Wiring a trigger so one event starts the next step
Eventarc lets you build event-driven data pipelines on Google Cloud by routing events from sources like Cloud Storage, Pub/Sub, BigQuery, and Cloud Audit Logs to targets such as Cloud Functions, Cloud Run, Workflows, and Cloud Composer/Dataflow pipelines. It decouples event producers from consumers, enabling automatic triggering of ingestion, transformation, or orchestration steps as soon as new data arrives or a resource state changes. For the Associate Data Practitioner exam, understand Eventarc as the standard mechanism to start pipelines reactively rather than on a fixed schedule.
Must-know
- Eventarc uses Cloud Audit Logs or direct events (e.g., Cloud Storage object finalize) as triggers, so the underlying API must have audit logging enabled for audit-log-based triggers to fire.
- A common pattern is Cloud Storage upload → Eventarc trigger → Cloud Function or Cloud Run service that starts a Dataflow job or triggers a Dataform workflow invocation.
- Cloud Composer (managed Apache Airflow) can be triggered by Eventarc-driven Cloud Functions calling the Airflow REST API or by DAG sensors, since Composer itself is not a native Eventarc target.
- Eventarc requires the Eventarc API and often the Cloud Pub/Sub API to be enabled, and the triggering service account needs appropriate IAM roles (e.g., eventarc.eventReceiver, run.invoker) to invoke the target.
- Eventarc triggers can target Cloud Run and Cloud Functions (2nd gen, which runs on Cloud Run infrastructure) directly, making these the most common lightweight glue services in event-driven data pipelines.
- Unlike Cloud Scheduler (time-based) or Pub/Sub push subscriptions, Eventarc provides a unified, standardized eventing layer across many Google Cloud sources without requiring custom Pub/Sub topic wiring for supported event types.
A data engineer configures an Eventarc trigger that invokes a Cloud Function to start a Dataflow ingestion job whenever a new file lands in a Cloud Storage bucket. The function must run only after the object's bytes are fully written and durably stored, not merely when the object's metadata changes. Which Eventarc event type should the trigger use?
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.