DP-750 Preparation Details
Preparing for the DP-750 Implementing Data Engineering Solutions Using Azure Databricks certification exam? Start here with a complete, objective-wise DP-750 study guide designed to help you pass faster.
This guide brings together official Microsoft documentation, key concepts, and curated resources for every DP-750 exam objective, making it ideal for both beginners and last-minute revision.
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DP-750 Copilot Materials
| Udemy | Azure Databricks Data Engineer Associate Exam Prep |
| Coursera | Mastering Azure Databricks for Data Engineers Specialization |
Set up and configure an Azure Databricks environment (15–20%)
Select and configure compute in a workspace
Choose an appropriate compute type, including job compute, serverless, warehouse, classic compute, and shared compute
Compute selection recommendations – Azure Databricks
SQL warehouse types – Azure Databricks
Configure compute performance settings, including CPU, node count, autoscaling, termination, node type, cluster size, and pooling
Compute configuration reference – Azure Databricks
Compute configuration recommendations – Azure Databricks
Phase 8: Design compute configuration – Azure Databricks
Configure compute feature settings, including Photon acceleration, Azure Databricks runtime/Spark version, and machine learning
What is Photon? – Azure Databricks
Databricks Runtime for Machine Learning – Azure Databricks
Best practices for configuring classic Lakeflow Jobs – Azure Databricks
Install libraries for a compute resource
Install libraries – Azure Databricks
Compute-scoped libraries – Azure Databricks
Notebook-scoped Python libraries – Azure Databricks
Configure access permissions to a compute resource
Classic compute overview – Azure Databricks
Manage classic compute – Azure Databricks
Create and organize objects in Unity Catalog
Apply naming conventions based on requirements, including isolation, development environment, and external sharing
What are catalogs in Azure Databricks?
Unity Catalog best practices – Azure Databricks
Create a catalog based on requirements
Create catalogs – Azure Databricks
What is Unity Catalog? – Azure Databricks
Create a schema based on requirements
Create schemas – Azure Databricks
Create and manage schemas (databases) – Azure Databricks
Create volumes based on requirements
What are Unity Catalog volumes? – Azure Databricks
Create and manage Unity Catalog volumes – Azure Databricks
Create tables, views, and materialized views
Unity Catalog securable objects reference – Azure Databricks
Use materialized views in Databricks SQL – Azure Databricks
Implement a foreign catalog by configuring connections
What is query federation? – Azure Databricks
Manage and work with foreign catalogs – Azure Databricks
What is catalog federation? – Azure Databricks
Implement data definition language (DDL) operations on managed and external tables
What is Unity Catalog? – Azure Databricks
Connect to cloud object storage using Unity Catalog – Azure Databricks
Configure AI/BI Genie instructions for data discovery
What is a Genie Space – Azure Databricks
Create and Organize Objects in Unity Catalog – Training
Secure and govern Unity Catalog objects (15–20%)
Secure Unity Catalog objects
Grant privileges to a principal (user, service principal, or group) for securable objects in Unity Catalog
Manage privileges in Unity Catalog – Azure Databricks
Unity Catalog privileges reference – Azure Databricks
Unity Catalog permissions model concepts – Azure Databricks
Implement table- and column-level access control and row-level security
Access control in Unity Catalog – Azure Databricks
Row filters and column masks – Azure Databricks
Access Azure Key Vault secrets from within Azure Databricks
Secret management – Azure Databricks
Tutorial: Create and use a Databricks secret
Tutorial: Connect to Azure Data Lake Storage – Azure Databricks
Authenticate data access by using service principals
Service principals – Azure Databricks
Run a job with a Microsoft Entra ID service principal – Azure Databricks
Authenticate resource access by using managed identities
Use Azure managed identities in Unity Catalog to access storage – Azure Databricks
Secure and Govern Unity Catalog Objects in Azure Databricks – Training
Govern Unity Catalog objects
Create, implement, and preserve table and column definitions and descriptions for data discovery
Data governance with Azure Databricks
Unity Catalog best practices – Azure Databricks
Configure attribute-based access control (ABAC) by using tags and policies
Unity Catalog attribute-based access control (ABAC) – Azure Databricks
Create and manage attribute-based access control (ABAC) policies – Azure Databricks
Configure row filters and column masks
Row filters and column masks – Azure Databricks
Create and manage attribute-based access control (ABAC) policies – Azure Databricks
Apply data retention policies
Best practices for data and AI governance – Azure Databricks
Data governance with Azure Databricks
Set up and manage data lineage tracking by using Catalog Explorer, including owner, history, dependencies, and lineage
View data lineage using Unity Catalog – Azure Databricks
Manage Unity Catalog object ownership – Azure Databricks
Configure audit logging
Diagnostic log reference – Azure Databricks
