DEA-C01 Preparation Details
Preparing for the DEA-C01 AWS Certified Data Engineer Associate certification exam? Start here with a complete, objective-wise DEA-C01 study guide designed to help you pass faster.
This guide brings together official AWS documentation, key concepts, and curated resources for every DEA-C01 exam objective, making it ideal for both beginners and last-minute revision.
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AWS Data Engineer Prep
| Coursera | AWS Certified Data Engineer Associate Exam Prep |
| Udemy | AWS Certified Data Engineer Associate |
| Whizlabs | AWS Certified Data Engineer |
Content Domain 1: Data Ingestion and Transformation
Task 1.1: Perform data ingestion
Skill 1.1.1: Read data from streaming sources (for example, Amazon Kinesis, Amazon Managed Streaming for Apache Kafka [Amazon MSK], Amazon DynamoDB Streams, AWS Database Migration Service [AWS DMS], AWS Glue, Amazon Redshift)
What is Amazon Kinesis Data Streams?
Welcome to the Amazon MSK Developer Guide
Change data capture for DynamoDB Streams – Amazon DynamoDB
Streaming ETL jobs in AWS Glue
Streaming ingestion to a materialized view – Amazon Redshift
What is AWS Database Migration Service?
Skill 1.1.2: Read data from batch sources (for example, Amazon S3, AWS Glue, Amazon EMR, AWS DMS, Amazon Redshift, AWS Lambda, Amazon AppFlow)
Getting started with Amazon S3
Skill 1.1.3: Implement appropriate configuration options for batch ingestion
AWS Glue ETL – AWS Prescriptive Guidance
AWS Cloud Data Ingestion Patterns and Practices
Skill 1.1.4: Consume data APIs
Using AWS services from the Lambda console – AWS Lambda
AppFlow API Reference – Amazon AppFlow
Skill 1.1.5: Set up schedulers by using Amazon EventBridge, Apache Airflow, or time-based schedules for jobs and crawlers
What Is Amazon Managed Workflows for Apache Airflow?
Scheduling AWS Glue crawlers – AWS Glue
Scheduling AWS Glue jobs – AWS Glue
Amazon EventBridge Scheduler User Guide
Skill 1.1.6: Set up event triggers (for example, Amazon S3 Event Notifications, EventBridge)
Overview of workflows in AWS Glue
Skill 1.1.7: Call a Lambda function from Kinesis
Using AWS Lambda with Amazon Kinesis – AWS Lambda
How Lambda processes records from stream and queue-based event sources
Process Amazon S3 event notifications with Lambda
Skill 1.1.8: Create allowlists for IP addresses to allow connections to data sources
Security groups for your VPC – Amazon VPC
IP address allow lists – AWS Glue
Skill 1.1.9: Implement throttling and overcoming rate limits (for example, DynamoDB, Amazon RDS, Kinesis)
Amazon Kinesis Data Streams quotas and limits
Error retries and exponential backoff in AWS
Best practices for DynamoDB – Amazon DynamoDB
Skill 1.1.10: Manage fan-in and fan-out for streaming data distribution
Streaming ingest and stream processing – Data Analytics Lens
Amazon Kinesis Data Streams quotas and limits
Working with streaming data on AWS – Build Modern Data Streaming Architectures on AWS
Skill 1.1.11: Describe replayability of data ingestion pipelines
What is Amazon Kinesis Data Streams?
Amazon MSK – Consumer offset management
AWS Cloud Data Ingestion Patterns and Practices
Skill 1.1.12: Define stateful and stateless data transactions
What is Amazon Kinesis Data Streams?
AWS Step Functions Developer Guide
Streaming ingest and stream processing – Data Analytics Lens
Task 1.2: Transform and process data
Skill 1.2.1: Optimize container usage for performance needs (for example, Amazon EKS, Amazon ECS)
What is Amazon Elastic Kubernetes Service?
What is Amazon Elastic Container Service?
