Content domains
The 4 domains of DEA-C01, with every task statement and its objectives from the official guide. Study a whole domain, or drill a single task.
1Data Ingestion and Transformation
34% of examStudy domain
1.1Perform data ingestion23 q>
Knowledge of
- Streaming ingestion sources and their characteristics (Kinesis Data Streams, Amazon MSK, DynamoDB Streams, AWS DMS, AWS Glue, Amazon Redshift)
- Batch ingestion sources and their characteristics (Amazon S3, AWS Glue, Amazon EMR, AWS DMS, Amazon Redshift, AWS Lambda, Amazon AppFlow)
- Batch size, frequency, and other tunable configuration options for batch ingestion
- Scheduling mechanisms: EventBridge rules, Apache Airflow DAGs, and cron-style time-based triggers for jobs and crawlers
- Event-driven triggers such as S3 Event Notifications and EventBridge events
- Throttling behavior and service rate limits in DynamoDB, Amazon RDS, and Kinesis
- Fan-in and fan-out patterns for distributing streaming data to multiple consumers
- Replayability of ingestion pipelines and how retention windows enable reprocessing
- The difference between stateful and stateless data transactions
- Network access requirements for reaching data sources, including IP allowlists
Skills in
- Reading data from streaming sources such as Kinesis, Amazon MSK, and DynamoDB Streams
- Reading data from batch sources such as Amazon S3, AWS Glue, Amazon EMR, and Amazon AppFlow
- Choosing appropriate configuration options for batch ingestion jobs
- Consuming data APIs to pull data into a pipeline
- Setting up schedulers with EventBridge, Apache Airflow, or time-based schedules for jobs and crawlers
- Setting up event triggers such as S3 Event Notifications and EventBridge rules
- Invoking a Lambda function from a Kinesis stream
- Creating IP address allowlists so pipelines can connect to data sources
- Handling throttling and working around rate limits in DynamoDB, Amazon RDS, and Kinesis
- Managing fan-in and fan-out for streaming data distribution
- Explaining how to make an ingestion pipeline replayable
- Distinguishing stateful from stateless data transactions
1.2Transform and process data23 q>
Knowledge of
- Container sizing and tuning for data workloads on Amazon EKS and Amazon ECS
- Connectivity options to external data sources, including JDBC and ODBC drivers
- Transformation services and when each fits: Amazon EMR, AWS Glue, AWS Lambda, and Amazon Redshift
- Columnar and row-based file formats, and the tradeoffs of converting between them (for example, .csv to Apache Parquet)
- Cost drivers in data processing, such as compute time, data scanned, and instance purchase options
- Common causes of transformation failures and performance bottlenecks
- The volume, velocity, and variety dimensions of data, and structured compared with unstructured data
- Patterns for exposing processed data to other systems through APIs
- Use cases for integrating large language models into data processing workflows
Skills in
- Optimizing container usage for performance needs on Amazon EKS and Amazon ECS
- Connecting to different data sources through JDBC and ODBC
- Integrating and joining data from multiple sources
- Reducing cost while processing data
- Selecting and implementing transformation services based on requirements
- Converting data between formats, such as .csv to Apache Parquet
- Troubleshooting and debugging transformation failures and performance issues
- Creating data APIs with AWS services to make data available to other systems
- Characterizing the volume, velocity, and variety of a dataset
- Integrating large language models (LLMs) into data processing
1.3Orchestrate data pipelines23 q>
Knowledge of
- Orchestration services and their tradeoffs: AWS Lambda, EventBridge, Amazon MWAA, AWS Step Functions, and AWS Glue workflows
- Design characteristics of a well-built pipeline: performance, availability, scalability, resiliency, and fault tolerance
- Serverless workflow patterns, including retries, error handling, and state management
- Notification services for pipeline alerting, including Amazon SNS and Amazon SQS
Skills in
- Building ETL workflows with orchestration services such as Step Functions, Amazon MWAA, and AWS Glue workflows
- Designing pipelines for performance, availability, scalability, resiliency, and fault tolerance
- Implementing and maintaining serverless workflows
- Sending alerts from a pipeline by using notification services such as Amazon SNS and Amazon SQS
1.4Apply programming concepts23 q>
Knowledge of
- Techniques for reducing runtime in ingestion and transformation code
