Chapter 1 of 5 · Google Cloud Professional Data Engineer
Designing data processing systems
Security, compliance, reliability, portability and migrations.
- 5topics
- 25 minfast track
- 2diagrams and exhibits
- 22%of the exam
Topics in this chapter
Take them in order, or jump to the one you need.
- 1IAM, Encryption Keys and PII ProtectionData engineer security design on Google Cloud: IAM roles and organization policies, CMEK with Cloud KMS, AEAD crypto shredding…5 min
- 2Data Sovereignty, Compliance and Environment DesignData residency, regulations, audit logs, VPC Service Controls, project and dataset layout for governance, and separate…Diagram5 min
- 3Reliability, Data Quality and ACID ChoicesData preparation with Dataform, Dataflow and Data Fusion, LLM query generation, pipeline monitoring, data validation, disaster…5 min
- 4Flexible, Portable Designs and Data DiscoveryMap business needs to architecture, design for multi-cloud and residency with BigLake and Omni, and stage, catalog, profile and…5 min
- 5Planning and Validating Data MigrationsPlan a Google Cloud migration: assess needs, choose BigQuery Data Transfer Service, Database Migration Service, Transfer…Diagram5 min
About this chapter
What this chapter covers
Designing data processing systems is about 22% of the Google Cloud Professional Data Engineer exam. It covers the design decisions you make before any pipeline runs: who can see which data, where it may live, how the system survives failures and bad data, how it adapts to new requirements, and how data moves from existing platforms to Google Cloud.
It covers security and compliance (IAM, organization policies, encryption keys, PII, data sovereignty, regulations, project and dataset layout, development and production environments), reliability and fidelity (data preparation, validation, monitoring, disaster recovery and ACID trade-offs), flexibility and portability (multi-cloud, open formats, cataloging and discovery) and data migrations (BigQuery Data Transfer Service, Database Migration Service, Transfer Appliance, networking and Datastream).
The exam has 40 to 50 multiple choice and multiple select questions in 2 hours. Most questions here are business scenarios that end "What should you do?", and the right answer is the design that meets every stated constraint with the least effort.
How the topics fit together
Every later chapter builds on these choices. A dataset location, a key choice or a project layout made here is often hard to change afterwards, which is why the exam tests it so heavily.
Read the constraints in the stem first: "must stay in the EU", "least operational overhead", "no code changes", "minimal downtime". Each phrase usually rules out all but one option.
How to use these pages
Each topic page has the same shape, so you can skim for what you need.
- What it is: the idea in plain words.
- How it works: the mechanism, with tables and code examples.
- What to remember for the exam: the facts to memorise and how questions are phrased.
- Common mistakes: the classic traps.
- Check yourself: three questions to test your understanding.
Ready for more practice?
The full Google Cloud Professional Data Engineer question bank has 733 questions, with timed practice exams and an explanation for every answer.
This study guide is independent and is not affiliated with, endorsed by, or sponsored by Google Cloud. Vendor names, certification names and exam codes are trademarks of their respective owners and are used for identification only.