Lake Formation, IAM, KMS, Macie, lineage and access patterns
What this course is
Where AWS data stops being a stack of services.
DEA-C01 is AWS's data-engineer associate certification. It validates that you can design, build, operate and govern data pipelines on AWS at production scale — not just list services. It's the most-hired-for cloud-data cert in Malaysia today.
At Nexperts, DEA-C01 is delivered against a real AWS data sandbox with seeded ingestion streams. By day 4 you've shipped a Glue + Iceberg lakehouse, a Step Functions orchestration, a Redshift Serverless warehouse and a governed Lake Formation domain.
Cloud data engineering is not 'put data on S3 and write SQL'. DEA-C01 tests whether you can design pipelines that survive scale, audit and incident.
The DEA-C01 (released March 2024, refreshed for 2026) leans heavily into lakehouse patterns, Glue 4.0 / 5.0, Lake Formation governance and observability. We cover all four with hands-on builds.
Who should take this course
📊
Data engineers
Already work with data and want the formal AWS associate credential.
📡
Cloud engineers
Pivoting into the data domain. DEA gives the AWS-native data lens.
🌟
DVA / SAA holders
Natural progression. Add data depth to your associate-level base.
📚
Analytics engineers
From a tools-first background. DEA gives the platform engineering grounding.
💼
Data architects
Designing data platforms on AWS. DEA sharpens the AWS-native depth.
👨💻
Backend developers
Working with data-heavy systems. DEA opens cloud-data engineering as a career path.
Prerequisites
✓ 1–2 years of professional data or backend-engineering experience
✓ Comfortable with SQL at intermediate level
✓ Familiarity with Python OR Scala / Spark
✓ Basic AWS familiarity (CCP / SAA / DVA helpful but not required)
→ Don't have an associate AWS cert? Ask about our CCP → DEA bundle.
Course Curriculum
Four domains. One data toolkit.
DEA-C01 is structured into Data Ingestion & Transformation, Data Store Management, Data Operations & Support, and Data Security & Governance. We deliver in pipeline-build order — you ship a real pipeline by day 1.
Hands-On Data Sandbox
9 pipelines. Real AWS data.
Every learner gets an AWS data sandbox with seeded ingestion (Kinesis stream + S3 batch). By day 4 you've shipped four production-shape data products.
01
Bronze-Silver-Gold Lakehouse
Build a 3-layer lakehouse on Glue + Iceberg over a seeded order stream.
Lakehouse
02
Streaming to Lakehouse
Ingest a Kinesis stream into Iceberg with deduplication and late-arrival handling.
Streaming
03
Step Functions Orchestration
Build a 6-step pipeline with retry, parallel branches and approval gate.
Orchestration
04
Redshift Zero-ETL
Wire zero-ETL from Aurora MySQL to Redshift Serverless. Validate freshness.
Warehouse
05
Cost Optimisation Sprint
Take a costly pipeline. Cut spend 40% without losing freshness or correctness.
Cost
06
Lake Formation Governance
Apply LF-Tags across 3 domains. Validate denial and grant flows.
Governance
07
DMS Migration
Use DMS to migrate a SQL Server schema to Aurora PostgreSQL with CDC.
Migration
08
Schema Evolution Drill
Handle schema evolution on Iceberg without breaking downstream consumers.
Schema
09
Pipeline Observability
Build a release-decision dashboard for a data product across freshness and quality.
Observability
+ 13 micro-tasks across PySpark, Athena SQL, Glue Studio and AWS CLI.
Exam Information
One scenario-heavy exam. Heavy on trade-offs.
DEA-C01 is 65 questions over 130 minutes. The exam is heavy on trade-off scenarios — 'Glue or EMR?', 'Iceberg or Hudi?', 'Redshift or Athena?' — the kind of decisions a real data engineer makes.
AWS DEA-C01 Exam
Questions65 (scenario + MCQ)
Duration130 minutes
Passing score720 / 1000
FormatPearson VUE / PSI / Online proctored
Validity3 years (recertification)
Industry avg pass rate~64% first attempt
Nexperts pass rate92% first attempt
Trade-Off Decision Drill
Drill length4-hour structured drill
FormatWhiteboard — you decide, peers challenge
Items practised20 trade-off scenarios
Common gotchasChoosing 'best' over 'most-pragmatic'
StrategyIdentify the constraint before the technology
OutcomeTrade-off score uplift averages +20%
WalkthroughPast scenario archive provided
Our 3-Mock Programme
01
Diagnostic Mock
End of day 1. Sets the baseline. Average score: 58%.
02
Trade-Off-Heavy Mock
Mid-course. 60% trade-off scenarios. Average score: 72%.
03
Final Clearance
Full timed simulation. 80%+ before we book. Average score: 84%.
0%
Pass Rate
92% of our DEA candidates pass on first attempt.
The AWS global first-attempt rate for DEA-C01 sits around 64%. We hit 92% by drilling trade-off scenarios on a real data sandbox, and gating booking on a clearance mock.
Real data sandboxTrade-off drill92% first attemptFree retake voucherLakehouse playbook
Why our pass rate is 92%
Industry average: ~64%
Most candidates revise services individually but never compare them under constraint. The exam asks them 'which fits this constraint best' and they pick by feature recall, not constraint match.
Nexperts: 92%
We run trade-off drills. We build pipelines on the sandbox. We gate booking on a clearance mock. Trade-off thinking becomes muscle memory.
Your AWS Data Path
DEA opens ML Specialty and Data-Pro paths.
DEA stacks naturally with MLS (Machine Learning Specialty) for ML-platform depth, or with DOP / SAP for delivery and architecture breadth. Strong second cert: Databricks Data Engineer Associate.
Before this
CCP / SAA / DVA
Helpful but not required. Many DEA candidates come from a data-engineering background.
Expected salary range after DEA: RM 9,500 – RM 16,500/month for cloud data engineer roles in MY MNCs and banks.
Student Reviews
What our DEA engineers say.
4.8
★★★★★
134 reviews
5★
84%
4★
13%
3★
3%
★★★★★
"Bronze-Silver-Gold lakehouse lab is the only data-engineering training I've taken that maps directly to what we ship at work. Cleared DEA two weeks after."
SR
Sridhar Rajendran
Senior Data Engineer · Maybank Data Platform
✓ Passed first attempt · 836/1000
★★★★★
"Cost-optimisation sprint cut my company's pipeline spend by RM 22,000/month. The course paid for itself before I'd even sat the exam."
NW
Nigel Wong
Data Engineer · Carsome
✓ Passed first attempt
★★★★
"Lake Formation governance lab is what most courses skip. Nexperts puts it front and centre. We're now using LF-Tags as our governance baseline at Astro."
TY
Theresa Yap
Data Platform Engineer · Astro Digital
✓ Passed first attempt · 798/1000
★★★★★
"Mock-3 simulation was harder than the real exam. Trade-off drill made the difference — I went into the exam knowing how to read those scenarios."
BD
Balachandran Devan
Senior Cloud Engineer · PETRONAS Digital
✓ Passed first attempt · 858/1000
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