AWS Machine Learning Engineer Associate

Keep the methods and definitions fresh.

Ready-made practice for the AWS Machine Learning Engineer Associate. Build recall one short session at a time — no decks to build before you begin.

968 cards·4 decks·Yours for life
or go All-Access →

Flashcards are a memory tool. Learn the material first, then use NoReread to retain it and refresh what you already know. It doesn't replace studying the subject.

Cards are created from publicly available sources and may contain errors. Check anything you rely on against your official materials.

What's inside

Explore the curriculum, one focused deck at a time.

1Data Preparation for ML and AI273 cards · AWS ML Engineer Associate (MLA-C02) Domain 1 — collecting and storing data, streaming ingestion, data formats, vector stores, Feature Store, transformation and feature engineering, embeddings, RAG document preparation, PII masking, FM training data, data quality, labeling, bias metrics, and class imbalance.2Deployment and Orchestration of ML and AI Workflows223 cards · AWS ML Engineer Associate (MLA-C02) Domain 3 — deployment targets and inference options, Bedrock deployment modes, agents and AgentCore, RAG configuration, infrastructure as code, containers, VPC endpoints, auto scaling, knowledge bases, CI/CD with CodePipeline and SageMaker Pipelines, model registry, and prompt and agent versioning.3ML Model and Foundation Model Development251 cards · AWS ML Engineer Associate (MLA-C02) Domain 2 — choosing ML models and Bedrock foundation models, fine-tuning and RAG strategies, AWS AI services, SageMaker AI built-in algorithms, script mode, automatic model tuning, regularization, prompt engineering, retrieval tuning, and evaluation metrics for ML and GenAI.4Operating, Monitoring, and Securing ML and AI Solutions221 cards · AWS ML Engineer Associate (MLA-C02) Domain 4 — Model Monitor and drift, A/B testing, agent and FM observability, CloudWatch and X-Ray troubleshooting, cost management and purchasing options, FM cost optimization, IAM and least privilege, auditing, network isolation, AI security risks, credentials for FMs, and Bedrock Guardrails.

Keep the AWS Machine Learning Engineer Associate material fresh.

Start free and study a few minutes a day. $7.99 once, and these 968 cards are yours for life.

or go All-Access →

AWS Certified Machine Learning Engineer – Associate flashcards for the MLA-C02 exam guide (beta from September 29, 2026) — 968 cards in 4 decks following the 4 content domains: data preparation for ML and AI (ingestion, formats, vector stores, feature engineering, RAG document prep, bias), ML and foundation model development (Bedrock FM selection, fine-tuning, SageMaker AI training and tuning, evaluation), deployment and orchestration (inference options, agents, knowledge bases, CI/CD), and operating, monitoring and securing ML and AI solutions (drift, cost, IAM, Guardrails). Built from publicly available sources such as the official AWS MLA-C02 exam guide. Cards are a memory-retention aid, not a substitute for study — check details against the official materials.