Quick answer
This is a reference, not a tutorial: exam facts, then every task the DP-750 skills outline names, mapped to the exact Databricks UI path, SQL statement, or CLI command that does it, plus a pattern table for exam phrasing that maps to a specific concept. Pair it with the full study guide for the reasoning behind each choice.
Exam Snapshot
| Exam code | DP-750 |
| Certification | Microsoft Certified: Azure Databricks Data Engineer Associate |
| Domains | 4 — two at 15–20%, two at 30–35% |
| Passing score | 700 / 1000 (scaled) |
| Exam / seat time | Not published per-exam by Microsoft as of this writing; general Associate/Expert (no-lab) allocation is 100 min exam / 120 min seat |
| Prerequisites | None enforced; SQL, Python, Git/SDLC, Microsoft Entra, Azure Data Factory, and Azure Monitor familiarity recommended |
| Languages | SQL and Python only — Scala is not named on the current audience profile |
| Renewal | Annual, free online assessment on Microsoft Learn |
| Practice Assessment | Available via AI Skills Navigator (sign-in required) |
Source: Microsoft Learn's DP-750 study guide, checked September 7, 2026. Price and per-exam duration/question count are not published on that page — we left them out rather than borrow a figure from a different exam.
Drill this into memory
Practice the Real Task-Selection Logic
40 free DP-750 questions, scored instantly. No credit card required.
Start Free Practice →Environment and Compute Tasks
Domain 1 (15–20%) is largely about choosing and configuring the right compute and Unity Catalog objects up front.
| Task | Where / How |
|---|---|
| Choose compute for interactive BI/SQL | Serverless SQL warehouse |
| Choose compute for a scheduled batch job | Job compute |
| Configure autoscaling / node type / pooling | Compute → Edit → Performance settings |
| Enable faster query execution | Compute → Photon acceleration toggle |
| Create a catalog / schema / volume | CREATE CATALOG / CREATE SCHEMA / CREATE VOLUME |
| Connect to an external data source as a catalog | Catalog Explorer → Foreign catalog → Connection |
| Let business users query data conversationally | Catalog Explorer → AI/BI Genie instructions |
Unity Catalog Governance Tasks
Domain 2 (15–20%) — the same weight as Domain 1, and easy to underestimate.
| Task | Where / How |
|---|---|
| Grant a user/group access to a table | GRANT SELECT ON TABLE … TO `group` |
| Hide specific column values by role | Column mask function on the table |
| Restrict which rows a user can see | Row filter function on the table |
| Read a secret from Key Vault in a notebook | dbutils.secrets.get(scope, key) |
| Authenticate a pipeline (not a user) to data | Service principal or managed identity |
| Tag-driven access rules across many objects | Attribute-based access control (ABAC) policy |
| See who queried a table and when | Catalog Explorer → Lineage / Audit logs |
| Share curated tables with another organization | Delta Sharing share/recipient |
Data Ingestion and Processing Tasks
Domain 3 (30–35%) — the largest domain, together with Domain 4.
| Task | Where / How |
|---|---|
| One-time batch load from files | CREATE TABLE … AS / COPY INTO |
| Continuously ingest new arriving files | Auto Loader (inside a Declarative Pipeline) |
| Ingest from a managed SaaS/DB source with minimal setup | Lakeflow Connect |
| Continuous low-latency processing | Spark Structured Streaming |
| Ingest a live event stream | Azure Event Hubs connector |
| Apply upserts from a source system\'s change feed | CDC feed + MERGE |
| Preserve full change history on a dimension | SCD Type 2 (versioned rows) |
| Enforce column types / block malformed rows | Schema enforcement + data type checks |
| Declare validation rules with a fail threshold | Pipeline expectations (Lakeflow Declarative Pipelines) |
Pipeline and Job Tasks
Domain 4 (30–35%) — shipping and keeping pipelines alive.
