# Content Domain 1: Data Preparation for Machine Learning (ML)
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**Topics**
+ [Task 1.1: Ingest and store data](#machine-learning-engineer-associate-01-domain1-task1)
+ [Task 1.2: Transform data and perform feature engineering](#machine-learning-engineer-associate-01-domain1-task2)
+ [Task 1.3: Ensure data integrity and prepare data for modeling](#machine-learning-engineer-associate-01-domain1-task3)

## Task 1.1: Ingest and store data
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Knowledge of:
+ Data formats and ingestion mechanisms (for example, validated and non-validated formats, Apache Parquet, JSON, CSV, Apache ORC, Apache Avro, RecordIO)
+ How to use the core AWS data sources (for example, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon FSx for NetApp ONTAP)
+ How to use AWS streaming data sources to ingest data (for example, Amazon Kinesis, Apache Flink, Apache Kafka)
+ AWS storage options, including use cases and tradeoffs

Skills in:
+ Extracting data from storage (for example, Amazon S3, Amazon Elastic Block Store [Amazon EBS], Amazon EFS, Amazon RDS, Amazon DynamoDB) by using relevant AWS service options (for example, Amazon S3 Transfer Acceleration, Amazon EBS Provisioned IOPS)
+ Choosing appropriate data formats (for example, Parquet, JSON, CSV, ORC) based on data access patterns
+ Ingesting data into Amazon SageMaker Data Wrangler and SageMaker Feature Store
+ Merging data from multiple sources (for example, by using programming techniques, AWS Glue, Apache Spark)
+ Troubleshooting and debugging data ingestion and storage issues that involve capacity and scalability
+ Making initial storage decisions based on cost, performance, and data structure

## Task 1.2: Transform data and perform feature engineering
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Knowledge of:
+ Data cleaning and transformation techniques (for example, detecting and treating outliers, imputing missing data, combining, deduplication)
+ Feature engineering techniques (for example, data scaling and standardization, feature splitting, binning, log transformation, normalization)
+ Encoding techniques (for example, one-hot encoding, binary encoding, label encoding, tokenization)
+ Tools to explore, visualize, or transform data and features (for example, SageMaker Data Wrangler, AWS Glue, AWS Glue DataBrew)
+ Services that transform streaming data (for example, AWS Lambda, Spark)
+ Data annotation and labeling services that create high-quality labeled datasets

Skills in:
+ Transforming data by using AWS tools (for example, AWS Glue, DataBrew, Spark running on Amazon EMR, SageMaker Data Wrangler)
+ Creating and managing features by using AWS tools (for example, SageMaker Feature Store)
+ Validating and labeling data by using AWS services (for example, SageMaker Ground Truth, Amazon Mechanical Turk)

## Task 1.3: Ensure data integrity and prepare data for modeling
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Knowledge of:
+ Pre-training bias metrics for numeric, text, and image data (for example, class imbalance [CI], difference in proportions of labels [DPL])
+ Strategies to address CI in numeric, text, and image datasets (for example, synthetic data generation, resampling)
+ Techniques to encrypt data
+ Data classification, anonymization, and masking
+ Implications of compliance requirements (for example, personally identifiable information [PII], protected health information [PHI], data residency)

Skills in:
+ Validating data quality (for example, by using DataBrew and AWS Glue Data Quality)
+ Identifying and mitigating sources of bias in data (for example, selection bias, measurement bias) by using AWS tools (for example, SageMaker Clarify)
+ Preparing data to reduce prediction bias (for example, by using dataset splitting, shuffling, and augmentation)
+ Configuring data to load into the model training resource (for example, Amazon EFS, Amazon FSx)