CertLabProAWS Certified Machine Learning Engineer Associate
275 practice questions
Last reviewed: April 2026
Personal notes and resource links for your study journey
Filter by Certification
The AWS Certified Machine Learning Engineer Associate (MLA-C01) launched in August 2024 as the practitioner-focused counterpart to the older Machine Learning Specialty. It validates the ability to build, deploy, monitor, and maintain ML workloads on AWS — with a strong emphasis on Amazon SageMaker, MLOps tooling, and production model lifecycles. The exam targets early- and mid-career ML engineers, data scientists moving into engineering, and DevOps engineers expanding into ML platforms. Expect scenario-driven questions about feature pipelines, model registries, deployment patterns, drift detection, and cost-aware inference. The exam is conceptual and hands-off (no labs), but assumes the candidate has actually shipped models to production.
The largest domain at 28%. SageMaker Data Wrangler, Feature Store, Glue, and S3 data lake patterns. Expect questions on handling imbalanced data, leakage, encoding strategies, and feature engineering at scale.
SageMaker training jobs, built-in algorithms vs. bring-your-own-container, JumpStart, hyperparameter tuning, and model evaluation. Common stumbling block: choosing between SageMaker Autopilot, Canvas, and custom training.
SageMaker endpoints (real-time, async, serverless, batch transform), Pipelines, Model Registry, and CI/CD with CodePipeline. Candidates often miss subtle differences between deployment modes and their cost tradeoffs.
SageMaker Model Monitor, Clarify (bias and explainability), drift detection, and IAM/VPC patterns for ML workloads. Tests practical MLOps fluency more than theory.
$120k–$165k–$230k USD annual
Range covers US-based mid-to-senior MLE roles where AWS proficiency is required. FAANG / unicorn senior MLEs frequently exceed $300k TC. Entry-level and non-coastal markets trend lower. The cert alone does not move salary — it complements a portfolio of shipped ML systems.
Source: levels.fyi 2025–2026 ML engineer roles, U.S. BLS OEWS May 2024 (15-2051 data scientists, 15-1252 software developers). Figures are approximate; actual compensation depends on role, region, and experience.
Demand for ML engineers who can productionize models — not just train them in notebooks — accelerated through 2024–2026 as enterprises operationalized GenAI and classical ML workloads. MLA-C01 functions as a credible signal that a candidate understands SageMaker end-to-end and can navigate MLOps tradeoffs. Recruiters at AWS-centric shops (financial services, healthcare, retail data teams) use it as a screening filter alongside Python and PyTorch/TensorFlow experience. It pairs naturally with the AI Practitioner (AIF-C01) and the Data Engineer Associate (DEA-C01) for a broader data-and-ML profile. It does NOT by itself qualify candidates for ML research roles, deep-learning specialist positions, or ML platform architect titles — those expect multi-year shipped-system experience plus often a graduate degree.
There are no formal prerequisites. AWS recommends at least one year of hands-on experience with SageMaker and ML workflows, plus working knowledge of Python, common ML libraries (scikit-learn, pandas, PyTorch or TensorFlow), and basic statistics.
The most efficient path is to pass AIF-C01 first (foundational AI vocabulary), then build a small end-to-end SageMaker project — feature store, training job, model registry, real-time endpoint, monitor — before sitting MLA-C01. Candidates with a Cloud Practitioner (CLF-C02) or Solutions Architect Associate (SAA-C03) background find the AWS-services questions much easier. A pure data-science background without AWS exposure is the hardest starting point and typically requires 80+ hours of additional service-specific study.
MLA-C01 is rated Associate and is meaningfully harder than AIF-C01 because it assumes hands-on SageMaker fluency. Plan 80–120 hours over 8–12 weeks if you have prior ML experience but limited AWS exposure; 40–60 hours over 4–6 weeks if you already work daily on AWS ML pipelines. The exam is 65 scored questions in 170 minutes — multiple-choice and multiple-response, no labs.
The most common stumbling block is the breadth of SageMaker sub-services (Studio, Pipelines, Feature Store, Model Registry, Clarify, Model Monitor, JumpStart, Canvas, Autopilot, Ground Truth) — questions often hinge on picking the right tool for a constrained scenario. The second pitfall is deployment modes: knowing precisely when to use real-time vs. async vs. serverless vs. batch transform endpoints, and the cost and latency tradeoffs of each.
Initial general availability. Beta exam ran in mid-2024. Replaces the older Machine Learning Specialty (MLS-C01) for engineering-focused candidates. Current version as of April 2026.
MLA-C01 (AWS Certified Machine Learning Engineer Associate) is a a moderately difficult exam expecting practical hands-on experience plus solid understanding of best practices Associate-level exam. Most candidates need 80–150 hours of study spread over 6–12 weeks for associate-level exams. Most candidates who score consistently above the passing threshold on practice exams pass on their first attempt.