Centralizing row and column access rules with Unity Catalog ABAC policies
Unity Catalog Attribute-Based Access Control (ABAC) policies let admins centrally define row filters and column masks using tags and conditions instead of hand-writing per-table SQL functions. Policies are attached at the catalog, schema, or table level and apply automatically to any matching object, reducing per-object governance overhead. This replaces manual row filter and column mask functions with a scalable, tag-driven approach.
Must-know
- ABAC policies use governed tags (on columns, tables, or attributes) as conditions to decide when a row filter or column mask applies, so tagging data correctly is essential for policies to take effect.
- Policies can be defined once at a catalog or schema level and automatically inherited by all matching tables/columns, avoiding the need to write a separate masking function for every table.
- Column masking policies can redact, hash, or partially obscure sensitive column values based on the querying user's group membership or attributes, without altering the underlying stored data.
- Row filter policies restrict which rows a user sees based on attribute matches (e.g., region or department tags), enforced transparently at query time regardless of how the data is accessed.
- ABAC policies are managed via SQL (CREATE POLICY / governance UI) and require appropriate metastore-level privileges to create or attach; policy changes take effect without needing to rewrite views or tables.
- Because enforcement happens centrally in Unity Catalog, ABAC policies apply consistently across all compute (SQL warehouses, clusters, notebooks) and access paths, unlike ad hoc view-based filtering.
An ABAC policy applies a row filter so analysts only see rows where region matches their assigned region attribute. A user with no region attribute set queries the table. What happens?
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 26, 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.