AWS Certified Data Engineer – Associate DAS-C01 PRACTICE EXAM PRACTICE TEST
What you will learn
Design and Implement Data Pipelines
Manage Data Lakes and Analytics Solutions
Optimize Data Storage and Retrieval
Apply Security Best Practices to Data Solutions
Why take this course?
“This practice exam consists of 6 sections, each containing 65 questions, covering all the topics included in the certification exam.”
The AWS Certified Data Engineer – Associate certification is designed to validate the skills required to work with data engineering solutions using Amazon Web Services (AWS). This certification ensures that individuals have the expertise in implementing and managing data pipelines, performing data transformation tasks, and ensuring data quality, while leveraging AWS data services. The course prepares candidates for the certification exam by focusing on key areas such as data ingestion, storage, transformation, orchestration, and security.
Course Title: AWS Certified Data Engineer – Associate
Course Overview:
This course provides comprehensive training for professionals aiming to achieve the AWS Certified Data Engineer – Associate certification. It covers the fundamental concepts and practical skills required to design, build, and maintain data solutions on AWS, focusing on the tools, services, and architecture needed to process and manage data at scale. The course is structured around the core AWS data services, ensuring learners gain hands-on experience and the knowledge necessary for the certification exam.
Key Learning Objectives:
- Introduction to Data Engineering on AWS
- Understand the role of a data engineer and the core AWS services used in data engineering, such as S3, Redshift, Kinesis, and Glue.
- Gain foundational knowledge of data engineering concepts, including data pipelines, data lakes, and batch vs. real-time processing.
- Building and Managing Data Pipelines
- Learn how to ingest, transform, and load data using AWS services like AWS Glue, Kinesis, and Data Pipeline.
- Implement automated workflows for ETL (Extract, Transform, Load) processes.
- Data Storage and Management
- Explore AWS storage options (S3, DynamoDB, Redshift) and how to choose the right storage solution based on specific use cases.
- Learn how to structure data in various formats like Parquet, Avro, and JSON for optimized querying and analysis.
- Data Security and Governance
- Understand the importance of data security in data engineering, including encryption, access control, and compliance with regulatory standards.
- Implement role-based access control (RBAC) and policies for sensitive data using AWS IAM and KMS.
- Data Quality Assurance
- Implement strategies for ensuring data quality, including validation checks, error handling, and auditing.
- Learn about data versioning, monitoring data pipelines, and troubleshooting data issues in a production environment.
- Optimizing Data Architecture for Performance and Cost
- Design cost-effective and high-performance data architectures that scale with business needs.
- Leverage AWS tools such as CloudWatch, Lambda, and Step Functions for operational efficiency.
Target Audience:
This course is ideal for data engineers, cloud professionals, and individuals who wish to specialize in data engineering on AWS. It’s also suitable for professionals with some experience in cloud computing or data-related roles who want to validate their skills and knowledge through the AWS certification exam.
Course Prerequisites:
- Basic knowledge of AWS services and cloud computing concepts.
- Familiarity with SQL and data modeling.
- Experience with data engineering tasks such as data migration, processing, or working with databases is helpful but not required.
Course Modules:
- Module 1: Data Engineering Foundations
- Overview of AWS data services
- Introduction to Data Lakes, Data Warehouses, and ETL pipelines
- Module 2: Ingesting Data with AWS
- Data ingestion using Kinesis, Glue, and S3
- Working with streaming and batch data processing
- Module 3: Data Transformation and ETL Workflows
- Creating and managing ETL workflows using AWS Glue and Lambda
- Data transformation best practices and tools
- Module 4: Data Storage and Query Optimization
- Data storage strategies with Redshift, S3, DynamoDB
- Query optimization techniques and performance tuning
- Module 5: Security and Compliance in Data Engineering
- Implementing data encryption and secure access control
- Managing compliance using AWS services
- Module 6: Data Pipeline Management and Monitoring
- Orchestrating data pipelines using Step Functions, Glue, and Lambda
- Monitoring and troubleshooting data workflows with CloudWatch and CloudTrail