AWS Machine Learning Specialty Exam Prep

The ML Specialty exam is heavily weighted toward modeling (36%) and tests deep understanding of ML algorithms, model selection, hyperparameter tuning, and evaluation metrics. You'll need to know when to use different algorithms (linear regression, logistic regression, decision trees, XGBoost, neural networks, k-means, PCA) and understand concepts like bias-variance tradeoff, regularization (L1/L2), and ensemble methods.

The exploratory data analysis domain (24%) covers data visualization, feature engineering, handling missing data, dealing with imbalanced datasets, and statistical analysis. Data engineering (20%) tests knowledge of data pipelines, S3 data lakes, and data transformation. The MLOps domain (20%) covers SageMaker deployment, A/B testing, model monitoring, and retraining pipelines.

This exam requires strong ML fundamentals — you should be comfortable with the math behind common algorithms, not just how to use them. Review linear algebra basics (matrix operations), probability and statistics (Bayes' theorem, distributions), and optimization (gradient descent, loss functions). Then focus on SageMaker: built-in algorithms, training with custom containers, hyperparameter tuning jobs, and deployment options.

Practice questions that ask you to choose the right algorithm for a given problem: classification vs regression vs clustering vs anomaly detection vs NLP vs computer vision. Understand data preprocessing techniques: normalization, one-hot encoding, handling missing values, and dealing with class imbalance (SMOTE, oversampling, undersampling). Budget 8-10 weeks of study.

The ML Specialty remains a respected credential that validates deep ML expertise on AWS. However, AWS is transitioning this certification — the ML Engineer Associate (MLA-C01) now covers many practical ML engineering topics, and the MLS-C01 is expected to retire. If you're deciding between the two, the ML Engineer Associate is the forward-looking choice for most professionals.

That said, if you already have deep ML/data science experience and want to validate it, the ML Specialty's focus on algorithm theory and model design may be more relevant to your work than the associate-level exam. Currently certified professionals will retain their credential through its validity period.