AWS Certified Machine Learning - Specialty Exam Prep

This domain covers the data engineering work needed to support machine learning solutions, including creating repositories, ingesting data, and transforming data for ML workloads. It emphasizes selecting appropriate storage, orchestration, and processing services for batch and streaming pipelines.

• Task 1.1: Create data repositories for ML
• Identify data sources
• Determine storage mediums
• Task 1.2: Identify and implement a data ingestion solution
• Identify data job styles and job types
• Orchestrate data ingestion pipelines
• Task 1.3: Identify and implement a data transformation solution

Key references: AWS MLS-C01 official exam guide · ExamPal shared topic tree

This domain covers preparing data for machine learning, engineering useful features, and analyzing data to understand patterns before modeling. It includes data sanitation, feature extraction, visualization, descriptive statistics, and cluster analysis.

• Task 2.1: Sanitize and prepare data for modeling
• Identify and handle missing data, corrupt data, and stop words
• Format, normalize, augment, and scale data
• Task 2.2: Perform feature engineering
• Identify and extract features from datasets, including from data sources such as text, speech, images, and public datasets
• Analyze and evaluate feature engineering concepts
• Task 2.3: Analyze and visualize data for ML

Key references: AWS MLS-C01 official exam guide · ExamPal shared topic tree

Covers how to frame business problems as machine learning problems, choose appropriate models, train and tune models, and evaluate model performance. This domain emphasizes both classical ML and modern foundation/LLM approaches, along with the practical tradeoffs involved in model selection and evaluation.

• Task 3.1: Frame business problems as ML problems
• Determine when to use and when not to use ML
• Know the difference between supervised and unsupervised learning
• Task 3.2: Select the appropriate model(s) for a given ML problem
• XGBoost, logistic regression, k-means, linear regression, decision trees, random forests, RNN, CNN, ensemble, transfer learning, and large language models (LLMs)
• Express the intuition behind models
• Task 3.3: Train ML models

Key references: AWS MLS-C01 official exam guide · ExamPal shared topic tree

Covers building, securing, deploying, and operating machine learning solutions in AWS. This domain emphasizes operational qualities such as performance, availability, scalability, resiliency, and fault tolerance, along with service selection and security practices.

• Task 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance
• Log and monitor AWS environments
• AWS CloudTrail and Amazon CloudWatch
• Task 4.2: Recommend and implement the appropriate ML services and features for a given problem
• ML on AWS (application services), for example:
• Amazon Polly
• Task 4.3: Apply basic AWS security practices to ML solutions

Key references: AWS MLS-C01 official exam guide · ExamPal shared topic tree