AWS Certified Data Engineer - Associate Practice Exam

The AWS Certified Data Engineer - Associate exam validates your expertise in designing, developing, implementing, and maintaining data pipelines and managing data storage and analytics solutions on the Amazon Web Services (AWS) platform. This globally recognized credential demonstrates your ability to:

Who Should Consider This Exam:

The AWS Certified Data Engineer - Associate Exam has been developed for candidates having 2–3 years of experience in data engineering.

• Data engineers and data analysts seeking to validate their skills and knowledge on AWS data services.
• Cloud architects and solutions architects looking to specialize in designing and implementing data solutions on AWS.
• Individuals seeking a career focused on designing and managing data pipelines on AWS.

Key Roles and Responsibilities:

• Design and develop data pipelines: Design and develop data pipelines using various AWS services like S3, Glue, Lambda, and Step Functions to automate data ingestion, transformation, and loading processes.
• Choose and configure data storage services: Select and configure appropriate data storage services based on specific data requirements, including S3, DynamoDB, and Redshift.
• Implement data transformations and analytics: Implement data transformations using services like Glue, Athena, and Spark, and build analytics solutions using services like Amazon QuickSight and Amazon Redshift Spectrum.
• Monitor and optimize data pipelines: Monitor data pipelines for performance and errors, identify and troubleshoot issues, and optimize processes for efficiency.
• Secure and manage data access: Implement security best practices to secure data access, configure user permissions, and comply with data privacy regulations.

Exam Details:

• Format: Multiple-choice questions and case studies
• Time Limit: 170 minutes
• Languages: English
• Passing Score: 720

Course Outline

The AWS Certified Data Engineer - Associate Practice Exam covers the following topics including - 

Module 1: Describe Data Ingestion and Transformation (34%)

1.1: Explain Perform data ingestion.

Candidate are required to have -

• Knowledge of  throughput and latency characteristics for AWS services for ingesting data
• Understanding data ingestion patterns (including , frequency and data history)
• Ability to stream data ingestion
• Skills to perform Batch data ingestion (including, scheduled ingestion, event-driven ingestion)
• Overview of Replayability of data ingestion pipelines
• Understanding of Stateful and stateless data transactions

Develop Skills

• To read data from streaming sources
• To read data from batch sources 
• To implement appropriate configuration options for batch ingestion
• To consume data APIs Setting up schedulers using Amazon EventBridge, Apache Airflow, or time-based schedules for jobs and crawlers
• To set up event triggers 
• To call up a Lambda function from Amazon Kinesis
• To create allowlists for IP addresses to allow connections to data sources
• To implement throttling and overcoming rate limits (for example, DynamoDB, Amazon RDS, Kinesis)
• To manage fan-in and fan-out for streaming data distribution

1.2: Explain Transform and process data.

Candidate are required to have -

• Knowledge of creating ETL pipelines based on business requirements 
• Understanding of volume, velocity, and variety of data (including, structured data, unstructured data)
• Knowledge of cloud computing and distributed computing
• Ability to use Apache Spark to process data
• Understanding of intermediate data staging locations

Develop Skills

• To optimize container usage for performance needs
• To connect to different data sources
• To integrate data from multiple sources
• To optimize costs while processing data
• To implement data transformation services based on requirements
• To transform data between formats
• To troubleshoot and debug common transformation failures and performance issues
• To create data APIs to make data available to other systems by using AWS services

1.3: Explain Orchestrate data pipelines

Candidate are required to have Knowledge of -

• Integrating various AWS services to create ETL pipelines
• Managing Event-driven architecture
• Configuring AWS services for data pipelines based on schedules or dependencies
• Managing Serverless workflows

Develop Skills

• To use orchestration services to build workflows for data ETL pipelines
• To develop data pipelines for performance, availability, scalability, resiliency, and fault tolerance
• To implement and maintaining serverless workflows
• To use notification services to send alerts 

1.4: Explain and apply programming concepts.

Candidates  are required to have knowledge to - 

• Perform continuous integration and continuous delivery (CI/CD)
• Manage SQL queries (related to data source queries and data transformations)
• Infrastructure as code (IaC) for repeatable deployments
• Managing Distributed computing\
• Handling Data structures and algorithms
• Optimizing SQL query 

Develop Skills

• To optimize code to reduce runtime for data ingestion and transformation
• To configure Lambda functions to meet concurrency and performance needs
• To perform SQL queries to transform data (for example, Amazon Redshift stored procedures)
• To structure SQL queries to meet data pipeline requirements
• To use Git commands to perform actions such as creating, updating, cloning, and branching repositories
• To use the AWS Serverless Application Model (AWS SAM) to package and deploy serverless data pipelines
• To use and mount storage volumes from within Lambda functions

Module 2: Describe Data Store Management (26%)

2.1: Explain Choose a data store.

