Turning a question from the business into an analysis that settles it
Analyzing data to answer business questions on Google Cloud typically involves querying data in BigQuery using SQL, then visualizing or exploring results with tools like Looker Studio or Looker. The Associate Data Practitioner exam focuses on choosing the right query techniques, aggregation functions, and joins to derive business insights, as well as picking appropriate visualization tools for presenting findings.
Must-know
- BigQuery supports standard SQL with aggregate functions (SUM, AVG, COUNT, MIN, MAX), GROUP BY, and window functions to answer business questions like trends, totals, and rankings.
- BigQuery's built-in query results can be directly explored using 'Explore in Looker Studio' or 'Explore with Sheets' for quick ad hoc analysis without writing additional code.
- Looker Studio connects natively to BigQuery, Google Sheets, and Cloud SQL, enabling drag-and-drop dashboards and reports without needing deep SQL knowledge.
- BigQuery ML allows practitioners to run basic predictive analysis (e.g., forecasting, classification) directly with SQL syntax, avoiding the need to export data to a separate ML platform.
- Materialized views and scheduled queries in BigQuery help automate recurring business reporting needs by pre-computing and refreshing summary data.
- When presenting data, choosing the right visualization (e.g., time series for trends, bar charts for comparisons) is as important as the underlying query logic for effectively communicating insights to business stakeholders.
A data analyst has a BigQuery table named sales_summary with one row per category per month, containing columns category, month, and monthly_revenue. The analyst needs a result set with exactly one row per category showing the month with the highest monthly_revenue over the past 12 months. Which SQL approach correctly achieves this?
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.