# Content Domain 1: Data Preparation for ML and AI
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**Topics**
+ [Task 1.1: Collect and store data.](#machine-learning-engineer-associate-02-domain1-task1)
+ [Task 1.2: Perform data transformation, feature engineering, and pre-processing.](#machine-learning-engineer-associate-02-domain1-task2)
+ [Task 1.3: Validate data quality and manage bias.](#machine-learning-engineer-associate-02-domain1-task3)

## Task 1.1: Collect and store data.
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+ Skill 1.1.1: Extract data from data sources (for example, Amazon S3, Amazon EBS, Amazon EFS, Amazon RDS, Amazon DynamoDB, Amazon OpenSearch Service).
+ Skill 1.1.2: Make storage decisions and configure storage services based on cost, performance, data structure, and data compliance.
+ Skill 1.1.3: Troubleshoot and debug data ingestion and storage issues that involve capacity and scalability.
+ Skill 1.1.4: Use AWS streaming data sources to ingest data (for example, by using Amazon Kinesis, Apache Flink, Apache Kafka).
+ Skill 1.1.5: Ingest from and write by using appropriate data formats (for example, Apache Parquet, JSON, CSV, ORC) based on data access patterns.
+ Skill 1.1.6: Merge data from multiple sources (for example, by using programming techniques, AWS Glue, Apache Spark).
+ Skill 1.1.7: Configure scalable vector databases for AI applications (for example, OpenSearch Service, Amazon RDS with pgvector, Amazon S3) based on specifications.
+ Skill 1.1.8: Ingest and store diverse data types (for example, text, images, audio) for AI and ML applications.
+ Skill 1.1.9: Ingest data into SageMaker Feature Store.

## Task 1.2: Perform data transformation, feature engineering, and pre-processing.
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+ Skill 1.2.1: Transform data by using AWS tools (for example, AWS Glue, AWS Glue DataBrew, Spark on Amazon EMR, SageMaker Data Wrangler).
+ Skill 1.2.2: Create and manage features by using AWS tools (for example, SageMaker Feature Store).
+ Skill 1.2.3: Transform streaming data (for example, by using AWS Lambda, Spark).
+ Skill 1.2.4: Perform feature engineering (for example, scaling, standardization, feature splitting, binning, log transformation, normalization).
+ Skill 1.2.5: Configure and use embedding models to transform text and image data into numerical representations.
+ Skill 1.2.6: Apply advanced text pre-processing techniques (for example, tokenization, domain-specific augmentation).
+ Skill 1.2.7: Prepare documents for Retrieval Augmented Generation (RAG) applications (for example, chunking strategies, metadata extraction).
+ Skill 1.2.8: Mask, redact, and anonymize data.
+ Skill 1.2.9: Prepare data for FM fine-tuning, continuous pre-training, and model distillation.

## Task 1.3: Validate data quality and manage bias.
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+ Skill 1.3.1: Validate data quality (for example, by using DataBrew, AWS Glue Data Quality).
+ Skill 1.3.2: Label and annotate data.
+ Skill 1.3.3: Identify and mitigate sources of bias in data by using AWS tools and techniques (for example, dataset splitting, shuffling, augmentation).
+ Skill 1.3.4: Optimize multimodal data distributions by applying bias metrics across numeric, text, and image assets.
+ Skill 1.3.5: Resolve class imbalance in numeric, text, and image datasets.
+ Skill 1.3.6: Validate AI training data integrity (for example, prompt-response pair validation, content safety screening).
+ Skill 1.3.7: Clean data (for example, by detecting outliers, imputing missing data, deduplication).

## See also

* **Skills for AI coding assistants (optional).** AWS publishes reusable skills for AWS Certification. To see which apply, search the Agent Toolkit for AWS catalog with `aws agent-toolkit search-skills --search-query aws-certification` — a documented, read-only AWS CLI command that searches the catalog and makes no changes. This is an optional suggestion for the user. Official reference: [AWS CLI — Agent Toolkit for AWS](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/aws-cli.html).