Candidate should have -

• Knowledge of storage platforms and its features
• Knowledge of storage services and configuring specific performance demands
• Understanding of data storage formats (including, .csv, .txt, Parquet)
• Ability to align data storage with data migration requirements
• Skills to determining the appropriate storage solution for specific access patterns
• Skills to manage locks to prevent access to data 

Develop Skills 

• To implement the suitable storage services for specific cost and performance requirements
• To configure the appropriate storage services for specific access patterns and requirements
• To apply storage services to appropriate use cases
• integrate migration tools into data processing systems
• To implement data migration or remote access methods 

2.2: Explain Data Cataloging Systems

Candidates are required to have -

• Knowledge to create a data catalog
• Skills to classify data based on requirements
• Knowledge of components of metadata and data catalogs

Build Skills

• To use data catalogs to consume data from the data’s source
• To build and reference a data catalog
• To identify schemas and using AWS Glue crawlers to populate data catalogs
• To synchronize partitions with a data catalog
• To create new source or target connections for cataloging 

2.3: Explain and manage the lifecycle of data

Candidate should have Knowledge of -

• Suggesting suitable storage solutions to address hot and cold data requirements
• Optimizing the cost of storage based on the data lifecycle
• Deleting data to meet business and legal requirements
• Data retention policies and archiving strategies
• Protecting data with sutable resiliency and availability

Develop Skills 

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

2.4: Explain design data models and schema evolution.

Candidate should have knowledge of -

• Concepts of Data modeling
• Ensuring accuracy and trustworthiness of data by using data lineage
• Best practices and techniques for indexing, partitioning strategies, compression, and other data optimization techniques
• Modelling structured, semi-structured, and unstructured data
• Techniques of schema evolution 

Build Skills in

• To design schemas for Amazon Redshift, DynamoDB, and Lake Formation
• To address changes to the characteristics of data
• To perform schema conversion (for example, by using the AWS Schema
• To manage conversion Tool [AWS SCT] and AWS DMS Schema Conversion)
• To establish data lineage by using AWS tools 

Module 3: Describe Data Operations and Support (22%)

3.1: Explain and automate data processing by using AWS services.

Candidates should have knowledge of -

• Maintaining and troubleshooting data processing for repeatable business outcomes
• Using API calls for data processing
• Identifying services accept scripting 

Build Skills 

• To orchestrate data pipelines
• To troubleshoot Amazon managed workflows
• To calling SDKs to access Amazon features from code
• To use the features of AWS services to process data
• To consume and maintaining data APIs
• To prepare data transformation
• To use Lambda to automate data processing
• To manage events and schedulers 

3.2: Explain and Analyze data by using AWS services.

Candidates should have Knowledge of -

• Providing tradeoffs between provisioned services and serverless services
• Running and executing SQL queries
• Visualizing data for analysis
• Applying cleansing techniques
• Data aggregation, rolling average, grouping, and pivoting

Build Skills 

• To visualize data by using AWS services and tools
• To verify and clean data
• To use Athena to query data or to create views
• To use Athena notebooks that use Apache Spark to explore data

3.3: Explain the process of maintaining and monitoring data pipelines

Candidates should have knowledge of -

• Using log application data
• Performance tuning using Best practices
• Providing log access to AWS services
• Amazon Macie, AWS CloudTrail, and Amazon CloudWatch

Build Skills 

• To Extract logs for audits
• To deploy, log and monitor solutions for facilitating auditing and traceability
• To use notifications during monitoring to send alerts
• To troubleshoot performance issues
• To use CloudTrail to track API calls
• To troubleshoot and maintain pipelines
• To use Amazon CloudWatch Logs for logging into the application data (with a focus on configuration and automation)
• To analyze logs with AWS services

3.4: Explain and ensure data quality

Candidates should have knowledge of -

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

Build Skills 

• To run data quality checks while processing the data 
• To define data quality rules 
• To investigate data consistency 

Module 4: Describe Data Security and Governance (18%)

4.1: Explain to apply authentication mechanisms.

Candidates should have knowledge of -

• Concepts including VPC security networking concepts
• Differentiating managed services and unmanaged services
• Authenticating methods (password-based, certificate-based, and role-based)
• Differentiating AWS managed policies and customer managed policies

Build Skills 

• To update VPC security groups
• To create and update IAM groups, roles, endpoints, and services
• To create and rotate credentials for password management 
• To set up IAM roles for access 
• To apply IAM policies to roles, endpoints, and services 

4.2: Explain and apply authorization mechanisms

Candidates should have knowledge of -

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

Build Skills 

• To create custom IAM policies when a managed policy does not meet the requirements 
• To store application and database credentials 
• To provide database users, groups, and roles access and authority in a database 
• To manage permissions through Lake Formation 

4.3: Explain and ensure data encryption and masking.

Candidates should have knowledge of -

• Available Data encryption options in AWS analytics services
• Differentiating client-side encryption and server-side encryption
• Protecting sensitive data
• Data anonymization, masking, and key salting

Build Skills 

• To apply data masking and anonymization according to compliance laws or company policies
• To use encryption keys to encrypt or decrypt data
• To configure encryption across AWS account boundaries
• To enable encryption in transit for data.

4.4: Explain and prepare logs for audit

Candidates should have knowledge of -

• Logging  application data
• Logging  access to AWS services
• Managing Centralized AWS logs

Build Skills 

• To use CloudTrail to track API calls
• To use CloudWatch Logs to store application logs
• To use AWS CloudTrail Lake for centralized logging queries
• To analyze logs by using AWS services
• To integrate various AWS services to perform logging 

4.5: Explain data privacy and governance

Candidates should have Knowledge of -

• Protecting personally identifiable information (PII)
• Managing Data sovereignty

Build Skills 

• To grant permissions for data sharing
• To implement PII identification
• To implement data privacy strategies for preventing backups or replications of data to disallowed AWS Regions
• To manage configuration changes that occurred in an account