Microsoft · Associate
Microsoft Data Engineering with Azure Databricks (DP-750)
Implement data engineering solutions on Azure Databricks with Unity Catalog and Lakeflow.
DP-750 exam at a glance
| Exam code | DP-750 |
|---|---|
| Vendor | Microsoft |
| Level | Associate |
| Domains | 4 |
| Published practice questions | 229 |
| Our mock exam | 55 questions in 100 minutes |
| Our practice pass target | 70% |
| Checked against the exam guide | September 22, 2026 |
| Official exam page | Microsoft — DP-750 |
The mock exam length, time limit and pass target are our practice settings, not official exam scores or a guarantee of passing. Check the official DP-750 exam page for the current format, price and scoring before you book.
DP-750 exam domains
Weights show our practice allocation, built from the official exam guide; official blueprints may give ranges. Each domain links to its free sample questions (with explanations) and study guide preview once they are published.
Set up and configure an Azure Databricks environment
17%Select and configure compute and create/organize Unity Catalog objects.
- Compute types: job, serverless, warehouse, classic, shared
- Compute performance settings: autoscaling, termination, node types, pooling
- Photon acceleration, runtime versions, ML runtimes
- Library installation and compute access permissions
- Unity Catalog naming conventions and environment isolation
- Catalogs, schemas, volumes, tables, views, materialized views
- Foreign catalogs via connections
- DDL on managed vs external tables; AI/BI Genie instructions
Free Set up and configure an Azure Databricks environment practice questions →Set up and configure an Azure Databricks environment study guide →
Secure and govern Unity Catalog objects
17%Secure data access and govern Unity Catalog objects.
- Privileges for users, service principals, groups on securables
- Table/column-level access control and row-level security
- Azure Key Vault secrets from Databricks; service principals; managed identities
- Table/column definitions and descriptions for discovery
- Attribute-based access control (ABAC) with tags and policies
- Row filters and column masks; data retention policies
- Lineage tracking via Catalog Explorer; audit logging
- Secure Delta Sharing strategy
Free Secure and govern Unity Catalog objects practice questions →Secure and govern Unity Catalog objects study guide →
Prepare and process data
33%Model, ingest, cleanse, transform, and quality-check data in Unity Catalog.
- Ingestion design: extraction types, file types, Lakeflow Connect vs notebooks vs ADF
- Batch vs streaming loading; table formats (Delta, Parquet, Iceberg, CSV, JSON)
- Partitioning schemes; SCD types; temporal history tables
- Clustering: liquid clustering, Z-ordering, deletion vectors
- Managed vs unmanaged tables
- CTAS, COPY INTO, CDC feeds, Spark Structured Streaming, Event Hubs
- Lakeflow Spark Declarative Pipelines with Auto Loader
- Profiling, type choices, dedupe/null handling, joins/unions, pivot/unpivot, merge/insert/append
- Validation checks, schema enforcement and drift, pipeline expectations
Free Prepare and process data practice questions →Prepare and process data study guide →
Deploy and maintain data pipelines and workloads
33%Design pipelines, implement Lakeflow Jobs, run the dev lifecycle, and optimize workloads.
- Pipeline order of operations; notebooks vs Declarative Pipelines
- Lakeflow Jobs: task logic, triggers, schedules, alerts, automatic restarts
- Error handling and precedence constraints
- Git best practices: branching, PRs, conflict resolution
- Testing strategy: unit, integration, end-to-end, UAT
- Databricks Asset Bundles: configuration and deployment (CLI, REST)
- Cluster consumption monitoring; job repair/restart
- Spark troubleshooting: DAG, Spark UI, query profile; caching, skew, spill, shuffle
- Delta optimization: OPTIMIZE, VACUUM; Azure Monitor log streaming and alerts
Free Deploy and maintain data pipelines and workloads practice questions →Deploy and maintain data pipelines and workloads study guide →
DP-750 FAQ
How is the Data Engineering with Azure Databricks (DP-750) practice assessment configured?
Our practice format targets 55 questions in 100 minutes. Availability depends on the published question bank and is shown on this course page. Official exam question counts and formats may differ.
What does the 70% practice target mean?
Microsoft technical exams use a scaled passing score of 700; this is not equivalent to 70% correct. DataCertPrep uses 70% as an internal practice target, not a prediction or guarantee of passing.
How should I assess my progress in Data Engineering with Azure Databricks (DP-750)?
Use practice accuracy to find weak topics. Repeating familiar questions can inflate your score. Check coverage of the current objectives, practise hands-on tasks, and assess yourself on unseen questions.
What is the best way to study Data Engineering with Azure Databricks (DP-750)?
Read the available guides, work through practice explanations, use flashcards and complete practical exercises. Take a timed assessment when available. Check the provider's current exam guide before booking your exam.
Should I use DP-750 exam dumps?
No. "Dumps" are questions copied or recalled from the live exam. Certification exams are covered by a candidate agreement that prohibits sharing or using them, and breaking it can lead to a revoked certification or a ban from future exams. Dumps are also often wrong, and they go out of date when the exam blueprint changes. Memorising answers does not build the skills the exam tests. Study the official Microsoft exam guide and practise with original questions that explain each answer, such as the free sample questions linked on this page.
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