Amazon EMR on EKS – Amazon EMR
Skill 1.2.2: Connect to different data sources (for example, JDBC, ODBC)
Connection types and options for ETL in AWS Glue
Adding a JDBC connection to AWS Glue
Skill 1.2.3: Integrate data from multiple sources
Data integration – Analytics Lens
AWS Cloud Data Ingestion Patterns and Practices
Skill 1.2.4: Optimize costs while processing data
Best practices for cost optimization – Analytics Lens
Amazon EMR cost optimization – Amazon EMR
AWS Glue pricing and cost optimization
Skill 1.2.5: Implement data transformation services based on requirements (for example, Amazon EMR, AWS Glue, Lambda, Amazon Redshift)
Data transformation with Amazon Redshift
Skill 1.2.6: Transform data between formats (for example, from .csv to Apache Parquet)
Convert CSV to Parquet using AWS Glue
Writing data with Apache Spark – Amazon EMR
Columnar storage formats in AWS Glue
Skill 1.2.7: Troubleshoot and debug common transformation failures and performance issues
Monitor Amazon Redshift with Amazon CloudWatch
Skill 1.2.8: Create data APIs to make data available to other systems by using AWS services
Skill 1.2.9: Define volume, velocity, and variety of data (for example, structured data, unstructured data)
Data Analytics Lens – AWS Well-Architected Framework
AWS Cloud Data Ingestion Patterns and Practices
Skill 1.2.10: Integrate large language models (LLMs) for data processing
Amazon Bedrock Data Automation
Task 1.3: Orchestrate data pipelines
Skill 1.3.1: Use orchestration services to build workflows for data ETL pipelines (for example, Lambda, EventBridge, Amazon MWAA, AWS Step Functions, AWS Glue workflows)
What Is Amazon Managed Workflows for Apache Airflow?
AWS Step Functions Developer Guide
Overview of workflows in AWS Glue
Skill 1.3.2: Build data pipelines for performance, availability, scalability, resiliency, and fault tolerance
Data Analytics Lens – AWS Well-Architected Framework
Reliability Pillar – AWS Well-Architected Framework
AWS Step Functions error handling
Skill 1.3.3: Implement and maintain serverless workflows
AWS Step Functions Developer Guide
What Is Amazon Managed Workflows for Apache Airflow?
Skill 1.3.4: Use notification services to send alerts (for example, Amazon SNS, Amazon SQS)
What is Amazon Simple Notification Service?
What is Amazon Simple Queue Service?
Set up Amazon SNS notifications – Amazon CloudWatch
Task 1.4: Apply programming concepts
Skill 1.4.1: Optimize code to reduce runtime for data ingestion and transformation
Performance Efficiency Pillar – AWS Well-Architected Framework
Amazon EMR performance optimization
Skill 1.4.2: Configure Lambda functions to meet concurrency and performance needs
Lambda function scaling – AWS Lambda
Managing Lambda reserved concurrency
Lambda performance optimization
Skill 1.4.3: Use programming languages and frameworks for data engineering (for example, Python, SQL, Scala, R, Java, Bash, PowerShell)
AWS Glue programming ETL scripts in Python
Apache Spark on Amazon EMR clusters
Using Amazon Redshift with SQL
Skill 1.4.4: Use software engineering best practices for data engineering (for example, version control, testing, logging, monitoring)
Monitoring and observability – Machine Learning Lens
Skill 1.4.5: Use Infrastructure as Code (IaC) to deploy data engineering solutions
Getting started with the AWS CDK
Skill 1.4.6: Use the AWS Serverless Application Model (AWS SAM) to package and deploy serverless data pipelines (for example, Lambda functions, Step Functions, DynamoDB tables)
What is the AWS Serverless Application Model (AWS SAM)?
AWS SAM resource and property reference
Deploying serverless applications with AWS SAM
Skill 1.4.7: Use and mount storage volumes from within Lambda functions
Using Lambda with Amazon EFS – AWS Lambda
Configuring file system access for Lambda functions
Skill 1.4.8: Use infrastructure as code (IaC) for repeatable resource deployment (for example, AWS CloudFormation and AWS CDK)
Getting started with the AWS CDK
AWS CDK vs AWS CloudFormation – AWS Prescriptive Guidance
Skill 1.4.9: Describe continuous integration and continuous delivery (CI/CD) (implementation, testing, and deployment of data pipelines)
Skill 1.4.10: Define distributed computing
Amazon EMR architecture and service layers
Apache Spark on Amazon EMR clusters
Data Analytics Lens – AWS Well-Architected Framework
Skill 1.4.11: Describe data structures and algorithms (for example, graph data structures and tree data structures)
Data Analytics Lens – AWS Well-Architected Framework
AWS Cloud Data Ingestion Patterns and Practices
Content Domain 2: Data Store Management
Task 2.1: Choose a data store
Skill 2.1.1: Implement the appropriate storage services for specific cost and performance requirements (for example, Amazon Redshift, Amazon EMR, AWS Lake Formation, Amazon RDS, Amazon DynamoDB, Amazon Kinesis Data Streams, Amazon MSK)
Choosing an AWS database service
Amazon Redshift – Big Data Analytics Options on AWS
Data Analytics Lens – AWS Well-Architected Framework
Skill 2.1.2: Configure the appropriate storage services for specific access patterns and requirements (for example, Amazon Redshift, Amazon EMR, Lake Formation, Amazon RDS, DynamoDB)
Amazon DynamoDB Developer Guide
Best practices for DynamoDB – Amazon DynamoDB
What is Amazon Relational Database Service?