- Lambda concurrency models, memory sizing, and performance tuning
- Languages and frameworks common to data engineering: Python, SQL, Scala, R, Java, Bash, and PowerShell
- Software engineering practices applied to data work: version control, testing, logging, and monitoring
- Infrastructure as code tools for repeatable deployment, including AWS CloudFormation and AWS CDK
- AWS SAM for packaging and deploying serverless data pipelines
- Storage volume options that can be mounted inside Lambda functions
- CI/CD concepts applied to implementing, testing, and deploying data pipelines
- Distributed computing fundamentals
- Data structures and algorithms, including graph and tree structures
Skills in
- Optimizing code to reduce runtime for ingestion and transformation
- Configuring Lambda functions to meet concurrency and performance needs
- Writing data engineering code in languages such as Python, SQL, and Scala
- Applying software engineering best practices such as version control, testing, and logging
- Deploying data engineering solutions with infrastructure as code
- Packaging and deploying serverless data pipelines with AWS SAM
- Mounting and using storage volumes from within Lambda functions
- Using CloudFormation and AWS CDK for repeatable resource deployment
- Describing CI/CD for data pipelines
- Explaining distributed computing and relevant data structures and algorithms
2Data Store Management
26% of examStudy domain
2.1Choose a data store23 q>
Knowledge of
- Cost and performance profiles of storage and data services: Amazon Redshift, Amazon EMR, AWS Lake Formation, Amazon RDS, DynamoDB, Kinesis Data Streams, and Amazon MSK
- How access patterns drive data store configuration choices
- Purpose-built store use cases, such as HNSW indexing on Aurora PostgreSQL and MemoryDB for fast key/value access
- Migration and transfer tooling that feeds data processing systems, such as AWS Transfer Family
- Remote access and federation options: Amazon Redshift federated queries, materialized views, and Redshift Spectrum
- Locking behavior in Amazon Redshift and Amazon RDS
- Open table formats such as Apache Iceberg and what they add over plain files
- Vector index types, including HNSW and IVF
Skills in
- Selecting storage services that meet specific cost and performance requirements
- Configuring storage services for specific access patterns
- Matching purpose-built stores to their use cases, including vector and key/value workloads
- Integrating migration tools such as AWS Transfer Family into data processing systems
- Implementing remote access with Redshift federated queries, materialized views, and Redshift Spectrum
- Managing locks that prevent access to data in Amazon Redshift and Amazon RDS
- Managing open table formats such as Apache Iceberg
- Comparing vector index types such as HNSW and IVF
2.2Understand data cataloging systems23 q>
Knowledge of
- The role of a data catalog in making source data discoverable and queryable
- Technical catalogs: AWS Glue Data Catalog and the Apache Hive metastore
- Schema discovery with AWS Glue crawlers
- Partition management and how partitions stay in sync with a catalog
- Source and target connection objects in AWS Glue
- Business catalogs such as Amazon SageMaker Catalog and how they differ from technical catalogs
Skills in
- Using data catalogs to consume data from its source
- Building and referencing a technical data catalog such as the AWS Glue Data Catalog
- Discovering schemas and populating catalogs with AWS Glue crawlers
- Synchronizing partitions with a data catalog
- Creating source and target connections for cataloging in AWS Glue
- Creating and managing business data catalogs such as Amazon SageMaker Catalog
2.3Manage the lifecycle of data24 q>
Knowledge of
- Load and unload operations between Amazon S3 and Amazon Redshift
- S3 storage classes and how S3 Lifecycle policies move data between tiers
- Expiration rules that delete data at a specific age
- S3 versioning behavior and DynamoDB TTL
- Legal and business drivers for data deletion and retention
- Resiliency and availability options that protect stored data, including replication and backups
Skills in
- Loading and unloading data between Amazon S3 and Amazon Redshift
- Managing S3 Lifecycle policies to change storage tiers
- Expiring data at a specific age with S3 Lifecycle policies
- Managing S3 versioning and DynamoDB TTL
- Deleting data to meet business and legal requirements
- Protecting data with appropriate resiliency and availability configurations
2.4Design data models and schema evolution23 q>
Knowledge of