| Task | Where / How |
|---|---|
| Build a declarative, expectation-driven pipeline | Lakeflow Spark Declarative Pipelines |
| Run tasks on a fixed schedule | Lakeflow Job → Cron trigger |
| Start a run the moment a file lands | Lakeflow Job → File-arrival trigger |
| Recover automatically after a transient failure | Job → Automatic restarts / retry policy |
| Package job/pipeline config as version-controlled code | Databricks Asset Bundles |
| Deploy a bundle from a CI/CD pipeline | databricks bundle deploy (CLI) or REST API |
| Review/approve a teammate\'s pipeline change | Git pull request |
Monitoring and Optimization Tasks
| Task | Where / How |
|---|---|
| Diagnose one slow task in an otherwise-fast stage | Spark UI + query profile → check for skew |
| Visualize a job\'s execution stages | DAG (Directed Acyclic Graph) view |
| Reclaim space from old Delta file versions | VACUUM table_name |
| Compact small files for faster reads | OPTIMIZE table_name |
| Improve reads filtered on a changing, high-cardinality column | Liquid clustering |
| Send job logs to a central monitoring workspace | Log Analytics via Azure Monitor |
| Get notified when a job fails or runs long | Azure Monitor alert rule |
If the Exam Says This, Think That
DP-750 scenarios describe a situation rather than naming the feature directly. This table maps common phrasing to the concept it is actually testing.
| If the exam says… | Think |
|---|---|
| "…must preserve every historical version of a row" | SCD Type 2 / temporal table |
| "…one task is taking far longer than the rest of the stage" | Data skew |
| "…needs to start the moment a file arrives, not on a schedule" | File-arrival trigger |
| "…must hide a column\'s value but keep the table queryable" | Column mask |
| "…share curated data with another org, no copy, no full access" | Delta Sharing |
| "…the pipeline should call itself to a service, not a person" | Service principal / managed identity |
| "…stop the run automatically if too many rows fail a rule" | Pipeline expectations |
| "…same job config deployed identically via a Git-reviewed workflow" | Databricks Asset Bundles |
| "…reads filtered on a column whose values keep changing" | Liquid clustering (over static Z-ordering) |
| "…recover automatically without manual intervention" | Automatic restarts / retry policy |
Exam-day reminder:DP-750's audience profile names only SQL and Python. If a scenario shows Scala syntax, treat it as legacy/inherited code being described, not a language you need to write yourself on this exam.
Common Cheat-Sheet Questions
Can I print this for exam-day review?
Yes — every table on this page uses plain borders with no interactive elements, so it prints cleanly from any browser's print dialog.
Is memorizing this task list enough to pass DP-750?
No. DP-750 is scenario-based, and roughly two-thirds of the exam sits in the two largest domains — this sheet is for fast recall of terms and task mappings you've already studied, not a substitute for the full study guide or working through practice scenarios.
Why isn't Scala anywhere on this sheet?
Because Microsoft's official DP-750 audience profile only names SQL and Python, unlike the retired DP-203 exam, which explicitly required Scala. This cheat sheet reflects what the current exam actually tests.
Does this cover Databricks CLI syntax in depth?
No — this sheet maps tasks to the tool or command category that does them (CLI, REST API, SQL, or UI path), since the exam tests knowing which mechanism fits a scenario, not memorizing every CLI flag.
What's the real difference between liquid clustering and Z-ordering?
Z-ordering is a static optimization you re-run manually; liquid clustering adapts more gracefully as data and query patterns change over time — the exam tests picking the right one for a described access pattern, not defining either term.
Is this cheat sheet useful for DP-700 (Fabric) too?
No — DP-700 tests Microsoft Fabric-native tools (Lakehouses, Fabric pipelines), which use different terminology and portal paths than Azure Databricks. Use this sheet only for DP-750 prep.
About This Reference
MSCertQuiz sells a 500-question DP-750 practice bank alongside this cheat sheet, built by the same team. Facts above trace to Microsoft's DP-750 study guide and Azure Databricks documentation, checked September 7, 2026.