Course Program Overview
AWS Certified Data Engineer – Associate (DEA-C01) Course
- Duration: 4 Months
- Format: Live Online / Classroom / Blended / Corporate
- Sessions: 5 per week
- Session Length: 01 Hour of each
- Tech Stack: AWS Management Console, Amazon Athena, AWS Glue, Amazon Redshift, Amazon Kinesis, AWS Lake Formation, Amazon EMR, AWS Step Functions, AWS IAM.
- Outcome: Industry-grade portfolio + AWS Certification + Career acceleration
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📌 Key Exam Information
Certification Name
AWS Certified Data Engineer – Associate (DEA-C01)
Level
Intermediate / Associate-level
Role Alignment
Data Engineer, Analytics Engineer, Big Data Infrastructure Associate, Cloud Data Architect
Duration
130 minutes
Exam Format
Multiple choice and multiple select technical scenario questions
Number of Questions
65 (85 scored/unscored combined distribution matrix)
Passing Score
Scaled score of 720 / 1000
Languages
English, Japanese, Korean, Simplified Chinese
🎯 Who Should Take DEA-C01
- Data analysts and database administrators aiming to scale their on-premise pipeline workflows into high-throughput AWS environments.
- Cloud developers looking to master production-grade ETL design, complex data pipelines, and distributed data lakes.
- Technical professionals seeking to validate domain mastery over structured, semi-structured, and streaming enterprise storage pools.
🧠 Exam Domains & Weightage
24%
Data Ingestion and Transformation
Setting up batch/streaming collection components, building schema mapping structures, and cleaning operational inputs.
26%
Data Store Management
Configuring optimal cloud storage engines, indexing keys, table partitioning models, and managing data lifecycles.
24%
Data Operations and Support
Orchestrating end-to-end processing loops, managing system workflows, and optimizing pipeline compute clusters.
26%
Data Security and Governance
Applying encryption keys, checking system audit configurations, masking columns, and establishing access perimeters.
🏆 Benefits of Certification
- Global Recognition: Holding an official AWS associate engineering milestone dramatically increases visibility with enterprise recruiters.
- Career Pathways: Establishes a highly foundational operational baseline for advanced cloud specializations like MLOps or enterprise architecture.
- Practical Skills: Validates day-to-day coding and architectural competence across AWS Glue, Amazon Redshift, and Lake Formation setups.
- Downstream Value: Directly certifies your ability to pipe, format, cleanse, and structure large data assets ready for high-scale AI systems.
🌟 Top 5 Reasons to Choose Adian Solutions for this course
Comprehensive Exam Alignment
Our 3-month roadmap is mapped directly to the official AWS DEA-C01 exam blueprint. Every domain, every infrastructure optimization metric, and every storage design pattern is covered in detail, ensuring you’re exam-ready with no gaps.
Certification Guarantee & Retake Support
We’re committed to your success. If you don’t pass the DEA-C01 exam on your first attempt, we offer extended access to our learning materials and personalized retake support at no extra cost. Our team will guide you through exam registration, help you apply for AWS vouchers (if available), and ensure you’re fully confident before your next attempt.
Hands-On, Real-World Learning
We go beyond theory with practical labs in Amazon Redshift warehouses, AWS Glue jobs, and Athena query environments. You’ll optimize streaming pipelines, configure granular security parameters, and arrange automated big-data processing structures — skills you can showcase immediately in your portfolio.
Capstone Project & Portfolio Development
Learners complete a comprehensive capstone project that integrates real-time event ingestion, automated serverless ETL transforms, and partitioned data lake optimization. This project becomes part of your professional portfolio, giving you a tangible edge in interviews and job applications.
Career & Placement Support
We provide resume workshops, LinkedIn profile optimization, mock interviews, and direct placement support. Top performers may be referred to hiring partners or offered internships on live corporate projects, accelerating your career journey.
Skills You Will Get
You will gain a strong foundation in artificial intelligence by mastering machine learning concepts, computer vision, natural language processing, and conversational AI. Along the way, you’ll learn to work hands-on with AWS Glue, Amazon Redshift, and Amazon Athena to build deployable solutions. These skills will empower you to design, integrate, and implement real-world AI applications that align directly with industry needs and certification standards.
