AWS Certified Data Engineer – Associate

Certilum focus

Certilum’s AWS Data Engineer prep uses scenario‑based practice questions that mirror real‑world challenges: S3 ingestion, multi‑service orchestrations, and fine‑grained access controls. Our practice exams emphasize cost optimization, performance tuning, and security compliance with diagnostic feedback on data partitioning and Glue job configurations.

Cognitive load

High – requires deep understanding of multiple AWS data services, their integration patterns, and operational best practices. Memory recall for service limits, pricing models, and specific configurations is less emphasized; instead, scenario-based problem-solving and architecture design dominate the exam. Candidates must synthesize knowledge across storage, compute, networking, and security domains within data engineering contexts.

Study strategy

1. Start with the official AWS Data Engineer exam guide and sample questions. 2. Complete hands-on labs on AWS Workshops and AWS Skill Builder – focus on AWS Glue, S3, Redshift, and Kinesis. 3. Build a real-world project: ingest streaming data with Kinesis, transform it with Glue (Spark), store results in S3 (Parquet), query with Athena, and orchestrate with Step Functions. 4. Master IAM policies for data lake access and Lake Formation. 5. Practice with the official AWS Practice Exam and third-party simulators (e.g., Tutorials Dojo). 6. Review re:Invent sessions on modern data architecture. Expected study time: 80–120 hours for experienced data engineers; 150+ hours for those newer to AWS.

Skills measured

• Data ingestion (batch, streaming, CDC)
• Orchestration and workflow automation
• Data storage design (lake, warehouse, No)
• ETL/ELT transformation (Spark, , Lambda)
• Data governance (IAM, Lake Formation, encryption)
• Monitoring, logging, and auditing (CloudWatch, CloudTrail)
• Cost management (S3 lifecycle, instance selection)

Mandatory preconditions

• 1–2 years of hands-on experience with AWS data services
• Basic understanding of Python or Scala programming
• Familiarity with and data modeling concepts
• Practical experience with ETL/ELT pipelines and data lake architectures