DP-750 Implementing Data Engineering Solutions Using Azure Databricks Exam Topics and Questions

These Microsoft Implementing Data Engineering Solutions Using Azure Databricks (DP-750) exam topics are organized according to official exam domains to help candidates quickly verify coverage and focus on assessment rather than theory. Each domain is paired with topic-wise DP-750 sample questions that reflect how objectives are tested in the actual exam. This structure enables efficient review, targeted self-assessment, and rapid identification of weak areas when preparing for the Microsoft Implementing Data Engineering Solutions Using Azure Databricks certification exam.

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Follows Microsoft's official outline Updated 09 Sep, 2026 4 Topics

Reviewed by Emma Thompson, Microsoft DP-750 Certified Professional

Topic Content

Set up and configure an Azure Databricks environment by selecting and configuring compute in a workspace. Choose an appropriate compute type, including job compute, serverless, warehouse, classic compute, and shared compute based on workload requirements. Configure compute performance settings such as CPU, node count, autoscaling, termination policies, node type, cluster size, and pooling to optimize resource utilization. Configure compute feature settings including Photon acceleration, Azure Databricks runtimeSpark version, and machine learning capabilities to enhance performance. Install and manage libraries for... See More

Sample Questions for Topic 1 : Set up and configure an Azure Databricks environment

You have an Azure Databricks workspace that is enabled for Unity Catalog.

You need to profile a table to meet the following requirements:

The count of null values per column must be evaluated repeatedly as new records are added to the table.

Changes in the count of null values must be observable over the progression of the dataset.

Which type of profile should you create?

snapshot inference time series

Topic Content

Secure and govern Unity Catalog objects by granting privileges to principals including users, service principals, and groups for securable objects, while implementing table-level, column-level, and row-level security controls. Manage authentication through Azure Key Vault secrets, service principals for data access, and managed identities for resource access. Establish governance through creating and maintaining comprehensive table and column definitions with descriptions to support data discovery, configuring attribute-based access control using tags and policies, and applying row filters and column masks for granular... See More

Sample Questions for Topic 2 : Secure and govern Unity Catalog objects

You need to deploy Declarative Automation Bundles to a development environment. The solution must support automated and repeatable deployments across environments.

What should you use?

the Databricks CLI the Databricks SDK for Python Git folders the Jobs UI

Topic Content

Design and implement data modeling in Unity Catalog by establishing logic for data ingestion and source configuration with appropriate extraction and file types, selecting suitable ingestion tools such as Lakeflow Connect, notebooks, or Azure Data Factory, and determining optimal loading methods between batch and streaming approaches. Choose appropriate data table formats including Parquet, Delta, CSV, JSON, or Iceberg, design partitioning schemes and slowly changing dimension types based on requirements, and implement temporal history tables to track changes over time. Establish... Design and implement data modeling in Unity Catalog by establishing logic for data ingestion and source configuration with appropriate extraction and file types, selecting suitable ingestion tools such as Lakeflow Connect, notebooks, or Azure Data Factory, and determining optimal loading methods between batch and streaming approaches. Choose appropriate data table formats including Parquet, Delta, CSV, JSON, or Iceberg, design partitioning schemes and slowly changing dimension types based on requirements, and implement temporal history tables to track changes over time. Establish clustering strategies using liquid clustering, Z-ordering, and deletion vectors, and decide between managed and unmanaged tables to align with organizational needs and data governance requirements. Ingest data into Unity Catalog through multiple methods including Lakeflow Connect for batch and streaming operations, notebooks for flexible data loading, and SQL methods such as CREATE TABLE AS SELECT, CREATE OR REPLACE TABLE, and COPY INTO statements. Utilize change data capture feeds for incremental updates, implement Spark Structured Streaming for real-time data processing, and ingest streaming data from Azure Event Hubs. Leverage Lakeflow Spark Declarative Pipelines with Auto Loader capabilities to automate and simplify the data ingestion process while maintaining data integrity and consistency. Cleanse, transform, and load data into Unity Catalog by profiling data to generate summary statistics and assess distributions, selecting appropriate column data types, and identifying and resolving duplicate, missing, and null values through systematic validation. Apply transformation techniques including filtering, grouping, aggregating, and using join, union, intersect, and except operators to restructure data, while denormalizing, pivoting, and unpivoting data as needed. Execute load operations using merge, insert, and append methods to efficiently populate Unity Catalog tables while maintaining data quality and consistency. Implement and manage data quality constraints in Unity Catalog by establishing validation checks for nullability, data cardinality, and range checking to ensure data accuracy and completeness. Implement data type checks and schema enforcement mechanisms while managing schema drift to accommodate evolving data structures, and utilize pipeline expectations within Lakeflow Spark Declarative Pipelines to automate quality monitoring and maintain consistent data standards throughout the ingestion and transformation processes. See More

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Topic Content

Design and implement data pipelines by establishing the correct order of operations, selecting between notebook and Lakehouse Spark Declarative Pipelines based on requirements, and designing task logic for Lakehouse Jobs. Implement comprehensive error handling across data pipelines, notebooks, and jobs, then create pipelines using notebooks with precedence constraints or Lakehouse Spark Declarative Pipelines. Configure Lakehouse Jobs by setting up and configuring jobs, establishing job triggers and schedules, configuring alerts, and enabling automatic restarts for jobs and data pipelines. Apply development... See More

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DP-750 Implementing Data Engineering Solutions Using Azure Databricks Exam Topics and Questions

Let's Practice Free Microsoft DP-750 Questions Aligned with Official Exam Topics

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