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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📄 Exam Contains: 4 Topics

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

Q1 You need to configure compute resources for a mixed workload environment that includes interactive SQL queries, batch ETL jobs, and real-time streaming analytics. The organization wants to optimize performance and control costs. Which combination of compute types and configurations would be most effective?

Use a single classic compute cluster with high CPU and memory for all workloads, with Photon acceleration enabled Use warehouse compute for SQL queries, job compute for ETL with autoscaling, and serverless compute for streaming with machine learning runtime Use shared compute for all workloads with pooling enabled to reduce startup time and costs Use only serverless compute for all workloads with AI/BI Genie enabled for query optimization

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

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

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

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