Granting, revoking, and denying access with SQL and the UI
Unity Catalog secures data using standard SQL GRANT/REVOKE syntax applied across a hierarchy: metastore, catalog, schema, table/view, and column/row levels. Privileges can be assigned to users, groups, or service principals, and access can also be managed visually through the Catalog Explorer UI. Effective access is determined by combining grants inherited from higher levels with grants at the specific object level.
Must-know
- Privileges granted at a catalog or schema level are inherited by all objects nested beneath them, so granting USE CATALOG and USE SCHEMA plus SELECT at the catalog level cascades down to all schemas/tables within it.
- GRANT SELECT ON TABLE catalog.schema.table TO
group_nameis the standard syntax; REVOKE removes a previously granted privilege using the same object path. - Common privileges include USE CATALOG, USE SCHEMA, SELECT, MODIFY, CREATE TABLE, and ALL PRIVILEGES, and the correct privilege must exist at every level of the hierarchy (catalog, then schema, then table) for a query to succeed.
- Unity Catalog does not support DENY; access control is additive only through GRANT and REVOKE, so restricting access means revoking or simply never granting a privilege rather than explicitly denying it.
- The Catalog Explorer UI's Permissions tab lets admins grant/revoke the same privileges as SQL commands, making it interchangeable with running GRANT/REVOKE statements in a notebook or SQL editor.
- Service principals are treated as principals just like users and groups, and are the recommended identity type for granting access to automated jobs and pipelines rather than granting access to individual user accounts.
A data engineering team stores raw and curated tables in the same schema. Analysts need SELECT on curated tables only, must never see raw tables, and the grant must apply automatically to any curated table added later. Which approach meets these requirements?
What you have tried across Databricks DEA's objectives, not a readiness score.
Databricks Intelligence Platform
Data Ingestion and Loading
- Batch, streaming, and incremental loading patterns, and where the data comes from
- Loading files from cloud storage into governed tables with COPY INTO
- Landing data with Auto Loader, and handling schema enforcement and evolution
- Setting up Lakeflow Connect to ingest from enterprise sources reliably
- Pulling data through JDBC, ODBC, or REST clients and scheduling the job
- Choosing the right ingestion method for a given volume, frequency, and governance need
- Bringing semi-structured and unstructured data into governed Delta tables
Data Transformation and Modeling
- Cleaning bronze data into silver tables with PySpark and SQL
- Joining and combining DataFrames with the different join and union types
- Reshaping columns, rows, and arrays in a table
- Deduplicating and aggregating DataFrames
- Tuning Spark's core parameters and measuring what changed
- Building Gold-layer views and tables for BI and analytics
- Validating Silver and Gold datasets for quality
Working with Lakeflow Jobs
Implementing CI/CD
- Branching, committing, and opening pull requests from inside the Databricks workspace
- Promoting one codebase across dev, test, and prod with bundle variables and overrides
- Packaging and deploying jobs and pipelines with Automation Bundles
- Validating and managing bundle deployments from the Databricks CLI
Troubleshooting, Monitoring, and Optimization
- Spotting performance trends in a job's run history
- Reading job status, task graphs, and failure rates to monitor pipeline health
- Diagnosing skew, shuffle, and spill from Spark UI stage metrics
- What Liquid Clustering and predictive optimization actually do
- Diagnosing cluster startup failures, library conflicts, and out-of-memory errors
Governance and Security
Coverage checked against the published exam guide on Jul 27, 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.