AWS Certified Data Engineer Associate

The AWS Certified Data Engineer Associate (DEA-C01) exam confirms a candidate’s skill in setting up data pipelines and addressing issues related to cost and performance using best practices. The exam also verifies a candidate’s ability to:

• Ingest and transform data, and manage data pipelines with programming concepts.
• Opt for the best data store, devise data models, organize data schemas, and handle data lifecycles.
• Operate, sustain, and supervise data pipelines.
• Evaluate data and guarantee data quality.
• Implement suitable authentication, authorization, data encryption, privacy, and governance.
• Activate logging.

Target Audience

The ideal candidate should possess around 2–3 years of experience in data engineering. They should grasp how the volume, variety, and velocity of data impact aspects like ingestion, transformation, modeling, security, governance, privacy, schema design, and optimal data store design. Additionally, the candidate should have hands-on experience with AWS services for at least 1–2 years.

Recommended general IT knowledge includes:

• Setting up and maintaining extract, transform, and load (ETL) pipelines from ingestion to destination
• Application of high-level programming concepts, regardless of language, as required by the pipeline
• Utilization of Git commands for source control
• Knowledge of data lakes for storing data
• General understanding of networking, storage, and compute concepts

Recommended AWS knowledge for the candidate includes:

• Knowing how to utilize AWS services to complete the tasks outlined in the Introduction section of this exam guide
• Grasping the AWS services related to encryption, governance, protection, and logging for all data within data pipelines
• Being able to compare AWS services to comprehend the differences in cost, performance, and functionality
• Having the skill to structure and execute SQL queries on AWS services
• Understanding how to analyze data, check data quality, and maintain data consistency using AWS services

Exam Details

AWS Data Engineer Associate is an associate-level exam that will have 85 questions. The time duration for the exam is 170 minutes. The exam consists of two types of questions:

• Multiple choice: You choose one correct response from four options, including three incorrect ones (distractors).
• Multiple response: You pick two or more correct responses from five or more options.

The passing score for the exam is 720. The exam cost is 75$ USD and is available in English language.

Course Outline

This exam course ourline contains information about the weightings, content domains, and tasks for the exam. It provide extra details for each task statement to aid in your preparation. The exam is divided into different content domains, each with its own weighting.

Domain 1: Data Ingestion and Transformation

Task Statement 1.1: Perform data ingestion.

Knowledge of:

• Throughput and latency characteristics for AWS services that ingest data
• Data ingestion patterns (for example, frequency and data history)
• Streaming data ingestion
• Batch data ingestion (for example, scheduled ingestion, event-driven ingestion)
• Replayability of data ingestion pipelines
• Stateful and stateless data transactions

Skills in:

• Reading 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)
• Reading data from batch sources (for example, Amazon S3, AWS Glue, Amazon EMR, AWS DMS, Amazon Redshift, AWS Lambda, Amazon AppFlow)
• Implementing appropriate configuration options for batch ingestion
• Consuming data APIs
• Setting up schedulers by using Amazon EventBridge, Apache Airflow, or time-based schedules for jobs and crawlers
• Setting up event triggers (for example, Amazon S3 Event Notifications, EventBridge)
• Calling a Lambda function from Amazon Kinesis
• Creating allowlists for IP addresses to allow connections to data sources
• Implementing throttling and overcoming rate limits (for example, DynamoDB, Amazon RDS, Kinesis)
• Managing fan-in and fan-out for streaming data distribution

Task Statement 1.2: Transform and process data.

Knowledge of:

• Creation of ETL pipelines based on business requirements
• Volume, velocity, and variety of data (for example, structured data, unstructured data)
• Cloud computing and distributed computing
• How to use Apache Spark to process data
• Intermediate data staging locations

Skills in:

• Optimizing container usage for performance needs (for example, Amazon Elastic Kubernetes Service [Amazon EKS], Amazon Elastic Container Service [Amazon ECS])
• Connecting to different data sources (for example, Java Database Connectivity [JDBC], Open Database Connectivity [ODBC])
• Integrating data from multiple sources
• Optimizing costs while processing data
• Implementing data transformation services based on requirements (for example, Amazon EMR, AWS Glue, Lambda, Amazon Redshift)
• Transforming data between formats (for example, from .csv to Apache Parquet)
• Troubleshooting and debugging common transformation failures and performance issues
• Creating data APIs to make data available to other systems by using AWS services

Task Statement 1.3: Orchestrate data pipelines.

