Choosing what should drive a multi-step workflow
Google Cloud offers several orchestration options ranging from lightweight scheduling to full workflow management, and choosing the right one depends on complexity, dependency management needs, and the tools involved. The Associate Data Practitioner exam expects you to match business requirements, such as multi-step pipelines, cross-service dependencies, or simple scheduled jobs, to the most appropriate orchestration service.
Must-know
- Cloud Composer (managed Apache Airflow) is best for complex, multi-step data pipelines with dependencies across multiple GCP services (BigQuery, Dataproc, Cloud Storage, etc.) and requires DAGs written in Python; it has higher cost and operational overhead than lighter alternatives.
- BigQuery scheduled queries are the simplest solution when the entire workflow is a single SQL query or a short sequence of queries run on a time-based schedule, with no need for cross-service orchestration or complex branching logic.
- Dataproc Workflow Templates are purpose-built for orchestrating a sequence of Dataproc jobs (Spark, Hadoop, etc.) on ephemeral or existing clusters, including automatic cluster creation and teardown, making them ideal when orchestration is scoped entirely within Dataproc.
- Workflows is a lightweight, serverless orchestrator best suited for coordinating a sequence of HTTP-based services, Cloud Functions, or Cloud Run jobs with conditional logic, retries, and error handling, without the overhead of managing Airflow environments.
- A key exam gotcha: if the requirement mentions orchestrating tasks across many different GCP products with complex dependency graphs and scheduling, Cloud Composer is usually the correct answer; if the requirement is narrowly scoped (e.g., just BigQuery SQL or just Dataproc jobs), prefer the lighter-weight native tool.
- Cost and management overhead should guide selection: scheduled queries and Workflows require minimal setup and maintenance, Dataproc Workflow Templates require some Dataproc knowledge, while Cloud Composer demands ongoing environment management and is the most expensive/heaviest option.
A retail analytics team wants to refresh a single BigQuery table every night by running one SQL statement that aggregates the previous day's sales into a summary table. No other Google Cloud services are involved, and the team wants the lowest-maintenance option. Which orchestration approach should they choose?
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.