Best practices for data and AI governance – Azure Databricks
Design and implement a secure strategy for Delta Sharing
What is Delta Sharing? – Azure Databricks
Set up Delta Sharing for your account (for providers) – Azure Databricks
Prepare and process data (30–35%)
Design and implement data modeling in Unity Catalog
Design logic for data ingestion and data source configuration, including extraction type and file type
What is Lakeflow Connect? – Azure Databricks
Data engineering with Databricks – Azure Databricks
Build ETL pipelines with Azure Databricks and Delta Lake – Azure Architecture Center
Choose an appropriate data ingestion tool, including Lakeflow Connect, notebooks, and Azure Data Factory
Managed connectors in Lakeflow Connect – Azure Databricks
What is Auto Loader? – Azure Databricks
Prepare and Process Data with Azure Databricks – Training
Choose a data loading method, including batch and streaming
Lakeflow Spark Declarative Pipelines concepts – Azure Databricks
Load data in pipelines – Azure Databricks
Choose a data table format, such as Parquet, Delta, CSV, JSON, or Iceberg
Databricks Unity Catalog table types – Azure Databricks
Best practices: Delta Lake – Azure Databricks
Design and implement a data partitioning scheme
When to partition tables on Azure Databricks – Azure Databricks
Use liquid clustering for tables – Azure Databricks
Choose a slowly changing dimension (SCD) type
Change data capture and snapshots – Azure Databricks
The AUTO CDC APIs: Simplify change data capture with pipelines – Azure Databricks
Choose granularity on a column or table based on requirements
Aggregate data on Azure Databricks – Azure Databricks
Best practices: Delta Lake – Azure Databricks
Design and implement a temporal (history) table to record changes over time
Change data capture and snapshots – Azure Databricks
AUTO CDC INTO (pipelines) – Azure Databricks
Design and implement a clustering strategy, including liquid clustering, Z-ordering, and deletion vectors
Use liquid clustering for tables – Azure Databricks
Deletion vectors in Databricks – Azure Databricks
Choose between managed and unmanaged tables
Unity Catalog best practices – Azure Databricks
Ingest data into Unity Catalog
Ingest data by using Lakeflow Connect, including batch and streaming
Managed connectors in Lakeflow Connect – Azure Databricks
Connect to managed ingestion sources – Azure Databricks
Ingest data by using notebooks, including batch and streaming
Data engineering with Databricks – Azure Databricks
Lakeflow Spark Declarative Pipelines concepts – Azure Databricks
Ingest data by using SQL methods, including CTAS, CREATE OR REPLACE TABLE, and COPY INTO
Develop Lakeflow Spark Declarative Pipelines code with SQL – Azure Databricks
Use streaming tables in Databricks SQL – Azure Databricks
Ingest data by using a change data capture (CDC) feed
The AUTO CDC APIs: Simplify change data capture with pipelines – Azure Databricks
Change data capture and snapshots – Azure Databricks
Ingest data by using Spark Structured Streaming
Load data in pipelines – Azure Databricks
Transform data with pipelines – Azure Databricks
Ingest streaming data from Azure Event Hubs
Load data in pipelines – Azure Databricks
Build ETL pipelines with Azure Databricks and Delta Lake – Azure Architecture Center
Ingest data by using Lakeflow Spark Declarative Pipelines, including Auto Loader
What is Auto Loader? – Azure Databricks
Develop Lakeflow Spark Declarative Pipelines – Azure Databricks
Cleanse, transform, and load data into Unity Catalog
Profile data to generate summary statistics and assess data distributions
Data profiling – Azure Databricks
Data profiling metric tables – Azure Databricks
Choose appropriate column data types
Schema enforcement – Azure Databricks
Prepare and Process Data with Azure Databricks – Training
Identify and resolve duplicate, missing, and null values
Prepare and Process Data with Azure Databricks – Training
Manage data quality with pipeline expectations – Azure Databricks
Transform data, including filtering, grouping, and aggregating data
Aggregate data on Azure Databricks – Azure Databricks
Transform data with pipelines – Azure Databricks
Transform data by using join, union, intersect, and except operators
Transform data with pipelines – Azure Databricks
Prepare and Process Data with Azure Databricks – Training
Transform data by denormalizing, pivoting, and unpivoting data
Develop Lakeflow Spark Declarative Pipelines code with SQL – Azure Databricks
Prepare and Process Data with Azure Databricks – Training
Load data by using merge, insert, and append operations
The AUTO CDC APIs: Simplify change data capture with pipelines – Azure Databricks
Implement and manage data quality constraints in Unity Catalog
Implement validation checks, including nullability, data cardinality, and range checking
Manage data quality with pipeline expectations – Azure Databricks
Expectation recommendations and advanced patterns – Azure Databricks
Implement data type checks
Schema enforcement – Azure Databricks
Implement and Manage Data Quality Constraints with Azure Databricks – Training
Implement schema enforcement and manage schema drift
Schema enforcement – Azure Databricks
Best practices for Lakeflow Spark Declarative Pipelines – Azure Databricks