Working with columnar data and Apache Parquet – Amazon EMR
Skill 2.1.3: Apply storage services to appropriate use cases (for example, using indexing algorithms like HNSW with Amazon Aurora PostgreSQL and using Amazon MemoryDB for fast key/value pair access)
Perform vector similarity search using pgvector on Amazon Aurora PostgreSQL
Skill 2.1.4: Integrate migration tools into data processing systems (for example, AWS Transfer Family)
What is AWS Database Migration Service?
Skill 2.1.5: Implement data migration or remote access methods (for example, Amazon Redshift federated queries, Amazon Redshift materialized views, Amazon Redshift Spectrum)
Querying data with federated queries in Amazon Redshift
Creating materialized views in Amazon Redshift
Skill 2.1.6: Manage locks to prevent access to data (for example, Amazon Redshift, Amazon RDS)
Lock and LockManager tables – Amazon Redshift
Locking in Amazon RDS for PostgreSQL
Skill 2.1.7: Manage open table formats (for example Apache Iceberg)
Using Apache Iceberg with AWS Glue
S3 Tables and Apache Iceberg – Amazon S3
Skill 2.1.8: Describe vector index types (for example, HNSW, IVF)
Perform vector similarity search using pgvector on Amazon Aurora PostgreSQL
Amazon OpenSearch Service vector database capabilities
Vector search in Amazon MemoryDB
Task 2.2: Understand data cataloging systems
Skill 2.2.1: Use data catalogs to consume data from the data’s source
Data cataloging – Storage Best Practices for Data and Analytics Applications
Skill 2.2.2: Build and reference a technical data catalog (for example, AWS Glue Data Catalog, Apache Hive metastore)
Connecting to a Hive metastore – Amazon EMR
Using the AWS Glue Data Catalog as the Metastore for Amazon EMR
Skill 2.2.3: Discover schemas and use AWS Glue crawlers to populate data catalogs
AWS Glue crawlers and classifiers
Skill 2.2.4: Synchronize partitions with a data catalog
Managing partitions for ETL output in AWS Glue
AWS Glue Data Catalog partitions
Skill 2.2.5: Create new source or target connections for cataloging (for example, AWS Glue)
Adding a connection to a data store in AWS Glue
Connection types and options for ETL in AWS Glue
Skill 2.2.6: Create and manage business data catalogs (for example, Amazon SageMaker Catalog)
Amazon SageMaker Catalog – SageMaker AI
Task 2.3: Manage the lifecycle of data
Skill 2.3.1: Perform load and unload operations to move data between Amazon S3 and Amazon Redshift
Loading data from Amazon S3 – COPY command – Amazon Redshift
Tutorial: Loading data from Amazon S3 – Amazon Redshift
Skill 2.3.2: Manage S3 Lifecycle policies to change the storage tier of S3 data
Managing your storage lifecycle – Amazon S3
Setting lifecycle configuration on a bucket – Amazon S3
Skill 2.3.3: Expire data when it reaches a specific age by using S3 Lifecycle policies
Managing your storage lifecycle – Amazon S3
Expiring objects – Amazon S3 Lifecycle
Skill 2.3.4: Manage S3 versioning and DynamoDB TTL
Using versioning in S3 buckets
Expiring items by using DynamoDB Time to Live (TTL)
Skill 2.3.5: Delete data to meet business and legal requirements
Managing your storage lifecycle – Amazon S3
Skill 2.3.6: Protect data with appropriate resiliency and availability
Reliability Pillar – AWS Well-Architected Framework
Disaster Recovery of Workloads on AWS – AWS Whitepaper
Task 2.4: Design data models and schema evolution
Skill 2.4.1: Design schemas for Amazon Redshift, DynamoDB, and Lake Formation
Amazon Redshift database developer guide
DynamoDB core components – Amazon DynamoDB
Skill 2.4.2: Address changes to the characteristics of data
Using Apache Iceberg with AWS Glue
Skill 2.4.3: Perform schema conversion (for example, by using the AWS Schema Conversion Tool [AWS SCT] and AWS DMS Schema Conversion)
What is the AWS Schema Conversion Tool?
