Google Cloud Professional Data Engineer
Study notes for every exam objective
One note per objective in the published exam guide: what it covers, and the facts the exam actually tests. 67 objectives are written up. Coverage last checked against the guide on 2026-08-11.
Designing data processing systems
22% of the exam · 17 notes- Scoping IAM roles and org policies so access matches need
- Choosing an encryption and key management approach for data at rest and in transit
- Handling personally identifiable information without over-exposing it
- Keeping data storage and access inside a required region or jurisdiction
- Designing a pipeline that meets legal and regulatory constraints
- Laying out projects, datasets, and tables so governance holds up
- Separating development and production environments cleanly
- Picking a tool to prepare and clean data before it lands
- Watching and coordinating a pipeline's moving parts
- Planning for recovery when a pipeline or region fails
- Weighing ACID guarantees and availability against each other
- Deciding how and where incoming data gets validated
- Translating current and future business needs into an architecture
- Designing so data and workloads can move across clouds or regions
- Staging, cataloging, and profiling data so it can be found and governed
- Reading current stakeholder needs and mapping a path to the target state
- Planning and validating a migration of data and workloads into Google Cloud
Ingesting and processing the data
25% of the exam · 11 notes- Identifying where data comes from and where it needs to land
- Defining what a pipeline transforms and in what order
- Networking basics that shape how ingestion actually reaches its source
- Encrypting data as it moves into the platform
- Cleaning up bad or malformed records during ingestion
- Picking the right ingestion or processing service for a given source
- Shaping data during the ingestion pipeline itself
- Pulling in and importing data from an external source
- Onboarding a data source the pipeline has not seen before
- Automating and orchestrating ingestion jobs with Composer or Workflows
- Building a CI/CD path for pipeline code changes
Storing the data
20% of the exam · 13 notes- Reading how data actually gets accessed before picking storage
- Matching a managed storage service to the workload it needs to serve
- Balancing storage cost against the performance a workload needs
- Aging, tiering, and expiring data over its lifecycle
- Shaping a data model for how it will actually be queried
- Deciding how normalized or denormalized a schema should be
- Turning business requirements into a concrete storage design
- Architecting storage around the access patterns it must support
- Configuring discovery, access, and cost controls across a data lake
- Processing data once it is sitting in storage
- Monitoring a data lake's health and usage
- Assembling a data platform out of Google Cloud's storage and catalog tools
- Governing data consistently across a federated, distributed system
Preparing and using data for analysis
15% of the exam · 11 notes- Connecting downstream tools to the data they need to read
- Precomputing fields so dashboards do not recompute them every time
- Using BigQuery's BI-facing features like BI Engine and materialized views
- Diagnosing why a query is slow and fixing it
- Masking sensitive fields and controlling who can see them
- Preparing data for feature engineering and model training and serving
- Turning unstructured data into embeddings a model can retrieve from
- Setting the rules under which data gets shared with others
- Publishing a dataset for other teams or consumers to use
- Getting a dashboard or report in front of the people who need it
- Sharing BigQuery data through Analytics Hub
Maintaining and automating data workloads
18% of the exam · 15 notes- Cutting cost without cutting what the business actually needs
- Making sure business-critical processes never starve for resources
- Choosing between a persistent cluster and a job-based one
- Building a DAG for Cloud Composer
- Scheduling jobs so they run repeatably
- Managing BigQuery capacity through editions and reservations
- Running a query job interactively versus as a batch
- Watching pipeline health through Cloud Monitoring, Logging, and the BigQuery admin panel
- Monitoring usage against what was planned
- Making sense of a cryptic error, an odd bill, or a job stuck on a quota
- Managing jobs, queries, and compute reservations as one workload
- Designing a system that tolerates failure and restarts cleanly
- Spreading a workload's runs over more than one region or zone
- Preparing for corrupted or missing data before it happens
- Setting up replication and failover for services like Cloud SQL and Redis