Knowledge of:

• How to integrate various AWS services to create ETL pipelines
• Event-driven architecture
• How to configure AWS services for data pipelines based on schedules or dependencies
• Serverless workflows

Skills in:

• Using orchestration services to build workflows for data ETL pipelines (for example, Lambda, EventBridge, Amazon Managed Workflows for Apache Airflow [Amazon MWAA], AWS Step Functions, AWS Glue workflows)
• Building data pipelines for performance, availability, scalability, resiliency, and fault tolerance
• Implementing and maintaining serverless workflows
• Using notification services to send alerts (for example, Amazon Simple Notification Service [Amazon SNS], Amazon Simple Queue Service [Amazon SQS])

Task Statement 1.4: Apply programming concepts.

Knowledge of:

• Continuous integration and continuous delivery (CI/CD) (implementation, testing, and deployment of data pipelines)
• SQL queries (for data source queries and data transformations)
• Infrastructure as code (IaC) for repeatable deployments (for example, AWS Cloud Development Kit [AWS CDK], AWS CloudFormation)
• Distributed computing
• Data structures and algorithms (for example, graph data structures and tree data structures)
• SQL query optimization

Skills in:

• Optimizing code to reduce runtime for data ingestion and transformation
• Configuring Lambda functions to meet concurrency and performance needs
• Performing SQL queries to transform data (for example, Amazon Redshift stored procedures)
• Structuring SQL queries to meet data pipeline requirements
• Using Git commands to perform actions such as creating, updating, cloning, and branching repositories
• Using the AWS Serverless Application Model (AWS SAM) to package and deploy serverless data pipelines (for example, Lambda functions, Step Functions, DynamoDB tables)
• Using and mounting storage volumes from within Lambda functions

Domain 2: Data Store Management

Task Statement 2.1: Choose a data store.

Knowledge of:

• Storage platforms and their characteristics
• Storage services and configurations for specific performance demands
• Data storage formats (for example, .csv, .txt, Parquet)
• How to align data storage with data migration requirements
• How to determine the appropriate storage solution for specific access patterns
• How to manage locks to prevent access to data (for example, Amazon Redshift, Amazon RDS)

Skills in:

• Implementing the appropriate storage services for specific cost and performance requirements (for example, Amazon Redshift, Amazon EMR, AWS Lake Formation, Amazon RDS, DynamoDB, Amazon Kinesis Data Streams, Amazon MSK)
• Configuring the appropriate storage services for specific access patterns and requirements (for example, Amazon Redshift, Amazon EMR, Lake Formation, Amazon RDS, DynamoDB)
• Applying storage services to appropriate use cases (for example, Amazon S3)
• Integrating migration tools into data processing systems (for example, AWS Transfer Family)
• Implementing data migration or remote access methods (for example, Amazon Redshift federated queries, Amazon Redshift materialized views, Amazon Redshift Spectrum)

Task Statement 2.2: Understand data cataloging systems.

Knowledge of:

• How to create a data catalog
• Data classification based on requirements
• Components of metadata and data catalogs

Skills in:

• Using data catalogs to consume data from the data’s source
• Building and referencing a data catalog (for example, AWS Glue Data Catalog, Apache Hive metastore)
• Discovering schemas and using AWS Glue crawlers to populate data catalogs
• Synchronizing partitions with a data catalog
• Creating new source or target connections for cataloging (for example, AWS Glue)

Task Statement 2.3: Manage the lifecycle of data.

Knowledge of:

• Appropriate storage solutions to address hot and cold data requirements
• How to optimize the cost of storage based on the data lifecycle
• How to delete data to meet business and legal requirements
• Data retention policies and archiving strategies
• How to protect data with appropriate resiliency and availability

Skills in:

• Performing load and unload operations to move data between Amazon S3 and Amazon Redshift
• Managing S3 Lifecycle policies to change the storage tier of S3 data
• Expiring data when it reaches a specific age by using S3 Lifecycle policies
• Managing S3 versioning and DynamoDB TTL

Task Statement 2.4: Design data models and schema evolution.

Knowledge of:

• Data modeling concepts
• How to ensure accuracy and trustworthiness of data by using data lineage
• Best practices for indexing, partitioning strategies, compression, and other data optimization techniques
• How to model structured, semi-structured, and unstructured data
• Schema evolution techniques

Skills in:

• Designing schemas for Amazon Redshift, DynamoDB, and Lake Formation
• Addressing changes to the characteristics of data
• Performing schema conversion (for example, by using the AWS Schema Conversion Tool [AWS SCT] and AWS DMS Schema Conversion)
• Establishing data lineage by using AWS tools (for example, Amazon SageMaker ML Lineage Tracking)

Domain 3: Data Operations and Support

Task Statement 3.1: Automate data processing by using AWS services.