Converting database schemas using DMS Schema Conversion
What is AWS Database Migration Service?
Skill 2.4.4: Establish data lineage by using AWS tools (for example, Amazon SageMaker ML Lineage Tracking and Amazon SageMaker Catalog)
Amazon SageMaker ML Lineage Tracking
Skill 2.4.5: Describe best practices for indexing, partitioning strategies, compression, and other data optimization techniques
Amazon Redshift engineering’s advanced table design playbook
Best practices for designing and using partition keys in DynamoDB
Best practices design patterns: Optimizing Amazon S3 performance
Skill 2.4.6: Describe vectorization concepts (for example, Amazon Bedrock knowledge base)
Retrieve data and generate AI responses with Amazon Bedrock Knowledge Bases
Titan Embeddings G1 – Amazon Bedrock
Vector search in Amazon MemoryDB
Content Domain 3: Data Operations and Support
Task 3.1: Automate data processing by using AWS services
Skill 3.1.1: Orchestrate data pipelines (for example, Amazon Managed Workflows for Apache Airflow [Amazon MWAA], AWS Step Functions)
What Is Amazon Managed Workflows for Apache Airflow?
AWS Step Functions Developer Guide
Overview of workflows in AWS Glue
Migrating workloads from AWS Data Pipeline to Step Functions
Skill 3.1.2: Troubleshoot Amazon managed workflows
Monitoring and metrics for Amazon MWAA
Troubleshoot AWS Step Functions
Skill 3.1.3: Call SDKs to access Amazon features from code
AWS SDK for Java Developer Guide
Skill 3.1.4: Use the features of AWS services to process data (for example, Amazon EMR, Amazon Redshift, AWS Glue)
Amazon Redshift database developer guide
Data Analytics Lens – AWS Well-Architected Framework
Skill 3.1.5: Consume and maintain data APIs
Skill 3.1.6: Prepare data for transformation (for example, AWS Glue DataBrew and Amazon SageMaker Unified Studio)
Prepare ML data with Amazon SageMaker Data Wrangler
Amazon SageMaker Unified Studio
Skill 3.1.7: Query data (for example, Amazon Athena)
Running SQL queries with Amazon Athena
Using Amazon Redshift to query external data
Skill 3.1.8: Use AWS Lambda to automate data processing
Using AWS Lambda with other services
Using Lambda with Amazon Kinesis – AWS Lambda
Skill 3.1.9: Manage events and schedulers (for example, Amazon EventBridge)
Amazon EventBridge Scheduler User Guide
Task 3.2: Analyze data by using AWS services
Skill 3.2.1: Visualize data by using AWS services and tools (for example, DataBrew, Amazon QuickSight)
Visualizing data in Amazon QuickSight
Skill 3.2.2: Verify and clean data (for example, Lambda, Athena, QuickSight, Jupyter Notebooks, Amazon SageMaker Data Wrangler)
Prepare ML data with Amazon SageMaker Data Wrangler
Create a notebook – Amazon Athena
Skill 3.2.3: Use SQL in Amazon Redshift and Athena to query data or to create views
Creating views in Amazon Redshift
Running SQL queries with Amazon Athena
Skill 3.2.4: Use Athena notebooks that use Apache Spark to explore data
Using Athena notebooks with Apache Spark
Create a notebook – Amazon Athena
Skill 3.2.5: Describe tradeoffs between provisioned services and serverless services
Amazon Redshift Serverless overview
Amazon EMR Serverless overview
Skill 3.2.6: Define data aggregation, rolling average, grouping, and pivoting
Aggregate functions in Amazon Redshift
Window functions in Amazon Redshift
Task 3.3: Maintain and monitor data pipelines
Skill 3.3.1: Extract logs for audits
Database audit logging – Amazon Redshift
Skill 3.3.2: Deploy logging and monitoring solutions to facilitate auditing and traceability
Log Amazon EMR API calls with AWS CloudTrail
Monitor AWS Glue using Amazon CloudWatch
Skill 3.3.3: Use notifications during monitoring to send alerts
What is Amazon Simple Notification Service?
