Assembling the services a simple transformation pipeline needs
For basic transformation pipelines on Google Cloud, the Associate Data Practitioner exam expects you to match the transformation need (batch vs streaming, code vs no-code, SQL vs programmatic) to the right service. Common building blocks include Cloud Data Fusion, Dataflow, Dataproc, and BigQuery itself, often orchestrated with Cloud Composer or Workflows.
Must-know
- Cloud Data Fusion is the go-to choice for a visual, no-code/low-code drag-and-drop ETL/ELT pipeline builder using pre-built connectors and transformations.
- Dataflow is the preferred fully managed service for both batch and streaming data transformations using Apache Beam, ideal when you need custom code-based, scalable pipelines with autoscaling.
- Dataproc is best when you have existing Hadoop/Spark workloads or need Spark-based transformations and want more control over cluster management than Dataflow provides.
- BigQuery supports in-place ELT transformations via SQL (including scheduled queries and BigQuery Data Transfer Service), which is the simplest option when data is already loaded and transformations can be expressed in SQL.
- Cloud Composer (managed Apache Airflow) is used to orchestrate multi-step pipelines across multiple GCP services, not to perform the transformation itself.
- Cloud Workflows is a lighter-weight orchestration option for simpler, serverless service-to-service orchestration compared to Composer's full Airflow-based DAG orchestration.
A data practitioner at a retail company needs to build a pipeline that reads CSV files landing in Cloud Storage, renames a few columns, filters out incomplete rows, and writes the results to BigQuery. The practitioner wants to design the transformation logic visually, without writing custom pipeline code, and prefers a managed service with prebuilt connectors. Which product should the practitioner use to implement this pipeline?
What you have tried across GCP ADP's objectives, not a readiness score.
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 13, 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.