Streaming messages into BigQuery as they arrive rather than in batches
Pub/Sub enables event-driven ingestion by publishing messages the moment data is generated, allowing near real-time delivery into BigQuery without polling or scheduled batch jobs. This pattern suits streaming use cases like clickstream analytics, IoT telemetry, application logs, and transactional events where low latency matters. Google Cloud offers multiple managed paths to move Pub/Sub data into BigQuery, each trading off simplicity versus transformation flexibility.
Must-know
- The Pub/Sub BigQuery subscription writes messages directly to a BigQuery table without needing Dataflow, ideal for simple schema-matched ingestion.
- Dataflow with the Pub/Sub-to-BigQuery template (or custom pipeline) is used when messages require transformation, enrichment, windowing, or complex parsing before landing in BigQuery.
- Pub/Sub guarantees at-least-once delivery, so downstream BigQuery tables or Dataflow pipelines must handle potential duplicate messages (e.g., via message IDs or dedup logic).
- Event-driven ingestion from Pub/Sub suits variable or unpredictable data arrival rates, unlike scheduled batch loads (e.g., BigQuery Data Transfer Service) which fit predictable, periodic data.
- Schema mismatches between Pub/Sub message payloads and the destination BigQuery table cause ingestion failures, so schema design and message format (JSON/Avro) must be planned upfront.
- Use Pub/Sub-to-BigQuery ingestion for use cases needing fresh, continuously updated data for dashboards or alerting, rather than for large historical batch loads better suited to other ingestion tools.
A retail company generates clickstream events from its website and wants these events available in a BigQuery table within seconds of being generated. The event schema already matches the destination table and no transformation is required before the data lands. Which approach best fits this use case?
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.