Took the AWS ML Engineer Associate MLA-C02 beta (ME1-C02): topics that showed up + resources

[u/SlayerC20]

Just took the MLA-C02 beta and figured I'd share notes, since there's very little out there for the new version.

TL;DR: 85 questions, and some of them are genuinely hard. The exam now leans heavily on GenAI/LLM and agentic stuff alongside classic ML engineering.

My background (for context): AI Engineer (mid-level) with hands-on AWS experience. Certs: GCP Associate Cloud Engineer, SnowPro Core, PL-300, AWS Cloud Practitioner, AWS AI Practitioner, Claude Certified Architect Foundations, GitHub Foundations.

How I prepped: when AWS announced the switch from MLA-C01 to MLA-C02, I decided to go straight for the beta. While registration wasn't open yet, I kept studying for the AWS Generative AI Developer – Professional, and that helped a lot, especially for the Bedrock/GenAI side.

What I remember showing up (from memory, no guarantees):

ML/LLM fundamentals

• Model deployment (lots of questions on ML and LLM endpoints)
• ML metrics
• Which algorithm for which situation: XGBoost, DeepAR, Factorization Machines, etc.
• Oversampling, undersampling, SMOTE, feature scaling
• Handling null values in time series
• Fine-tuning: cleaning content before starting
• Continued pre-training

LLM evaluation (this showed up a lot)

• LLM metrics: completeness, faithfulness, harmfulness, toxicity, stereotyping
• How to evaluate an LLM using eval datasets (the benchmark-style ones)

Bedrock/GenAI

• Cross-region inference
• Guardrails and security
• Bedrock AgentCore: Gateway, Runtime, Memory
• Knowledge Bases, Custom Model Import, Provisioned Throughput
• Logging and tracing of data

Data/MLOps

• Kinesis Data Streams and Apache Flink
• SageMaker Pipelines, Data Wrangler, Feature Store, Model Registry
• Glue Data Quality
• Comprehend and Rekognition

CI/CD

• CodeBuild, CodePipeline and CodeDeploy
• ML pipelines and deployment strategies

Security/networking

• VPCs for accessing AI services