Browse Certification Practice Tests by Exam Family

Microsoft DP-750 Cheat Sheet: Azure Databricks

May 1, 2026

Review Microsoft Azure Databricks Data Engineer Associate (DP-750) workspace setup, Unity Catalog, pipelines, processing, governance, and maintenance traps before using the DP-750 practice page.

On this page

DP-750 focuses on Azure Databricks as a Microsoft data-engineering route. Use this cheat sheet to review workspace setup, Unity Catalog, pipelines, governance, processing, and maintenance before trying the DP-750 sample questions.

Use this with practice. Review the Azure Databricks checklist, then open the DP-750 page for sample questions, current-exam notes, and related data-engineering practice paths.

Open DP-750 practice page Compare Microsoft Azure routes

Exam snapshot

FieldDetail
IssuerMicrosoft
Certification nameAzure Databricks Data Engineer Associate
Exam codeDP-750
PlatformMicrosoft Azure Databricks
Status in IT MasterySample questions with Notify me form

Topic map

AreaWhat to knowCommon trap
Environment setupWorkspaces, compute, Microsoft Entra integration, networking, and platform configurationStarting with notebook code before access and compute are controlled
GovernanceUnity Catalog, permissions, lineage, data quality, and policy-aware accessGranting workspace access and assuming table permissions are solved
Data processingSQL, Python, transformations, layered data design, and workload patternsSkipping validation between raw and curated layers
Pipelines and maintenanceJob orchestration, monitoring, troubleshooting, deployment, and cost controlTreating a failed scheduled job as only a notebook issue

Must-know distinctions

DistinctionHow to decide
Workspace permission vs data permissionWorkspace access lets users enter the environment; Unity Catalog governs data objects.
Cluster vs job computeInteractive clusters support exploration; job compute supports scheduled, repeatable workloads.
Notebook vs pipelineA notebook can contain logic; a pipeline controls repeatable processing and dependencies.
Bronze, silver, and gold layersRaw data lands first, cleaned data follows, curated analytics outputs come last.
SQL vs PythonSQL is strong for declarative data work; Python is useful for procedural logic and libraries.
Data quality vs data securityQuality checks validate values and shape; security controls access and policy boundaries.
Monitoring vs troubleshootingMonitoring detects symptoms; troubleshooting traces the failed step, dependency, permission, or data issue.

High-yield checklist

  • Confirm identity, roles, workspace access, and data access separately.
  • Use Unity Catalog for governed tables, permissions, lineage, and discovery.
  • Choose compute based on workload type, schedule, isolation, and cost.
  • Validate raw ingestion before promoting data to curated layers.
  • Design pipelines with retry, dependency, monitoring, and ownership in mind.
  • Use secrets and managed identity patterns instead of hard-coded credentials.
  • Track data quality expectations and failures close to the transformation step.
  • Review job logs, cluster events, permissions, and source availability when pipelines fail.
  • Control cost through right-sized compute, job scheduling, and workload cleanup.
  • Compare the Microsoft DP-750 route with vendor Databricks routes if your employer names a specific credential.

Common traps

  • Confusing Unity Catalog governance with only workspace administration.
  • Debugging transformation code before checking source access or schema drift.
  • Using all-purpose interactive compute for every production job.
  • Treating medallion layers as labels instead of quality and usability boundaries.
  • Ignoring lineage and downstream users before changing curated tables.
  • Storing credentials in notebooks or job parameters.

Practice strategy

Use the DP-750 page to classify misses by setup, governance, processing, pipeline, or maintenance. If governance misses dominate, drill Unity Catalog and permissions. If pipeline misses dominate, drill scheduling, monitoring, dependencies, and job failure signals.

Official source

Revised on Monday, May 25, 2026