monitor and control spend. Architectural choices — batch vs. real-time inference, model size, caching strategy — all carry real cost implications, and the exam expects you to reason through those trade-offs.
MLA-C02 is ultimately testing judgment: balancing security, performance, and cost in ML solutions, not just recalling which SageMaker feature does what.
Exam Preparation & Logistics for MLA-C02
Exam format. 65 questions in 130 minutes: 50 scored and 15 unscored pretest questions mixed in with no indication of which is which. Question formats include multiple choice, multiple response, ordering, matching, and case-study style scenarios — a broader mix than most associate-level AWS exams, so it's worth practicing each format rather than assuming it's all standard multiple choice. You can sit the exam at a Pearson VUE test center or online with a proctor.
Passing score. AWS uses scaled scoring, and MLA-C02 requires 720 out of 1000 to pass. As with other AWS certifications, the exact number of questions you need to get right can shift slightly between exam forms because of how the scaling is calculated.
How much study time to budget. Because MLA-C02 blends ML fundamentals with AWS service-specific knowledge, study time depends heavily on your starting point:
- New to ML and AWS: plan for a longer runway — comfortable data science or engineering fundamentals plus SageMaker hands-on time before you're ready for practice exams.
- Experienced with ML but new to AWS: focus study time on SageMaker's specific tooling (Pipelines, Model Monitor, Data Wrangler, endpoint configuration) rather than ML theory you already know.
- Experienced with AWS but new to ML: prioritize model evaluation metrics, training/tuning concepts, and MLOps workflows, since the AWS service navigation will already feel familiar.
Free resources worth using alongside practice tests:
- AWS Skill Builder (free digital training, including MLA-C02 prep material)
- AWS Whitepapers & FAQs, including SageMaker developer guides and the Well-Architected Machine Learning Lens
- AWS documentation for core services (SageMaker, S3, Glue, Lambda, API Gateway, CloudWatch)
- Free sample questions from AWS and reputable providers
Free resources build the foundation, but they won't show you where your specific gaps are. That's what study4pass MLA-C02 practice tests are for — realistic, scenario-style questions with detailed explanations that pinpoint exactly which domains need more work before exam day.
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Disclaimer: study4pass is an independent training and practice-test provider and is not affiliated with, endorsed by, or sponsored by Amazon Web Services (AWS). "AWS Certified Machine Learning Engineer – Associate," "MLA-C02," and related logos are trademarks or registered trademarks of Amazon Web Services, Inc. in the United States and/or other countries. Exam details such as format, passing score, and content domains are based on publicly available information and may change over time — always verify current exam policies on the official AWS Certification website. Our practice tests are designed for educational purposes to help you prepare for the MLA-C02 exam and do not guarantee exam success.
Other Useful Amazon AWS Certifications
Amazon AWS Foundational Certifications
Amazon AWS Associate Certifications
- AWS Certified Solutions Architect – Associate (SAA-C03)
- AWS Certified Developer – Associate (DVA-C02)
- AWS Certified SysOps Administrator – Associate (SOA-C02)
- AWS Certified CloudOps Engineer – Associate (SOA-C03)
- AWS Certified Data Engineer – Associate (DEA-C01)
- AWS Certified Machine Learning Engineer – Associate (MLA-C02)
Amazon AWS Professional Certifications
- AWS Certified Solutions Architect – Professional (SAP-C02)
- AWS Certified DevOps Engineer – Professional (DOP-C02)
- AWS Certified Generative AI Developer – Professional (AIP-C01)
Amazon AWS Specialty Certifications
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