Building a dashboard and getting it in front of the right people
Looker Studio is Google Cloud's primary tool for building and sharing interactive dashboards that connect to BigQuery, Sheets, Cloud SQL, and hundreds of other data sources. Dashboards use charts, filters, and controls to let business users explore data and answer questions without writing queries, and Looker Studio also supports connecting through Looker's semantic layer for governed metrics.
Must-know
- Looker Studio connects directly to BigQuery and can use either a live connection (queries run against BigQuery each time, reflecting current data) or an extracted/imported data source (a cached snapshot that refreshes on a schedule and often performs faster for large datasets).
- Blending data in Looker Studio lets you combine up to multiple data sources in a single chart using a join key, useful for answering questions that span different tables or systems without pre-joining in BigQuery.
- Sharing a Looker Studio report follows Google Drive-style sharing: you can share with specific people/groups, generate a link with viewer or editor access, or schedule automated email delivery of the report as a PDF/image.
- Report viewers only see data they have permission to access if the underlying data source uses viewer's credentials (row-level security via BigQuery IAM/authorized views) rather than owner's credentials, which is an important gotcha when sharing dashboards containing sensitive data.
- Filters, filter controls, and parameters allow dashboard consumers to interactively slice data (e.g., by date range or region) without needing to duplicate charts or modify the underlying dataset.
- Calculated fields let you create custom metrics/dimensions directly in Looker Studio using formulas, which is useful for quick business logic changes without altering the source BigQuery tables or views.
A data practitioner built a Looker Studio dashboard that queries a table in BigQuery. The dashboard needs to be shared with external partners who do not have any IAM role on the BigQuery project, but the partners still need to see live, up-to-date data rather than a static snapshot. Which sharing configuration should the practitioner 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 13, 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.