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MLA-C02 data preparation for ML and AI

Updated September 20, 2026

Data Preparation for ML and AI is worth 28% of MLA-C02 — the largest domain. AWS frames the task as ingesting, transforming, validating and preparing data for AI and ML modelling.

Engineers arriving from a modelling background routinely underrate this. In practice most ML failures are data failures, and AWS has weighted the exam to reflect that.

Ingestion and storage

Getting data in, and choosing where it lives:

  • The common AWS sources and how data arrives — batch versus streaming.
  • Storage choices, and the cost and access trade-offs behind them.
  • Data formats. Columnar formats compress well and read efficiently for analytics; row formats suit record-at-a-time access. Choosing the right one is a cost and performance decision the exam tests.
  • Partitioning, so queries and training jobs read only what they need.

Transformation and cleaning

  • Handling missing values, duplicates and outliers, and knowing that each choice is a modelling decision rather than housekeeping.
  • Type conversion, normalisation and scaling.
  • Joining and aggregating across sources.
  • Doing this at scale, which is what distinguishes this exam from a data science course.

Feature engineering

  • Creating features that carry signal, and encoding categorical variables.
  • Storing and reusing features so training and inference compute them identically.

That last point matters more than it sounds: training/serving skew, where features are computed one way in training and another at inference, is a classic production failure and a natural exam scenario.

Validation and data quality

  • Completeness, schema conformity, and detecting corrupt or anomalous data before it reaches training.
  • Detecting bias in the dataset, including under-representation.
  • Establishing a baseline so you can detect drift later.

Splitting data, and leakage

Training, validation and test sets, and why the split must reflect how the model will be used.

Data leakage is the concept most worth internalising. It occurs when information unavailable at prediction time reaches the training data — a column derived from the outcome, or a random split applied to time-series data so the model effectively sees the future. The signature is the same every time: excellent test performance, poor production performance. Any scenario with that shape is a leakage question.

Preparing data for foundation models

New in C02, and different from preparing training data:

  • Content for retrieval is chunked and embedded rather than used to fit a model.
  • Chunk size and overlap affect retrieval quality directly.
  • Data for fine-tuning is example pairs demonstrating desired behaviour, and needs far less volume than training from scratch.

Sample questions

Question 1. A team trains a model on time-series sales data using a random 80/20 split. Test accuracy is excellent but production accuracy is poor. What is the most likely cause?

  • A. A random split leaked future information; the data should be split chronologically
  • B. The model needs more training epochs
  • C. The inference endpoint is under-provisioned
  • D. The test set is too small to be meaningful
Show answer

Answer: A

A random split on time-series data lets the model train on later periods and test on earlier ones, effectively seeing the future, which inflates test performance and collapses in production. Insufficient epochs and endpoint sizing would not produce strong test results, and an imbalanced test set would not systematically inflate accuracy this way.

Question 2. Analytical queries over a very large training dataset are slow and expensive because each job reads every column. Which change most directly helps?

  • A. Compress the existing row-based files
  • B. Move the data to a larger instance type
  • C. Convert to a partitioned columnar format
  • D. Sample the data down to fewer rows
Show answer

Answer: C

Storing data in a columnar format lets jobs read only the columns they need, cutting both scan volume and cost, and partitioning further limits what is read. Compressing a row format still requires reading whole rows, a larger instance treats the symptom, and reducing rows discards data.

Question 3. A feature is computed one way in the training pipeline and a slightly different way in the inference code. What problem does this create?

  • A. Overfitting
  • B. Training/serving skew
  • C. Data drift
  • D. Data leakage
Show answer

Answer: B

Computing features differently in training and serving is training/serving skew, so the model receives inputs at inference that do not match what it learned from, degrading production performance. Overfitting is memorisation of training data, drift develops over time from changing data, and leakage involves information unavailable at prediction time.

What to practise

Take one dataset and run it end to end: ingest, clean, engineer two features, validate, and split it correctly for its type. Then deliberately introduce a leak and watch your test metric jump. That second exercise teaches the most heavily tested concept in the largest domain in about twenty minutes.