Knowledge of:

• How to maintain and troubleshoot data processing for repeatable business outcomes
• API calls for data processing
• Which services accept scripting (for example, Amazon EMR, Amazon Redshift, AWS Glue)

Skills in:

• Orchestrating data pipelines (for example, Amazon MWAA, Step Functions)
• Troubleshooting Amazon managed workflows
• Calling SDKs to access Amazon features from code
• Using the features of AWS services to process data (for example, Amazon EMR, Amazon Redshift, AWS Glue)
• Consuming and maintaining data APIs
• Preparing data transformation (for example, AWS Glue DataBrew)
• Querying data (for example, Amazon Athena)
• Using Lambda to automate data processing
• Managing events and schedulers (for example, EventBridge)

Task Statement 3.2: Analyze data by using AWS services.

Knowledge of:

• Tradeoffs between provisioned services and serverless services
• SQL queries (for example, SELECT statements with multiple qualifiers or JOIN clauses)
• How to visualize data for analysis
• When and how to apply cleansing techniques
• Data aggregation, rolling average, grouping, and pivoting

Skills in:

• Visualizing data by using AWS services and tools (for example, AWS Glue DataBrew, Amazon QuickSight)
• Verifying and cleaning data (for example, Lambda, Athena, QuickSight, Jupyter Notebooks, Amazon SageMaker Data Wrangler)
• Using Athena to query data or to create views
• Using Athena notebooks that use Apache Spark to explore data

Task Statement 3.3: Maintain and monitor data pipelines.

Knowledge of:

• How to log application data
• Best practices for performance tuning
• How to log access to AWS services
• Amazon Macie, AWS CloudTrail, and Amazon CloudWatch

Skills in:

• Extracting logs for audits
• Deploying logging and monitoring solutions to facilitate auditing and traceability
• Using notifications during monitoring to send alerts
• Troubleshooting performance issues
• Using CloudTrail to track API calls
• Troubleshooting and maintaining pipelines (for example, AWS Glue, Amazon EMR)
• Using Amazon CloudWatch Logs to log application data (with a focus on configuration and automation)
• Analyzing logs with AWS services (for example, Athena, Amazon EMR, Amazon OpenSearch Service, CloudWatch Logs Insights, big data application logs)

Task Statement 3.4: Ensure data quality.

Knowledge of:

• Data sampling techniques
• How to implement data skew mechanisms
• Data validation (data completeness, consistency, accuracy, and integrity)
• Data profiling

Skills in:

• Running data quality checks while processing the data (for example, checking for empty fields)
• Defining data quality rules (for example, AWS Glue DataBrew)
• Investigating data consistency (for example, AWS Glue DataBrew)

Domain 4: Data Security and Governance

Task Statement 4.1: Apply authentication mechanisms.

Knowledge of:

• VPC security networking concepts
• Differences between managed services and unmanaged services
• Authentication methods (password-based, certificate-based, and role-based)
• Differences between AWS managed policies and customer managed policies

Skills in:

• Updating VPC security groups
• Creating and updating IAM groups, roles, endpoints, and services
• Creating and rotating credentials for password management (for example, AWS Secrets Manager)
• Setting up IAM roles for access (for example, Lambda, Amazon API Gateway, AWS CLI, CloudFormation)
• Applying IAM policies to roles, endpoints, and services (for example, S3 Access Points, AWS PrivateLink)

Task Statement 4.2: Apply authorization mechanisms.

Knowledge of:

• Authorization methods (role-based, policy-based, tag-based, and attributebased)
• Principle of least privilege as it applies to AWS security
• Role-based access control and expected access patterns
• Methods to protect data from unauthorized access across services

Skills in:

• Creating custom IAM policies when a managed policy does not meet the needs
• Storing application and database credentials (for example, Secrets Manager, AWS Systems Manager Parameter Store)
• Providing database users, groups, and roles access and authority in a database (for example, for Amazon Redshift)
• Managing permissions through Lake Formation (for Amazon Redshift, Amazon EMR, Athena, and Amazon S3)

Task Statement 4.3: Ensure data encryption and masking.

Knowledge of:

• Data encryption options available in AWS analytics services (for example, Amazon Redshift, Amazon EMR, AWS Glue)
• Differences between client-side encryption and server-side encryption
• Protection of sensitive data
• Data anonymization, masking, and key salting

Skills in:

• Applying data masking and anonymization according to compliance laws or company policies
• Using encryption keys to encrypt or decrypt data (for example, AWS Key Management Service [AWS KMS])
• Configuring encryption across AWS account boundaries
• Enabling encryption in transit for data.