- Schema design for Amazon Redshift, DynamoDB, and Lake Formation
- How changes in data characteristics force schema and model changes
- Schema conversion tooling: AWS SCT and AWS DMS Schema Conversion
- Data lineage tracking with Amazon SageMaker ML Lineage Tracking and Amazon SageMaker Catalog
- Optimization techniques: indexing, partitioning strategies, compression, and file sizing
- Vectorization concepts, including Amazon Bedrock knowledge bases
Skills in
- Designing schemas for Amazon Redshift, DynamoDB, and Lake Formation
- Adapting models when the characteristics of data change
- Performing schema conversion with AWS SCT and AWS DMS Schema Conversion
- Establishing data lineage with AWS tools such as SageMaker Catalog
- Applying indexing, partitioning, compression, and other optimization techniques
- Explaining vectorization concepts, including Amazon Bedrock knowledge bases
3Data Operations and Support
22% of examStudy domain
3.1Automate data processing by using AWS services23 q>
Knowledge of
- Orchestration services for automated pipelines, including Amazon MWAA and AWS Step Functions
- Common failure modes in Amazon managed workflows and how to diagnose them
- AWS SDKs and how code calls AWS features programmatically
- Processing features of Amazon EMR, Amazon Redshift, and AWS Glue
- Data API creation and maintenance
- Data preparation tooling such as AWS Glue DataBrew and Amazon SageMaker Unified Studio
- Interactive querying with Amazon Athena
- Lambda as an automation target for data processing
- Event and schedule management with Amazon EventBridge
Skills in
- Orchestrating data pipelines with Amazon MWAA and AWS Step Functions
- Troubleshooting Amazon managed workflows
- Calling AWS SDKs to access AWS features from code
- Processing data with features of Amazon EMR, Amazon Redshift, and AWS Glue
- Consuming and maintaining data APIs
- Preparing data for transformation with AWS Glue DataBrew and SageMaker Unified Studio
- Querying data with Amazon Athena
- Automating data processing with AWS Lambda
- Managing events and schedulers with Amazon EventBridge
3.2Analyze data by using AWS services23 q>
Knowledge of
- Visualization tooling, including AWS Glue DataBrew and Amazon QuickSight
- Data verification and cleaning options: Lambda, Athena, QuickSight, Jupyter notebooks, and SageMaker Data Wrangler
- SQL in Amazon Redshift and Athena, including views
- Athena notebooks backed by Apache Spark for exploratory analysis
- Tradeoffs between provisioned and serverless analytics services
- Analytical operations: aggregation, rolling average, grouping, and pivoting
Skills in
- Visualizing data with AWS services such as QuickSight and DataBrew
- Verifying and cleaning data with Lambda, Athena, QuickSight, or SageMaker Data Wrangler
- Writing SQL in Amazon Redshift and Athena to query data and create views
- Exploring data with Athena notebooks that use Apache Spark
- Weighing provisioned services against serverless services for an analytics workload
- Applying aggregation, rolling averages, grouping, and pivoting to datasets
3.3Maintain and monitor data pipelines23 q>
Knowledge of
- Log extraction for audit purposes
- Logging and monitoring solutions that support auditing and traceability
- Alerting through notification services during monitoring
- Common performance problems in pipelines and their symptoms
- AWS CloudTrail as the record of API calls
- Troubleshooting and maintenance of AWS Glue and Amazon EMR pipelines
- Amazon CloudWatch Logs configuration and automation for application logging
- Log analysis with Athena, Amazon EMR, Amazon OpenSearch Service, and CloudWatch Logs Insights
Skills in
- Extracting logs for audits
- Deploying logging and monitoring solutions for auditing and traceability
- Sending alerts through notifications during monitoring
- Troubleshooting pipeline performance issues
- Tracking API calls with AWS CloudTrail
- Troubleshooting and maintaining AWS Glue and Amazon EMR pipelines
- Logging application data to Amazon CloudWatch Logs with appropriate configuration and automation
- Analyzing logs with Athena, Amazon EMR, OpenSearch Service, and CloudWatch Logs Insights
3.4Ensure data quality3 q>
Knowledge of
- In-flight data quality checks, such as detecting empty or malformed fields
- Data quality rule definition in tools such as AWS Glue DataBrew
- Data consistency investigation techniques
- Data sampling techniques and when each is appropriate
- Data skew, its causes, and mechanisms that mitigate it
Skills in
- Running data quality checks while data is being processed
