Matching a workload to the right compute service and its cost model
Databricks offers several compute types—All-Purpose, Job, SQL Warehouses (Serverless, Pro, Classic), and Serverless compute for notebooks/jobs—each optimized for different workloads and billed differently in DBUs. Choosing the right compute impacts cost, startup latency, and concurrency, so exam scenarios test matching workload characteristics (interactive dev, scheduled ETL, BI/SQL queries) to the appropriate compute type.
Must-know
- All-Purpose compute is for interactive development in notebooks (collaborative, higher DBU cost) while Job compute is auto-created/terminated per job run and is cheaper per DBU since it lacks interactive overhead.
- SQL Warehouses (Serverless, Pro, Classic) are optimized for SQL analytics/BI tool connections; Serverless SQL Warehouses start almost instantly (no cluster spin-up wait) and auto-scale, trading some cost for speed and simplicity.
- Serverless compute (for notebooks, jobs, and SQL) removes the need to configure/manage clusters, reduces startup latency to seconds, and shifts billing purely to DBUs consumed rather than cloud VM uptime.
- Classic (non-serverless) compute requires you to configure instance types, autoscaling, and cluster policies, and you pay for underlying cloud VM time in addition to DBUs, so idle/misconfigured clusters can waste cost.
- Job compute should be used for scheduled/production pipelines to minimize cost since it's cheaper than All-Purpose and clusters are ephemeral, terminating automatically after the job completes.
- Cluster policies and autoscaling/auto-termination settings are key cost-control levers regardless of compute type, and exam questions often test recognizing when a workload is using an unnecessarily expensive compute choice (e.g., using All-Purpose for a scheduled job).
An analytics team runs ad hoc BI dashboard queries throughout the business day with unpredictable spikes in concurrent users. The team wants queries to start in seconds without provisioning or managing any infrastructure, and wants to pay only for compute while queries are running. Which SQL warehouse type should the team choose?
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 Aug 1, 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.