Matching a transformation job to Dataproc, Dataflow, Dataform, or a managed alternative
The Associate Data Practitioner exam expects you to match a data transformation need to the right Google Cloud tool based on workload type, coding preference, and pipeline complexity. Dataflow, Dataproc, Cloud Data Fusion, Dataform, and Cloud Composer each serve distinct transformation or orchestration roles rather than being interchangeable. Choosing correctly depends on whether the requirement is code-first stream/batch processing, Hadoop/Spark migration, no-code ETL, SQL-based ELT in BigQuery, or cross-tool workflow orchestration.
Must-know
- Dataflow is the serverless, autoscaling choice for unified batch and streaming data processing using Apache Beam, best when you need custom code (Java/Python/SQL) and low-latency streaming transformations.
- Dataproc is the right choice when you need to run or migrate existing Apache Spark, Hadoop, Hive, or Pig jobs with minimal code changes, giving more infrastructure control (clusters) than Dataflow.
- Cloud Data Fusion is a visual, no-code/low-code ETL tool ideal for business users or teams who want drag-and-drop pipeline building with pre-built connectors and transformations rather than writing code.
- Dataform is used specifically for SQL-based ELT transformations inside BigQuery, enabling version control, testing, and dependency management for SQL workflows (similar to dbt), not for general-purpose or streaming data processing.
- Cloud Composer (managed Apache Airflow) is an orchestration tool, not a transformation engine itself. Use it to schedule and coordinate multi-step workflows across Dataflow, Dataproc, BigQuery, and other services.
- A common exam gotcha: if the scenario emphasizes 'no-code,' choose Cloud Data Fusion; if it emphasizes 'SQL-only transformations within BigQuery,' choose Dataform; if it emphasizes 'orchestrating multiple dependent jobs across services,' choose Cloud Composer.
A financial services company is migrating its on-premises Hadoop cluster, including several custom Apache Spark and MapReduce jobs, to Google Cloud. The engineering team wants to reuse the existing job code with minimal rewriting and retain familiar Hadoop ecosystem tools such as YARN and Hive. Which service should they choose to run these transformation workloads?
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 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.