Manage data quality with pipeline expectations in Lakeflow Spark Declarative Pipelines
Manage data quality with pipeline expectations – Azure Databricks
Expectation recommendations and advanced patterns – Azure Databricks
Best practices for Lakeflow Spark Declarative Pipelines – Azure Databricks
Deploy and maintain data pipelines and workloads (30–35%)
Design and implement data pipelines
Design order of operations for a data pipeline
Procedural vs. declarative data processing in Azure Databricks – Azure Databricks
Design and Implement Data Pipelines with Azure Databricks – Training
Choose between notebook and Lakeflow Spark Declarative Pipelines
Procedural vs. declarative data processing in Azure Databricks – Azure Databricks
Choose a development language – Azure Databricks
Design task logic for Lakeflow Jobs
Lakeflow Jobs – Azure Databricks
Control the flow of tasks within Lakeflow Jobs – Azure Databricks
Design and implement error handling in data pipelines, notebooks, and jobs
Control the flow of tasks within Lakeflow Jobs – Azure Databricks
Best practices for Lakeflow Spark Declarative Pipelines – Azure Databricks
Create a data pipeline by using a notebook, including precedence constraints
Configure and edit Lakeflow Jobs – Azure Databricks
Configure and edit tasks in Lakeflow Jobs – Azure Databricks
Create a data pipeline by using Lakeflow Spark Declarative Pipelines
Develop Lakeflow Spark Declarative Pipelines – Azure Databricks
Tutorial: Build an ETL pipeline with Lakeflow Spark Declarative Pipelines – Azure Databricks
Implement Lakeflow Jobs
Create a job, including setup and configuration
Configure and edit Lakeflow Jobs – Azure Databricks
Configure and edit tasks in Lakeflow Jobs – Azure Databricks
Configure job triggers
Automating jobs with schedules and triggers – Azure Databricks
Pipeline task for jobs – Azure Databricks
Schedule a job
Automating jobs with schedules and triggers – Azure Databricks
Configure and edit Lakeflow Jobs – Azure Databricks
Configure alerts for a job
Monitoring and observability for Lakeflow Jobs – Azure Databricks
Configure and edit Lakeflow Jobs – Azure Databricks
Configure automatic restarts for a job or a data pipeline
Control the flow of tasks within Lakeflow Jobs – Azure Databricks
Best practices for Lakeflow Spark Declarative Pipelines – Azure Databricks
Implement development lifecycle processes in Azure Databricks
Apply version control best practices using Git
Best practices and recommended CI/CD workflows on Databricks – Azure Databricks
Implement Development Lifecycle Processes in Azure Databricks – Training
Manage branching, pull requests, and conflict resolution
Best practices and recommended CI/CD workflows on Databricks – Azure Databricks
What are Declarative Automation Bundles? – Azure Databricks
Implement a testing strategy, including unit tests, integration tests, end-to-end tests, and UAT
Best practices and recommended CI/CD workflows on Databricks – Azure Databricks
Declarative Automation Bundles FAQs – Azure Databricks
Configure and package Databricks Asset Bundles
What are Declarative Automation Bundles? – Azure Databricks
Declarative Automation Bundles configuration – Azure Databricks
Bundle configuration examples – Azure Databricks
Deploy a bundle by using the Azure Databricks CLI
bundle command group – Azure Databricks
Develop a job with Declarative Automation Bundles – Azure Databricks
Deploy a bundle by using REST APIs
Declarative Automation Bundles resources – Azure Databricks
Deploy bundles and run workflows from the workspace – Azure Databricks
Monitor, troubleshoot, and optimize workloads in Azure Databricks
Monitor and manage cluster consumption to optimize performance and cost
Best practices for performance efficiency – Azure Databricks
Monitor, Troubleshoot and Optimize Workloads in Azure Databricks – Training
Troubleshoot and repair issues in Lakeflow Jobs, including repair, restart, stop, and run functions
Monitoring and observability for Lakeflow Jobs – Azure Databricks
Configure and edit Lakeflow Jobs – Azure Databricks
Troubleshoot and repair issues in Apache Spark jobs and notebooks, including performance tuning, resolving resource bottlenecks, and cluster restart
Diagnose cost and performance issues using the Spark UI – Azure Databricks
Diagnosing a long job in Spark – Azure Databricks
Debugging with the Spark UI – Azure Databricks
Investigate and resolve caching, skewing, spilling, and shuffle issues by using a DAG, the Spark UI, and query profile
Skew and spill – Azure Databricks
Slow Spark stage with little I/O – Azure Databricks
Phase 9: Design observability strategy – Azure Databricks
Optimize Delta tables for performance and cost, including OPTIMIZE and VACUUM commands
Remove unused data files with vacuum – Azure Databricks
Best practices: Delta Lake – Azure Databricks
Implement log streaming by using Log Analytics in Azure Monitor
Configure diagnostic log delivery – Azure Databricks
Send Azure Databricks application logs to Azure Monitor – Azure Architecture Center
Configure alerts by using Azure Monitor
Supported log categories – Microsoft.Databricks/workspaces – Azure Monitor
Configure diagnostic log delivery – Azure Databricks
This brings us to the end of the DP-750 Implementing Data Engineering Solutions Using Azure Databricks Study Guide.
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