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MLA-C01 Data Preparation for Machine Learning Guide

AWS MLA-C01 data prep guide covering ingestion, feature engineering, labeling, quality, bias, and readiness decisions.

This chapter is where MLA-C01 tests whether you can get data ready for ML in a way that is technically sound and operationally realistic. AWS expects you to know how data is ingested, stored, transformed, labeled, validated, and prepared for training without creating hidden integrity or compliance problems.

Current weight in the exam guide

AWS currently weights Data Preparation for Machine Learning at 28% of scored content.

What this domain is really testing

This domain is not just about moving data into S3. It is testing whether you can:

  • get the right data into the right shape for ML workflows
  • choose storage, format, and feature-serving patterns that fit training and inference
  • clean, label, and transform data without breaking lineage or usefulness
  • keep quality, bias, and compliance concerns inside the preparation process instead of treating them as afterthoughts

Work this domain in order

Lesson Focus
1.1 Ingestion & Feature Store Learn how AWS expects ML engineers to choose storage, formats, ingestion paths, and feature-serving foundations.
1.2 Features & Labeling Learn how data is cleaned, transformed, encoded, labeled, and turned into useful features.
1.3 Quality, Bias & Readiness Learn how validation, bias checks, encryption, masking, and model-input readiness shape the final training dataset.

Fast routing inside this chapter

If the question is really about… Go first to…
S3, EFS, FSx, Kinesis, Kafka, file formats, feature store, or ingestion bottlenecks 1.1 Data Ingestion, Storage, Formats & Feature Store
Data Wrangler, Glue, DataBrew, Spark, encoding, feature creation, or labeling 1.2 Transformations, Feature Engineering & Labeling
Data quality, Clarify, bias mitigation, PII, PHI, masking, or training-data loading choices 1.3 Data Quality, Bias, Compliance & Modeling Readiness

If you keep missing questions in this domain

Symptom What is usually going wrong Fix first
every storage answer seems valid you are not mapping format and storage to the access pattern rework 1.1 and classify whether the problem is ingestion, serving, training throughput, or feature reuse
feature engineering questions feel hand-wavy you are not separating raw cleanup from model-useful transformation rework 1.2 and track what changes the information content versus what only cleans the pipeline
compliance and quality stems feel like policy trivia you are not treating bad data as a model-readiness problem rework 1.3 and tie every control to training quality, fairness, or safe use
you keep optimizing model choice before dataset quality you are skipping the upstream failure point stay in the data-prep lane until the training set is trustworthy and usable

What strong answers usually do

  • separate data movement from feature transformation
  • choose file formats and storage paths that match the access pattern
  • treat bias, masking, and compliance as part of data preparation instead of a later security-only concern
  • make sure the final training input is valid, loadable, and operationally maintainable

Common MLA-C01 traps in this domain

  • assuming the easiest storage option is also the best format for downstream training
  • confusing feature engineering with arbitrary preprocessing complexity
  • treating quality checks and bias checks as optional cleanup instead of core gating steps
  • ignoring how feature-serving choices affect both training consistency and online inference consistency

Before you leave this domain

Make sure you can explain:

  1. how the data is ingested and stored
  2. what transformations create model-ready features
  3. what checks prove the data is safe and usable
  4. how the same data logic stays consistent between training and inference

Then move to 2. Model Dev, where AWS assumes the data pipeline is good enough and starts testing model-family and evaluation judgment.

In this section

Revised on Sunday, May 10, 2026
2. Model Dev