Chapter 4 of 5 · Google Cloud Professional Data Engineer
Preparing and using data for analysis
BI performance, masking, BigQuery ML, RAG and data sharing.
- 3topics
- 15 minfast track
- 15%of the exam
Topics in this chapter
Take them in order, or jump to the one you need.
- 1Preparing Data for Visualization and BIConnect Looker and Looker Studio to BigQuery, precalculate fields, use BI Engine and materialized views, fix slow queries, secure…5 min
- 2Preparing Data for ML, Embeddings and RAGFeature engineering, training and serving with BigQuery ML, avoiding leakage and skew, and preparing unstructured data for…5 min
- 3Sharing Data, Datasets and ReportsDefine sharing rules, publish datasets and reports, and use BigQuery sharing (Analytics Hub) listings and data clean rooms to…5 min
About this chapter
What this chapter covers
Preparing and using data for analysis is about 15% of the Google Cloud Professional Data Engineer exam. It covers the last mile: making data fast and safe for dashboards, ready for machine learning and generative AI, and shareable with other teams and organizations.
It covers preparing data for visualization (connecting tools, precalculated fields, BI Engine and materialized views, slow query troubleshooting, masking, IAM and Cloud DLP), preparing data for AI and ML (feature engineering, training and serving with BigQuery ML, embeddings and retrieval-augmented generation) and sharing data (sharing rules, publishing datasets and reports, and BigQuery sharing, also known as Analytics Hub).
The exam has 40 to 50 multiple choice and multiple select questions in 2 hours. Questions here often describe a slow dashboard, a privacy rule or a sharing request, then ask "What should you do?"; the best answer usually reuses a built-in BigQuery feature instead of copying data.
How the topics fit together
All three topics serve consumers from the same governed tables. Speed comes from precalculation and caching, safety from masking and access rules, and reach from sharing without copies.
When a stem says "without copying the data", think of authorized views, linked datasets from BigQuery sharing, policy tag masking and materialized views. Copies are almost never the intended answer.
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.