6+ AWS AI Tools
6+ AWS AI Tools
20+ Guided Labs
20+ Guided Labs
4 Capstone Project
4 Capstone Project
10+ Case Studies
10+ Case Studies
Amazon Redshift Integration
Amazon Redshift Integration
NLP
NLP
Conversational AI
Conversational AI
Computer Vision
Computer Vision
Course Program
(4 Months)
Classroom / Live Online
100+ Hours
This four month program is designed to take learners from foundational data principles to advanced production data engineering architectures using AWS. By extending the roadmap, each domain is explored in greater depth, with additional labs and case studies to ensure mastery. The journey begins with enterprise storage and data lake foundations, progresses into streaming and ETL pipelines, then orchestration and governance, and finally culminates in a comprehensive Capstone Project with exam preparation.
● Month 1
Exam Coverage: 25–30%
Foundations of Core Machine Learning and Predictive Cloud AI
Learners begin with a deep introduction to industrial data engineering frameworks, tracking the flow of datasets across multi region corporate networks and evaluating cloud database choices against latency, scalability, and consistency metrics. We cover structured, semi structured, and unstructured storage locations, comparing Amazon S3 object layers, relational warehouses, and non relational document hubs. The month emphasizes foundational data lake engineering, explaining schema catalogs via AWS Glue Crawlers, organizing raw data stages, and defining central access patterns. Students also explore analytical repositories in Amazon Redshift, focusing on partitioning, sort/distribution keys, and query optimization.
Practical Labs:
Build tiered storage in Amazon S3, configure AWS Glue Crawlers for schema detection, deploy Redshift warehouses, and benchmark partitioned queries under high volume loads.
Case Studies:
Financial entities aggregating daily logs into S3 data lakes; retail chains modernizing records into Redshift; audit schemas ensuring operational transparency.
● Month 2
Exam Coverage: 25–30%
Streaming Fabrics & Serverless ETL
This month focuses on high throughput data processing systems and scalable compute engines. Learners explore real time asynchronous streaming channels using Amazon Kinesis, configuring streams, firehose delivery pipelines, and analytics integrations. We then transition into serverless ETL scripting, teaching scalable Spark and Python jobs inside AWS Glue Studio. Students also learn to migrate legacy Spark/Hive workloads onto managed Amazon EMR clusters, with emphasis on distributed optimization strategies.
Practical Labs:
Design real time metric capture paths with Kinesis, program ETL scripts in AWS Glue Studio, configure autoscaling EMR clusters, and query raw S3 datasets with Athena.
Case Studies:
Streaming services capturing global player data with Kinesis; medical logistics platforms managing automated updates; telecom networks processing millions of logs via EMR.
● Month 3
Exam Coverage: 25–30%
Workflow Orchestration & Governance
This month introduces production level pipeline orchestration and governance. Learners study DAG pipelines using AWS Step Functions and Glue workflows to automate multi stage data pipelines. Security frameworks like AWS Lake Formation are integrated to enforce granular row, column, and cell level permissions. Compliance controls such as Customer Managed Keys (CMK) via AWS KMS, structural data masking, and CloudTrail audit logging are explored. Students learn to balance governance with performance and cost optimization.
Practical Labs:
Build automated multi step pipelines with Step Functions, apply column level security in Lake Formation, configure CloudTrail logs, and debug pipeline bottlenecks.
Case Studies:
Financial groups deploying fine grained access masks; retail chains orchestrating nightly updates via Step Functions; consulting firms designing resilient enterprise pipelines.
● Month 3
Exam Coverage: 25–30% + Consolidation of 100%
Capstone Project & Exam Preparation
The final month consolidates all skills into a professional portfolio. Learners integrate ingestion, transformation, storage, orchestration, and governance into a unified AWS data solution. Emphasis is placed on operational monitoring, pipeline optimization, and exam readiness. Students complete full length mock exams, case study workshops, and targeted labs to ensure mastery of all DEA C01 domains.
Practical Labs:
Build an end to end pipeline with Kinesis, Glue, EMR, and Redshift; configure monitoring dashboards; enforce governance with Lake Formation; and run exam simulations.
Case Studies:
Logistics firms implementing real time ingestion pipelines; healthcare providers enforcing compliance with Lake Formation; smart energy providers combining streaming telemetry with analytical warehouses.
Capstone Project:
Architect and deploy a production grade AWS data engineering solution integrating streaming ingestion, serverless ETL, distributed compute, and secure governance.
Real Roles. Real Results.
Explore Your Post-Course Career
After completing the course, learners can unlock high-impact roles such as Machine Learning Engineer, Cloud Solutions Architect, Data Scientist, and AI Product Lead—across sectors like technology, BFSI, healthcare, retail, and manufacturing.
