Deciding whether a job calls for Looker or for Looker Studio
Looker and Looker Studio are both Google Cloud BI tools but target different use cases: Looker is an enterprise-grade platform for governed, semantic-layer-driven analytics at scale, while Looker Studio is a free, lightweight tool for quick, self-service dashboards and reports. Choosing between them depends on needs like data governance, scale, connectivity, and team size.
Must-know
- Looker uses LookML, a semantic modeling layer, to define consistent business metrics and enforce governance across an organization, ensuring all users see standardized, trusted data.
- Looker Studio is free (with a paid Pro tier for team collaboration features) and excels at rapid, ad hoc report and dashboard creation with an intuitive drag-and-drop interface.
- Looker connects natively to databases (including BigQuery) via a persistent semantic layer and supports real-time queries at enterprise scale, making it suited for embedded analytics and large organizations.
- Looker Studio connects to data via connectors (including a native BigQuery connector) and is ideal for individuals or small teams needing quick visualizations without heavy infrastructure setup.
- Looker supports advanced features like data actions, custom visualizations, version control (Git integration), and API-driven embedded analytics, which Looker Studio does not offer.
- For exam purposes, choose Looker Studio for lightweight, self-service reporting and fast time-to-insight, and choose Looker when the requirement emphasizes governance, a centralized semantic model, or enterprise-scale embedded analytics.
A data platform team wants to define company-wide metrics such as 'net revenue' and 'active customer' exactly once, enforce that definition for every downstream report, and manage changes to that logic through a code review and version control process. Which tool should they use to build this governed semantic layer?
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.