ExamSprintLab

DP-750 study plan: pass in 4 weeks

Updated October 9, 2026

You can prepare for DP-750 in four weeks if you already write SQL and Python and have used a Databricks workspace. The plan below spends the first week on environment and governance, the second and third on the two heavy domains (preparing data, and deploying and maintaining pipelines), and the last on tuning, troubleshooting and review. Budget about 8 to 10 hours a week and a Databricks workspace where you can build things. Without any Databricks background, double the time.

Before you start

Read the current skills outline summary on the exam format page, and take Microsoft’s practice assessment cold to see where you stand. Check the prerequisites page: SQL, Python, Git and a working picture of Microsoft Entra and Azure Monitor are assumed.

Week 1: environment and Unity Catalog security (30–40% together)

This week covers the two smaller domains.

  • Compute: the difference between serverless, job compute, SQL warehouses, classic all-purpose compute and shared compute; autoscaling, auto-termination, node types, pools, Photon, runtime versions, libraries and compute permissions.
  • Unity Catalog objects: the catalog, schema, volume, table, view and materialized view hierarchy; managed against external tables; foreign catalogs and connections; naming conventions for isolation and environments.
  • Security: grants to users, groups and service principals; row filters and column masks; ABAC with tags and policies; Key Vault-backed secrets; service principals and managed identities; lineage, audit logging, retention and Delta Sharing (now OpenSharing).

Lab: create a catalog with dev and prod schemas, grant a group read access, add a column mask and a row filter, and trace lineage in Catalog Explorer. Read the environment guide and the governance guide.

Week 2: preparing and processing data (30–35%)

The largest domain. Work through its four parts in order:

  1. Modelling: choose a table format, a partition or liquid clustering strategy, a slowly changing dimension type, and a temporal history table.
  2. Ingestion: Lakeflow Connect, notebooks, CTAS and COPY INTO, CDC feeds, Structured Streaming, Event Hubs, and Auto Loader inside Lakeflow Spark Declarative Pipelines.
  3. Transformation: profiling, duplicates and nulls, joins and set operators, pivots, and merge, insert and append loads.
  4. Quality: constraints, schema enforcement and drift, and pipeline expectations.

Lab: ingest a folder of JSON files with Auto Loader, apply a MERGE from a CDC feed into a Delta table, and add an expectation that drops bad rows. Read the data processing guide.

Week 3: pipelines, jobs and deployment (30–35%)

  • Notebook-based pipelines against Lakeflow Spark Declarative Pipelines, and when to choose each.
  • Lakeflow Jobs: tasks and dependencies, triggers, schedules, alerts, retries and automatic restarts, plus error handling.
  • Git folders, branching, pull requests, conflict resolution, and a testing strategy from unit tests to UAT.
  • Packaging a Declarative Automation Bundle and deploying it with the Databricks CLI or REST APIs.

Lab: build a three-task job, put it in a bundle, deploy it to a dev target with the CLI, and break a task to practise repair runs. Read the pipelines guide.

Week 4: monitoring, tuning and review

Cover cost and cluster monitoring, repairing jobs, the Spark UI and query profile for skew, spill and shuffle, OPTIMIZE and VACUUM, Log Analytics log streaming and Azure Monitor alerts. Then take the 20-question practice test, review each wrong answer by domain, and repeat the labs for your weakest domain. Read the exam tips and use the cheat sheet in the last two days.

If you are already experienced

If you run production Databricks pipelines, skim weeks 1 to 3 and spend the time on what daily work rarely covers: ABAC, Delta Sharing (OpenSharing), audit logging, foreign catalogs, Key Vault access, managed identities, Azure Monitor log streaming, and bundle deployment through REST APIs. Engineers who use only notebooks often have gaps there.

How to know you are ready

  • You score 80% or more on realistic practice questions twice in a row.
  • You can explain, without notes, when to use liquid clustering, Z-ordering, or neither.
  • You can say which compute type fits an interactive notebook, a nightly job and a BI query.
  • You have deployed a bundle yourself and repaired a failed job run.
  • No domain is below 70% in your practice results.

When you are there, book, and see the exam day guide.