- Defining data quality rules in tools such as DataBrew
- Investigating data consistency problems
- Choosing appropriate data sampling techniques
- Implementing mechanisms that address data skew
4Data Security and Governance
18% of examStudy domain
4.1Apply authentication mechanisms3 q>
Knowledge of
- VPC security group rules and how they gate access to data resources
- IAM groups, roles, endpoints, and service principals
- Credential creation and rotation with AWS Secrets Manager
- IAM roles for service access from Lambda, API Gateway, the AWS CLI, and CloudFormation
- IAM policies applied to roles, endpoints, and services, including S3 Access Points and AWS PrivateLink
- The differences between managed and unmanaged services and their security implications
- Domains, domain units, and projects in SageMaker Unified Studio
Skills in
- Updating VPC security groups to permit intended access
- Creating and updating IAM groups, roles, endpoints, and services
- Creating and rotating credentials with AWS Secrets Manager
- Setting up IAM roles for access from Lambda, API Gateway, the CLI, and CloudFormation
- Applying IAM policies to roles, endpoints, and services such as S3 Access Points and PrivateLink
- Distinguishing managed services from unmanaged services
- Using domains, domain units, and projects in SageMaker Unified Studio
4.2Apply authorization mechanisms3 q>
Knowledge of
- When a custom IAM policy is required instead of an AWS managed policy
- Credential storage options: AWS Secrets Manager and AWS Systems Manager Parameter Store
- Database-level users, groups, and roles, especially in Amazon Redshift
- AWS Lake Formation permissions across Amazon Redshift, Amazon EMR, Athena, and Amazon S3
- Authorization models: role-based, tag-based, and attribute-based access control
- The principle of least privilege and how to express it in policy documents
Skills in
- Writing custom IAM policies when managed policies do not fit
- Storing application and database credentials in Secrets Manager or Parameter Store
- Granting database users, groups, and roles the right authority in Amazon Redshift
- Managing permissions through AWS Lake Formation for Redshift, EMR, Athena, and S3
- Applying role-based, tag-based, and attribute-based authorization to meet business needs
- Constructing custom policies that follow least privilege
4.3Ensure data encryption and masking3 q>
Knowledge of
- Data masking and anonymization approaches and the compliance rules that require them
- AWS KMS keys and how services use them to encrypt and decrypt data
- Cross-account encryption configuration, including key policies and grants
- Encryption in transit and encryption before transit
Skills in
- Applying data masking and anonymization to satisfy compliance laws or company policy
- Encrypting and decrypting data with AWS KMS keys
- Configuring encryption that works across AWS account boundaries
- Enabling encryption in transit or before transit
4.4Prepare logs for audit3 q>
Knowledge of
- AWS CloudTrail and the API activity it records
- Amazon CloudWatch Logs as a store for application logs
- AWS CloudTrail Lake for centralized log querying
- Log analysis services: Athena, CloudWatch Logs Insights, and Amazon OpenSearch Service
- Integrating services such as Amazon EMR when log volume is very large
Skills in
- Tracking API calls with AWS CloudTrail
- Storing application logs in Amazon CloudWatch Logs
- Running centralized logging queries with AWS CloudTrail Lake
- Analyzing logs with Athena, CloudWatch Logs Insights, and OpenSearch Service
- Integrating AWS services such as Amazon EMR to handle large volumes of log data
4.5Understand data privacy and governance3 q>
Knowledge of
- Data sharing permissions, including Amazon Redshift data sharing
- PII identification with Amazon Macie and Lake Formation
- Controls that prevent backups or replication into disallowed AWS Regions
- AWS Config for viewing configuration changes in an account
- Data sovereignty requirements and how Region choice enforces them
- Data access management through Amazon SageMaker Catalog projects
- Governance frameworks and common data sharing patterns
Skills in
- Granting permissions for data sharing, such as Amazon Redshift data sharing
- Implementing PII identification with Amazon Macie and Lake Formation
- Preventing backups or replications of data to disallowed AWS Regions
- Reviewing configuration changes in an account with AWS Config
- Maintaining data sovereignty
- Managing data access through Amazon SageMaker Catalog projects
- Describing governance data frameworks and data sharing patterns