Editing LookML to change what a model exposes
LookML defines Looker's semantic data model using dimensions, measures, views, and explores written in a version-controlled project. Associate Data Practitioners are expected to read and make simple, guided edits to existing LookML parameters (such as label, description, sql, hidden, or type) without building models from scratch.
Must-know
- LookML files use .view.lkml, .model.lkml, and .explore.lkml extensions and are organized in a Looker project connected to a Git repository.
- Dimensions represent groupable/filterable fields (columns or derived SQL expressions), while measures represent aggregations (sum, count, average) computed via LookML measure types.
- Common editable parameters include label and description (change display text), hidden: yes/no (show or hide a field in Explore), and sql (modify the underlying SQL expression for a field).
- Changes made in the LookML IDE must be saved and validated (LookML validator checks syntax) before committing and deploying to production via Git.
- The Explore interface reflects LookML changes only after validation passes and the model is deployed; syntax errors block saving.
- Renaming or hiding a field in LookML does not alter the underlying BigQuery table or data. It only changes how the field is presented in Looker's semantic layer.
A data practitioner is building an Explore in LookML. The view includes a dimension named user_id that is needed internally for joins and primary key definitions, but the team wants it removed from the field picker so business users cannot select it directly. Which change to the dimension accomplishes this without breaking existing joins?
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.