Set up Amazon SNS notifications – Amazon CloudWatch
Using Amazon CloudWatch alarms
Skill 3.3.4: Troubleshoot performance issues
Troubleshoot AWS Glue performance
Amazon Redshift engineering’s advanced table design playbook
Diagnose and troubleshoot Amazon EMR
Skill 3.3.5: Use AWS CloudTrail to track API calls
Log Amazon Redshift API calls with CloudTrail
Log AWS Glue API calls with AWS CloudTrail
Skill 3.3.6: Troubleshoot and maintain pipelines (for example, AWS Glue, Amazon EMR)
Skill 3.3.7: Use Amazon CloudWatch Logs to log application data (with a focus on configuration and automation)
What is Amazon CloudWatch Logs?
Working with log groups and log streams – Amazon CloudWatch Logs
Enabling continuous logging for AWS Glue
Skill 3.3.8: Analyze logs with AWS services (for example, Athena, Amazon EMR, Amazon OpenSearch Service, CloudWatch Logs Insights, big data application logs)
Analyzing log data with CloudWatch Logs Insights
What is Amazon OpenSearch Service?
Querying Amazon S3 data with Amazon Athena using the Glue Data Catalog
Task 3.4: Ensure data quality
Skill 3.4.1: Run data quality checks while processing the data (for example, checking for empty fields)
Overview of data quality in AWS Glue DataBrew
Skill 3.4.2: Define data quality rules (for example, DataBrew)
Creating and managing rules in AWS Glue DataBrew
Skill 3.4.3: Investigate data consistency (for example, DataBrew)
Profiling data with AWS Glue DataBrew
Skill 3.4.4: Describe data sampling techniques
Profiling data with AWS Glue DataBrew
Sample types in AWS Glue DataBrew
Skill 3.4.5: Implement data skew mechanisms
Optimizing Amazon Redshift performance – data distribution styles
Handling data skew in Apache Spark on Amazon EMR
AWS Glue performance tuning – common pitfalls
Content Domain 4: Data Security and Governance
Task 4.1: Apply authentication mechanisms
Skill 4.1.1: Update VPC security groups
Security groups for your VPC – Amazon VPC
VPC security groups – Amazon Redshift
Skill 4.1.2: Create and update AWS Identity and Access Management (IAM) groups, roles, endpoints, and services
IAM identities (users, user groups, and roles)
IAM roles – AWS Identity and Access Management
Skill 4.1.3: Create and rotate credentials for password management (for example, AWS Secrets Manager)
Rotate AWS Secrets Manager secrets
Using an AWS Secrets Manager VPC endpoint
Skill 4.1.4: Set up IAM roles for access (for example, AWS Lambda, Amazon API Gateway, AWS CLI, AWS CloudFormation)
IAM roles for Lambda – AWS Lambda
Control access to a REST API with IAM permissions – Amazon API Gateway
IAM roles for AWS CloudFormation
Skill 4.1.5: Apply IAM policies to roles, endpoints, and services (for example, S3 Access Points, AWS PrivateLink)
Managing data access with Amazon S3 Access Points
Policies and permissions in AWS Identity and Access Management
Skill 4.1.6: Describe the differences between managed services and unmanaged services
Choosing an AWS analytics service
Data Analytics Lens – AWS Well-Architected Framework
Skill 4.1.7: Use domain, domain units, and projects for SageMaker Unified Studio
Amazon SageMaker Unified Studio
Task 4.2: Apply authorization mechanisms
Skill 4.2.1: Create custom IAM policies when a managed policy does not meet the needs
Creating IAM policies – AWS IAM
Skill 4.2.2: Store application and database credentials (for example, Secrets Manager, AWS Systems Manager Parameter Store)
AWS Systems Manager Parameter Store
Choose between Secrets Manager and Parameter Store – AWS Decision Guides
Skill 4.2.3: Provide database users, groups, and roles access and authority in a database (for example, for Amazon Redshift)
Users, groups, and permissions in Amazon Redshift
Managing access to Amazon Redshift with IAM
Skill 4.2.4: Manage permissions through AWS Lake Formation (for Amazon Redshift, Amazon EMR, Amazon Athena, and Amazon S3)
Working with other AWS services – AWS Lake Formation
