Writing BigQuery SQL that answers a reporting question
BigQuery uses standard SQL to query large datasets stored in tables and views, letting analysts generate reports and extract insights directly from the console, bq CLI, client libraries, or connected BI tools. Understanding query structure, functions, and cost/performance behavior is essential for the Associate Data Practitioner exam.
Must-know
- BigQuery uses GoogleSQL (standard SQL) by default; legacy SQL must be explicitly enabled and is largely deprecated.
- Queries can reference tables using fully qualified names in the form project.dataset.table, which is required when querying across projects.
- Common aggregation and analysis functions include SUM, COUNT, AVG, GROUP BY, ORDER BY, and window functions (e.g., RANK,
ROW_NUMBER) for ranking and running calculations. - BigQuery charges on-demand queries based on bytes scanned, so using SELECT with specific columns instead of SELECT * and filtering with WHERE clauses on partitioned/clustered columns reduces cost.
- Saved queries, scheduled queries, and views (including materialized views) allow recurring reports to be automated and reused without rewriting SQL.
- Query results can be exported to Google Sheets, Looker Studio, or other BI tools for visualization, or saved as new tables/views for downstream use.
A data practitioner is building a monthly revenue report from an orders table with a TIMESTAMP column named order_time. The current query groups rows using GROUP BY EXTRACT(MONTH FROM order_time), which combines January 2024 and January 2025 sales into a single group, producing incorrect totals for a report spanning multiple years. What change correctly groups revenue by calendar month within each year?
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.