Machine Learning Engineer
Designs, trains, and optimizes ML models for prediction, classification, and automation across industries like finance, healthcare, and manufacturing.
Data Scientist
Extracts insights from structured and unstructured data using statistical analysis, visualization, and modeling techniques. Drives decision-making and business intelligence.
AI Solutions Architect
Leads the design and deployment of scalable AI systems using cloud platforms, MLOps pipelines, and enterprise-grade frameworks.
Deep Learning Researcher
Specializes in neural networks, CNNs, RNNs, GANs, and transformers. Builds models for image recognition, NLP, and generative tasks.
MLOps Engineer
Implements CI/CD pipelines, containerization, and cloud-native deployment for AI models. Ensures reliability, scalability, and performance monitoring.
Generative AI Developer
Builds intelligent applications using LangChain, Hugging Face, and LLMs. Applies RAG pipelines and transformer models to domains like legal tech and marketing.
Computer Vision Engineer
Develops image and video analysis systems using OpenCV, TensorFlow, and deep learning. Works on facial recognition, object detection, and OCR.
NLP Engineer
Creates language models and conversational AI systems using NLTK, SpaCy, and transformers. Powers chatbots, sentiment analysis, and document summarization.
AI Product Lead
Bridges technical teams and business strategy. Oversees AI product lifecycle—from ideation to deployment—ensuring alignment with market needs and ethical standards.
Frequently Asked Questions
AWS Certified Data Engineer – Associate (DEA-C01)
This intermediate-level data engineering program assumes a basic understanding of relational database design, standard SQL query logic, and elementary data patterns. If you are entirely new to cloud-scale architectures, our Month 1 curriculum delivers tailored transition modules to help you bridge any technical knowledge gaps smoothly.
This is a comprehensive 3-month data engineering training track providing 80+ structural training hours. The path includes 5 interactive live programming lectures per week (1 hour each), deeply backed by hands-on sandbox labs, performance case studies, and structured practice exam tasks to ensure complete professional readiness.
The official exam is composed of 65 multiple-choice and multiple-select questions to be completed within a 130-minute testing window. The questions use complex scenario-based profiles where you act as a data infrastructure engineer who must choose the most appropriate, scalable, or secure AWS tool to solve a database or pipeline issue.
Earning this associate certification prepares you for high-impact technical roles such as Cloud Data Engineer, Big Data Architect, Analytics Pipeline Developer, Data Operations Specialist, or Business Intelligence Infrastructure Engineer across leading banking, e-commerce, and cloud consulting industries.
AWS professional and associate-tier engineering credentials are highly valued by technical managers worldwide. Because the DEA-C01 exam focuses on practical challenges—such as scaling distributed EMR clusters, managing real-time streams, and setting up granular governance boundaries—holding this credential proves true operational competence to recruiters.
In accordance with official AWS certification guidelines, your Data Engineer Associate credential remains fully valid for a period of three years from your test-passing date. To keep your certification active, you must retake the current exam version before your three-year eligibility period ends to prove your alignment with updated service releases.
You will develop direct, practical operational familiarity with the live AWS ecosystem. Over our 20+ guided labs, you will build automated schema catalogs using AWS Glue, optimize relational storage boundaries within Amazon Redshift warehouses, execute serverless data queries inside Amazon Athena, process live streams with Amazon Kinesis, and arrange automated workflows using AWS Step Functions.
Adian Solutions provides an extensive suite of career preparation resources. This includes multiple full-length scenario-based mock exams matching real testing structures, interactive pipeline architecture critiques, hands-on portfolio assembly assistance, specialized technical resume restructuring, LinkedIn profile optimization workshops, and direct matching support via our network of technical recruiters.
Yes, candidates have the flexibility to schedule and sit for the official examination via an online proctored testing framework from home or an isolated corporate office space, provided their local setup matches strict technical rules (including an active webcam stream, audio checking, and an empty workspace layout). Alternatively, you can take it at any physical Pearson VUE testing location.
Your technical career development remains our highest priority. If you do not clear the certification on your initial try, Adian Solutions grants extended system access and dedicated mentor remediation coaching sessions at completely zero extra cost. Our training leaders will analyze your score diagnostics, address underlying conceptual gaps, run booster labs, and ensure you return to pass with complete certainty.
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Partnering with Adian Soft Solutions transformed our AI adoption journey. Their training programs gave our team the confidence to deploy advanced ML models in production. The hands-on labs and real-world case studies were game changers.
Priya Sharma, CTO Global Tech Innovators