Task Statement 4.4: Prepare logs for audit.

Knowledge of:

• How to log application data
• How to log access to AWS services
• Centralized AWS logs

Skills in:

• Using CloudTrail to track API calls
• Using CloudWatch Logs to store application logs
• Using AWS CloudTrail Lake for centralized logging queries
• Analyzing logs by using AWS services (for example, Athena, CloudWatch Logs Insights, Amazon OpenSearch Service)
• Integrating various AWS services to perform logging (for example, Amazon EMR in cases of large volumes of log data)

Task Statement 4.5: Understand data privacy and governance.Knowledge of:

• How to protect personally identifiable information (PII)
• Data sovereignty

Skills in:

• Granting permissions for data sharing (for example, data sharing for Amazon Redshift)
• Implementing PII identification (for example, Macie with Lake Formation)
• Implementing data privacy strategies to prevent backups or replications of data to disallowed AWS Regions
• Managing configuration changes that have occurred in an account (for example, AWS Config)

AWS Data Engineer Associate Exam FAQs

Check here for FAQs!

AWS Exam Policy

Amazon Web Services (AWS) lays out specific rules and procedures for their certification exams. These guidelines cover various aspects of exam training and certification. Some of the key policies include:

Exam Retake Policy:

If a candidate doesn’t pass the exam, they must wait for 14 days before being eligible for a retake. There’s no limit on the number of attempts until the exam is passed, but the full registration fee is required for each attempt.

Exam Rescheduling:

To reschedule or cancel an exam, follow these steps:

1. Sign in to aws.training/Certification.
2. Click on the “Go to your Account” button.
3. Choose “Manage PSI” or “Pearson VUE Exams.”
4. You’ll be directed to the PSI or Pearson VUE dashboard.
5. If the exam is with PSI, click “View Details” for the scheduled exam. If it’s with Pearson VUE, select the exam in the “Upcoming Appointments” menu.
6. Keep in mind that you can reschedule the exam up to 24 hours before the scheduled time, and each appointment can only be rescheduled twice. If you need to take the exam a third time, you must cancel it and then schedule it for a suitable date.

AWS Data Engineer Associate Exam Study Guide

AWS Exam Page

AWS furnishes an exam page that includes the certification’s course outline, an overview, and crucial details. These information are crafted by AWS experts to showcase skills and guide candidates through hands-on exercises reflective of exam scenarios. Further, use the certification page validates proficiency in core data-related AWS services, the ability to implement data pipelines, troubleshoot issues, and optimize cost and performance following best practices. If you’re keen on leveraging AWS technology to transform data for analysis and actionable insights, taking this exam provides an early chance to earn the new certification.

AWS Learning Resources

AWS offers a diverse range of learning resources to cater to individuals at various stages of their cloud computing journey. From beginners seeking foundational knowledge to experienced professionals aiming to refine their skills, AWS provides comprehensive documentation, tutorials, and hands-on labs. The AWS Training and Certification platform offers structured courses led by expert instructors, covering a wide array of topics from cloud fundamentals to specialized domains like machine learning and security. Some of them for AWS Data Engineer Associate exams are:

• Engage in the best of re:Invent Analytics 2022 
• A Day in the Life of a Data Engineer 
• Building Batch Data Analytics Solutions on AWS 

Join Study Groups

Study groups offer a dynamic and collaborative approach to AWS exam preparation. By joining these groups, you gain access to a community of like-minded individuals who are also navigating the complexities of AWS certifications. Engaging in discussions, sharing experiences, and collectively tackling challenges can provide valuable insights and enhance your understanding of key concepts. Study groups create a supportive environment where members can clarify doubts, exchange tips, and stay motivated throughout their certification journey. This collaborative learning experience not only strengthens your grasp of AWS technologies but also fosters a sense of camaraderie among peers pursuing similar goals.

Use Practice Tests

Incorporating AWS practice tests into your preparation strategy is essential for achieving exam success. These practice tests simulate the actual exam environment, allowing you to assess your knowledge, identify areas for improvement, and familiarize yourself with the types of questions you may encounter. Regularly taking practice tests helps build confidence, refines your time-management skills, and ensures you are well-prepared for the specific challenges posed by AWS certification exams. The combination of study groups and practice tests creates a well-rounded and effective approach to mastering AWS technologies and earning your certification.