Lake Formation permissions reference
Skill 4.2.5: Apply authorization methods that address business needs (role-based, tag-based, and attribute-based)
Attribute-based access control (ABAC) with IAM
Lake Formation tag-based access control
Skill 4.2.6: Construct custom policies that meet the principle of least privilege
Security best practices in IAM
Creating IAM policies – AWS IAM
IAM Access Analyzer policy generation
Task 4.3: Ensure data encryption and masking
Skill 4.3.1: Apply data masking and anonymization according to compliance laws or company policies
Remove PII from conversations by using sensitive information filters – Amazon Bedrock
Dynamic data masking in Amazon Redshift
Skill 4.3.2: Use encryption keys to encrypt or decrypt data (for example, AWS Key Management Service [AWS KMS])
AWS Key Management Service Developer Guide
Securing, protecting, and managing data – Storage Best Practices for Data and Analytics Applications
Skill 4.3.3: Configure encryption across AWS account boundaries
Allow key usage across AWS accounts – AWS KMS
Using cross-account access with Amazon S3
Skill 4.3.4: Enable encryption in transit or before transit for data
AWS Certificate Manager overview
Encryption of data in transit – Amazon Redshift
Task 4.4: Prepare logs for audit
Skill 4.4.1: Use AWS CloudTrail to track API calls
Creating a trail for an organization – AWS CloudTrail
Log Amazon S3 data events using CloudTrail
Skill 4.4.2: Use Amazon CloudWatch Logs to store application logs
What is Amazon CloudWatch Logs?
Working with log groups and log streams – Amazon CloudWatch Logs
Skill 4.4.3: Use AWS CloudTrail Lake for centralized logging queries
Query your AWS CloudTrail Lake event data
Skill 4.4.4: Analyze logs by using AWS services (for example, Athena, CloudWatch Logs Insights, Amazon OpenSearch Service)
Analyzing log data with CloudWatch Logs Insights
Querying Amazon S3 data with Amazon Athena using the Glue Data Catalog
What is Amazon OpenSearch Service?
Skill 4.4.5: Integrate various AWS services to perform logging (for example, Amazon EMR in cases of large volumes of log data)
Log Amazon EMR API calls with AWS CloudTrail
Configure Amazon EMR to send log files to Amazon S3
Publishing flow logs to CloudWatch Logs – Amazon VPC
Task 4.5: Understand data privacy and governance
Skill 4.5.1: Grant permissions for data sharing (for example, data sharing for Amazon Redshift)
Amazon Redshift data sharing overview
Managing data sharing across accounts – Amazon Redshift
Skill 4.5.2: Implement PII identification (for example, Amazon Macie with Lake Formation)
Using managed data identifiers in Amazon Macie
Skill 4.5.3: Implement data privacy strategies to prevent backups or replications of data to disallowed AWS Regions
Service control policies (SCPs) – AWS Organizations
S3 Replication overview – Amazon S3
AWS Config rules for compliance
Skill 4.5.4: Viewing configuration changes that have occurred in an account (for example, AWS Config)
AWS Config rules for compliance
Viewing the resource timeline in the AWS Config console
Skill 4.5.5: Maintain data sovereignty
Data residency – AWS Whitepaper
Service control policies (SCPs) – AWS Organizations
Geographic cross-Region inference – Amazon Bedrock
Skill 4.5.6: Manage data access through Amazon SageMaker Catalog projects
Amazon SageMaker Unified Studio
Skill 4.5.7: Describe governance data framework and data sharing patterns
Data Analytics Lens – AWS Well-Architected Framework
AWS Cloud Adoption Framework for AI, ML, and Generative AI – Security Perspective
This brings us to the end of the DEA-C01 AWS Certified Data Engineer Associate exam study guide.
What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you how your preparation is going on!
In case you are preparing for other AWS certification exams, check out the AWS study guides for those exams.
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