Putting a query on a schedule and keeping it running
Google Cloud offers several ways to run queries and data pipelines on a schedule, ranging from BigQuery's built-in scheduled queries for simple recurring SQL jobs to Cloud Scheduler for triggering serverless functions or Pub/Sub messages, up to Cloud Composer for complex, multi-step, dependency-driven workflows. Choosing the right tool depends on complexity, orchestration needs, and whether you need cross-service coordination.
Must-know
- BigQuery scheduled queries use the BigQuery Data Transfer Service under the hood and require the caller to have appropriate BigQuery Data Editor/Job User roles plus the transfer service enabled.
- Scheduled queries run under the credentials of the user who created them (or a service account if configured), so query failures often occur silently if that user's permissions change or their OAuth token expires.
- Cloud Scheduler is a fully managed cron-job scheduler that can invoke HTTP endpoints, Pub/Sub topics, or Cloud Run/Cloud Functions, making it suitable for triggering custom scripts or lightweight pipeline steps but not for defining multi-step DAG dependencies itself.
- Cloud Composer (managed Apache Airflow) is the recommended tool for orchestrating complex workflows with task dependencies, retries, branching logic, and cross-service coordination (e.g., BigQuery, Dataflow, Cloud Storage) beyond simple time-based triggers.
- BigQuery scheduled queries support configurable repeat frequency (daily, weekly, custom cron) and can write results to a destination table, but they lack built-in dependency management for downstream tasks. Use Composer or Workflows if downstream steps depend on query completion.
- For cost and simplicity, prefer BigQuery scheduled queries for single recurring SQL jobs, Cloud Scheduler for simple periodic triggers of external actions, and Cloud Composer only when you need orchestration of multiple interdependent tasks across services.
A data practitioner creates a scheduled query in BigQuery that runs daily, reading from a table in a dataset located in "us-central1" and writing the results to a destination table in a dataset located in "europe-west1". The scheduled query consistently fails. What is the most likely cause?
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.