Picking how to pull data out of a source system
For the Data Preparation and Ingestion domain, you must know which Google Cloud tool fits a given extraction/ingestion scenario based on source type, transformation needs, and workload pattern (batch vs. streaming vs. replication). The exam tests recognition of the right tool for a described use case rather than deep implementation details.
Must-know
- Use Dataflow for custom, code-based batch or streaming pipelines that need complex transformations, windowing, or unified batch/stream processing built on Apache Beam.
- Use BigQuery Data Transfer Service for scheduled, managed ingestion of data from Google SaaS apps (e.g., Google Ads, YouTube), other cloud providers (e.g., Amazon S3, Teradata), or Google Cloud Storage directly into BigQuery tables with minimal code.
- Use Database Migration Service (DMS) for lift-and-shift migration or continuous replication of relational databases (MySQL, PostgreSQL, SQL Server, Oracle) into Cloud SQL, AlloyDB, or as a source feed toward BigQuery, not for general-purpose ETL.
- Use Cloud Data Fusion for a fully managed, graphical (drag-and-drop) ETL/ELT tool when the team prefers low-code pipeline design with a wide library of prebuilt connectors and transformations over hundreds of sources.
- A key gotcha: BigQuery Data Transfer Service is for scheduled bulk/incremental loads from specific supported sources, not for arbitrary custom transformation logic. Choose Dataflow or Cloud Data Fusion when transformation complexity is high.
- Another gotcha: DMS is about moving/replicating database data (often for migration or hybrid setups), while Dataflow, Cloud Data Fusion, and BigQuery Data Transfer Service are about ingesting/transforming data specifically for analytics pipelines feeding BigQuery.
Check this objectiveFree · always available
A marketing analyst wants BigQuery to automatically ingest daily campaign performance data from Google Ads on a recurring schedule, without writing or maintaining any custom extraction code. Which service should the data practitioner configure?
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Data Preparation and Ingestion
- When to load first and when to transform first, and what sits between the two
- Picking a way to move existing data into Google Cloud
- Judging whether a dataset is trustworthy enough to build on
- Fixing messy records before they reach a report
- Telling CSV, JSON, Parquet, Avro, and relational tables apart, and where each fits
- Picking how to pull data out of a source system
- Matching a workload to the right storage or database service
- Getting files and tables loaded with a CLI, a transfer service, or a client library
Data Analysis and Presentation
- Writing BigQuery SQL that answers a reporting question
- Exploring and charting data inside a hosted notebook
- Turning a question from the business into an analysis that settles it
- Building a dashboard and getting it in front of the right people
- Deciding whether a job calls for Looker or for Looker Studio
- Editing LookML to change what a model exposes
- Spotting a problem worth solving with BigQuery ML or AutoML
- Calling a hosted Google language model straight from BigQuery
- Sequencing a machine learning project from raw data to served predictions
- Building, fitting, and scoring a model with SQL alone
- Running predictions against a model you already trained
- Keeping trained models catalogued in one place
Data Pipeline Orchestration
- Matching a transformation job to Dataproc, Dataflow, Dataform, or a managed alternative
- Weighing whether the transform belongs before or after the load
- Assembling the services a simple transformation pipeline needs
- Putting a query on a schedule and keeping it running
- Watching a Dataflow job and spotting where it stalls
- Reading logs and metrics to work out what a pipeline actually did
- Choosing what should drive a multi-step workflow
- Streaming messages into BigQuery as they arrive rather than in batches
- Wiring a trigger so one event starts the next step
Data Management
- Granting only the access a person or service actually needs
- Controlling who can read a bucket, and what uniform access changes
- Sharing a dataset with another team or company without copying it
- Matching a storage class to how often the data gets read
- Expiring old data automatically so it stops costing money
- Picking somewhere to park data that must be kept but is rarely read
- Comparing the managed backup and restore options across services
- Working out when a second copy is worth what it costs
- Regions, dual-regions, multi-regions, and zones as redundancy choices
- Deciding who should hold the encryption keys
- What a key management service does for creating, rotating, and revoking keys
- Protecting data on the wire versus data sitting on a disk
Coverage checked against the published exam guide on Aug 12, 2026.
These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.