Study plan
Every objective in the published exam guide, grouped by exam section and ordered the way the guide orders them.
0 answered correctly0 not correct, still open77 not tried yet0 of 77 objectives tried
This is a record of what you have tried, not a score and not a prediction of the real exam.
Start hereStudy noteSet up and configure an Azure Databricks environment
Choosing between job compute, serverless, warehouses, classic, and shared compute
Objective 1 of 13 in Set up and configure an Azure Databricks environment, the first section in the guide. Not tried yet. See all 4 exam sections
What this note covers
Azure Databricks offers several compute types optimized for different workloads: all-purpose (interactive) clusters for exploration, job compute for automated pipelines, SQL warehouses for SQL analytics and BI, and serverless options that remove infrastructure management. Choosing the right compute type balances cost, performance, isolation, and use case (interactive development vs. scheduled production jobs vs. SQL querying).
The facts the exam tests
- Job compute clusters are created automatically when a job runs and terminate automatically when the job completes, making them more cost-effective than all-purpose clusters for scheduled/production workloads.
- All-purpose (classic) clusters are meant for interactive, collaborative work in notebooks and can be shared by multiple users simultaneously, but incur cost while running/idle unless auto-termination is configured.
- SQL warehouses (formerly SQL endpoints) are compute resources optimized specifically for running SQL queries via Databricks SQL, and come in classic, pro, and serverless variants.
- Serverless compute (available for both notebooks/jobs and SQL warehouses) removes the need to configure or manage cluster infrastructure, offering rapid startup and automatic scaling managed by Databricks.
- Shared/multi-user clusters allow several users to attach to the same compute resource simultaneously, which is efficient for teams but requires appropriate access mode configuration (e.g., shared access mode) to ensure proper data isolation and governance with Unity Catalog.
- Cluster access modes (e.g., single user vs. shared) affect which features (like certain library installs or specific language support) are available and impact Unity Catalog enforcement, so the access mode must match the intended use case.
A BI team connects Power BI to Delta tables in Azure Databricks so analysts can run ad hoc queries throughout the day. The team wants queries to start almost instantly, without waiting for a cluster to boot, and wants Azure Databricks to handle scaling automatically as concurrent query load changes. Which compute type best meets these requirements?
The 4 exam sections
Ordered as the guide orders them
Set up and configure an Azure Databricks environment15-20% of the exam0 of 13 tried
Secure and govern Unity Catalog objects15-20% of the exam0 of 12 tried
Prepare and process data30-35% of the exam0 of 28 tried
Deploy and maintain data pipelines and workloads30-35% of the exam0 of 24 tried
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.