DP-750 Cheat Sheet: Key Facts to Review

The short version

Use this sheet to refresh, not to learn. It lists what to recall per domain, which similar features get mixed up, and what the scenario wording usually points to. It does not cover everything on the exam, and it is no substitute for hands-on work in a workspace. The outline behind it is Microsoft’s “Skills measured as of October 19, 2026”.

Exam facts at a glance

Item	Fact
Exam	DP-750, Implementing Data Engineering Solutions Using Azure Databricks
Level	Associate
Passing score	700 on a scale of 1 to 1,000 (scaled, not 70 percent)
Domains	Set up environment 15-20%, Secure and govern Unity Catalog 15-20%, Prepare and process data 30-35%, Deploy and maintain pipelines 30-35%
Time	120 minutes, per Microsoft’s DP-750 certification page
Questions	Not published; Microsoft says 40 to 60 is typical
Guessing	No penalty, so answer everything
Learn access	Available during associate exams; the clock keeps running
Credential validity	One year, renewed free online

Domain 1: Set up and configure the environment

Key concepts

Compute types: job compute, serverless, SQL warehouse, classic compute, shared compute.
Settings: node type, node count, autoscaling, auto termination, pools, runtime version, Photon.
Unity Catalog hierarchy: catalog, schema, then tables, views, volumes.
Foreign catalogs use connections to query external systems.

Term pairs

A view stores a query; a materialized view stores the computed result and refreshes it.
A managed table lets Unity Catalog control data files; an external table points to a location you control.
A volume holds files; a table holds structured rows.
Job compute exists for a run and ends; all-purpose compute serves interactive work.

Decision rules

Scheduled production workload with no interactive use: think job compute or serverless.
Interactive SQL by analysts: think a SQL warehouse.
Idle clusters costing money: think auto termination.
Separate dev, test, and prod data: think separate catalogs or schemas with clear naming.

Traps

Picking a large cluster when autoscaling or serverless fits the need.
Forgetting that dropping a managed table removes its data, while dropping an external table does not.

Domain 2: Secure and govern Unity Catalog objects

Key concepts

Privileges go to a principal: user, group, or service principal.
Table-level, column-level, and row-level control.
Secrets live in Azure Key Vault and are reached through the platform.
Service principals and managed identities authenticate automated access.
Governance: descriptions, tags, lineage, audit logs, retention, Delta Sharing.

Term pairs

Row filter hides rows; column mask changes what a column shows.
ABAC applies policies by tags; a plain grant names one object.
Service principal is an app identity you manage; managed identity is an identity Azure manages for a resource.
Lineage shows where data came from and goes; audit logs show who did what.

Decision rules

Different users must see different rows of one table: row filter.
Sensitive column visible only to some users: column mask.
Policy should follow a tag across many tables: ABAC.
Share data with an outside party without copying it: Delta Sharing, with a secure setup.
A job needs a credential: Key Vault secret, not text in a notebook.

Traps

Granting access to a table without access to its parent catalog and schema.
Giving broad privileges to a person when a group would do.

Domain 3: Prepare and process data

Key concepts

Ingestion tools: Lakeflow Connect, notebooks, Azure Data Factory, SQL methods, Structured Streaming, Event Hubs, Lakeflow Spark Declarative Pipelines with Auto Loader.
Formats: Parquet, Delta, CSV, JSON, Iceberg.
Layout: partitioning, liquid clustering, Z-ordering.
Slowly changing dimensions: type 1 overwrites, type 2 keeps history.
Quality: nullability, ranges, schema enforcement, schema drift, pipeline expectations.

Term pairs

COPY INTO loads new files from a location in SQL; Auto Loader discovers files incrementally and scales to many.
Batch processes a bounded set; streaming processes continuously or incrementally.
Partitioning splits folders by value; liquid clustering adapts layout without a fixed scheme.
MERGE upserts; append only adds; CTAS creates a table from a query.
Schema enforcement rejects bad writes; schema evolution allows planned changes.

Decision rules

Many new files arriving continuously: Auto Loader.
Keep history of changed dimension rows: SCD type 2.
Apply inserts, updates, and deletes from a change feed: CDC with merge-style logic.
Declare quality rules inside a pipeline: expectations.
Very high-cardinality columns used in filters: clustering rather than partitioning.

Traps

Partitioning on a column with too many distinct values.
Choosing append when the scenario needs to update existing rows.

Domain 4: Deploy and maintain pipelines and workloads

Key concepts

Notebook pipelines versus Lakeflow Spark Declarative Pipelines.
Lakeflow Jobs: tasks, triggers, schedules, alerts, retries, repair runs.
Git: branches, pull requests, conflict resolution.
Testing levels: unit, integration, end to end, user acceptance.
Declarative Automation Bundles, deployed with the CLI or REST APIs.
Monitoring: cluster consumption, Spark UI, query profile, Azure Monitor and Log Analytics.
Table upkeep: OPTIMIZE and VACUUM.

Term pairs

Repair run re-runs only failed tasks; restart runs the job again.
OPTIMIZE compacts small files; VACUUM removes old unreferenced files.
Skew is uneven partitions; spill is data written to disk when memory is short; shuffle is data moving between nodes.
Unit test checks one piece; UAT checks the result with the business.

Decision rules

One task failed in a long job: repair, not a full rerun.
Many tiny files slowing reads: OPTIMIZE.
One task much slower than its peers: suspect skew, check the Spark UI.
Same deployment for dev, staging, and prod: bundles.
Alert on failures outside Databricks: Azure Monitor.

Traps

Running VACUUM with a very short retention and losing time travel.
Treating a performance symptom as a cluster-size problem before checking the plan.

Last-day checklist

Re-read the skills list and mark anything you cannot explain in a sentence.
Review the term pairs above out loud.
Reread the notes from your missed practice questions.
Confirm the exam time, ID, and room or system check.
Plan your pacing: mark hard questions and return.
Sleep. A tired mind misreads “not” and “except”.

More guides for DP-750

Next step

Revisit the domains explained page for any area that still feels thin, then check the exam format page for scoring and pacing. For timed practice, try the DP-750 practice tests. The DP-750 overview links everything together.

Try the DP-750 practice exams