DP-750 exam tips: how to approach the questions
The best DP-750 tip is to find the one constraint in each scenario before you read the options: cost, latency, isolation, history, least privilege, or minimal code. Most wrong answers are valid Databricks features that simply do not meet that constraint. The exam gives you 120 minutes, which is enough if you stay disciplined, and it rewards hands-on experience over memorised definitions.
Read the requirement, then the code
Many items include a snippet: a MERGE, a streaming read, a GRANT, a bundle command. Read the requirement sentence first so you know what the snippet should achieve, then check what the code actually does. Typical traps are a missing checkpoint location, a grant without USE SCHEMA, or a write mode that overwrites when the scenario wants an append.
Match the workload to the compute
| Scenario clue | Think |
|---|---|
| Scheduled, unattended, predictable | Job compute or serverless jobs |
| Many concurrent SQL users or dashboards | SQL warehouse |
| Interactive exploration by one developer | All-purpose compute with auto-termination |
| Slow cluster start, frequent runs | Pools or serverless |
Know the “which one” pairs
The outline lists several pairs that look alike. These are worth knowing cold:
- Managed or external table: what DROP does to the files.
- Row filter or column mask: rows against values.
- Partitioning, liquid clustering or Z-ordering: cardinality and changing access patterns.
- COPY INTO or Auto Loader: small-scale SQL versus scalable incremental ingestion.
- Notebook or declarative pipeline: custom logic versus declared tables with expectations.
- OPTIMIZE or VACUUM: compact files versus delete unreferenced files.
- Repair, restart, stop or run: finish a failed run, start over, cancel, start new.
Watch the Azure integration
The audience profile names Microsoft Entra, Azure Data Factory and Azure Monitor, so the exam does not stay inside Databricks. When a question asks how to reach storage or a secret without storing credentials, think managed identity or a Key Vault-backed secret scope. When it asks for centralised alerts, think Log Analytics and Azure Monitor. When it asks about orchestrating copies from many sources, Azure Data Factory is an accepted ingestion tool.
Prefer the least-effort supported option
Databricks has a managed option for many tasks: Lakeflow Connect for supported sources, Auto Loader for files, serverless for compute, expectations for quality, repair runs for failures. When two answers both work and one is managed, built-in or less custom, it is usually the intended one, unless the scenario names a constraint that rules it out.
Time management
With 120 minutes and an unpublished question count, aim to spend no more than a couple of minutes on any item. Answer what you can, flag what you cannot, and come back, and make sure every question has an answer before you submit.
Common traps
- Choosing a familiar feature that is real but wrong for the constraint, such as Z-ordering for a table that needs flexible clustering.
- Forgetting that reading a table needs USE CATALOG and USE SCHEMA as well as SELECT.
- Confusing schema enforcement (reject) with schema evolution (accept).
- Treating VACUUM as a performance command and OPTIMIZE as a cleanup command.
- Using an old product name. Lakeflow, Declarative Automation Bundles and Lakeflow Spark Declarative Pipelines are the current names in the outline; older material says Delta Live Tables or Asset Bundles.
The last week
Re-read the current skills outline, take the practice test under time, and drill the pairs above with the cheat sheet. Do one end-to-end lab you have not done before, such as a bundle deployment. See the exam day guide for logistics.