Wiring notebook, SQL, dashboard, and pipeline tasks into a job's task graph
Lakeflow Jobs orchestrate multi-task workflows as a DAG, where each node is a task (notebook, SQL query, dashboard refresh, pipeline, Python script, etc.) and edges define dependencies via 'Depends on'. Tasks run on job clusters or serverless compute, and a task only fires once all its upstream dependencies finish successfully, enabling fan-out/fan-in patterns and conditional branching.
Must-know
- A task's 'Depends on' field can list multiple upstream tasks, and by default all of them must succeed for the downstream task to run.
- Task run conditions can be changed from 'All succeeded' to options like 'At least one succeeded', 'All done', or 'At least one failed' to control branching on failure.
- Notebook, SQL, dashboard, and pipeline tasks each require a compute target: notebook and SQL script tasks can run on job/all-purpose clusters or serverless, SQL query/dashboard tasks run against a SQL warehouse, and pipeline tasks trigger a Lakeflow Declarative Pipelines update rather than running on a job cluster.
- A dashboard task refreshes a Lakeview dashboard and depends on the SQL warehouse and underlying queries being current, so it's commonly chained after notebook/pipeline tasks that update the source tables.
- Parameters and values can be passed between tasks using task values (dbutils.jobs.taskValues) or job/task parameters referenced with {{}} syntax, letting downstream tasks consume upstream outputs.
- The DAG structure is visualized in the Jobs UI graph view, and a job run fails overall if any task without a satisfied alternate condition fails, though 'Repair run' lets you rerun only the failed and downstream tasks.
A team wants a Lakeflow Job where a dashboard task, Sales_Dashboard, refreshes only after two independent SQL query tasks, Revenue_Query and Returns_Query, have both completed successfully, but those two query tasks have no dependency on each other and should run concurrently to save time. How should the task graph be configured?
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.