#DP 750

SQL School’s DP-750 Course covers Azure Databricks fundamentals, Unity Catalog, data ingestion and transformation with SQL & Python, Lakeflow orchestration, Git & CI/CD practices, and performance tuning. Gain hands-on experience with real-world data engineering workflows and prepare confidently for the Microsoft DP-750 certification exam.

Work on industry-focused projects that simulate real business scenarios and strengthen your practical skills. Learn from experienced trainers and build the expertise needed for in-demand Azure Data Engineering roles.

Training Highlights

✅ Cloud ETL, DWH with Big Data Analytics
✅ OneLake & Lakehouse for Unified Storage
✅ Dataflows Gen2, Self-Service Data Prep
✅ Delta Lake, Delta Tables with Big Data
✅ ETL Notebooks with PySpark, TSQL
✅ Realtme IoT with Eventstreams
✅ CI/CD with Fabric Git Integrations
✅ End-to-End Capstone Project on Banking

DP 750 Course Content

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DP 750 Detailed Course Curriculum

Ch 1: Databricks Introduction

Cloud ETL, DWH
Cloud Computing
Databricks Concepts
Big Data in Cloud

Ch 2: Databricks Architecture

Unity Catalog, Volume
Spark Clusters
Apache Spark and Databricks
Apache Spark Ecosystem
Compute Operations
Hadoop, MapReduce, Apache Spark

Ch 3: Unity Catalog

Unity Catalog Concepts
Workspace Objects
Databricks Notebooks
Databricks Workspace UI
Organizing Workspace Objects
Creating Volumes
Spark Table Creations
UI Limitations

Ch 4: Unity Catalog Operations, Spark SQL – 1

Spark SQL Notebooks
Creating Catalog
Creating Schemas, Tables
Spark Data Types
Data Partitioning
Managed Tables
SQL Queries with the PySpark API
Union, Views in Spark
Dropping Objects
External Tables, External Volumes
SQL Notebooks: Exports, Clone

Ch 5: Spark SQL Notebooks – 2

Math, Sort Functions
String, Date Time Functions
Conditional Statements
SQL Expressions with expr()
Volume for our Data Assets
File Formats, Schema Inference
Spark SQL Aggregations

Ch 6: Python Concepts – 1

Python Introduction
Python Versions
Python Implementations
Python in Spark (PySpark)
Python Print ()
Single, Multiline Statements

Ch 7: Python Concepts – 2

Python Data Types
Integer / Int Data Types
Float, String Data Types
Arithmetic, Assignment Ops
Comparison Operators
Operator Precedence
If … Else Statement
Shorthand If, OR, AND
ELIF and ELSE IF Statements

Ch 8: Python Concepts – 3

Python Lists
List Items, Indexes
Python Dictionaries
Tables Versus Dictionaries
Python Modules & Pandas
Import pandas DataFrame
Pandas Series, arrays
Indexes, Indexed Lists

Ch 9: PySpark – 1

DataFrames with SQL DB
Pandas DataFrame
Dataframe()
List Values, Mixed Values
spark.read.csv()
spark.read.format()
Filtering DataFrames
Grouping your DataFrame

Ch 10: PySpark – 2

DataFrameReader
DataFrameWriter Methods
CSV Data into a DataFrame
Reading Single Files
Reading Multiple Files
Schema with an SQL String
Schema Programmatically

Ch 11: PySpark Transformations – 1

Data Preparation
Selecting Columns
Column Transformations
Renaming Columns
Changing Data Types
select() and selectExpr()
Column Transformations
withColumn()

Ch 12: PySpark Transformations – 2

String Functions
Datetime Conversions
Date and Time Functions
Joining DataFrames
Union DataFrames
Joining DataFrames

Ch 13: PySpark Transformations – 3

Filtering DataFrame Records
Removing Duplicate Records
Sorting and Limiting Records
Filtering Null Values
Grouping and Aggregating
Pivoting and Unpivoting
Conditional Expressions

Ch 14: Medallion Architecture

Medallion Architecture
Aggregated Data Loads
Broze, Silver and Gold
Temp Views
Spark Tables (Parquet)
Work with File, Table Sources

Ch 15: Delta Lake – 1

Storage Layer
Delta Table API
Deleting Records
Updating Records
Merging Records
History & Time Travel

Ch 16: Delta Lake – 2 (SCD)

Schema Evolution
Delta Lake Data Files
Deleting and Updating Records
Merge Into
Incremental Loads
Old History Retention
Delta Transaction Log

Ch 17: Widgets

Text Widgets
User Parameters
Manual Executions
Lake Bridge
Databricks Bridge One

Ch 18: Lake Flow Jobs

Worksflows & CRON
Job Compute, Running Tasks
Python Script Tasks
Parameters
Concurrent Executions
Dependencies
Branching Control

Ch 19: Databricks Tuning

Lazy Evaluation
Explain Plan
Caching, Data Shuffling
Broadcast Joins
When to Partition
Data Skipping
Z Ordering
Liquid Clustering
Spark Configurations

Ch 20: Version Control & GitHub

Local Development
Runtime Compatibility
Git and GitHub Pre-requisites
Git and GitHub Basics
Linking to GitHub, Databricks
Databricks Git Folders
Project Code to GitHub
Databricks Job Updates, Runs

Ch 21: Spark Structured Streaming

Streaming Simulator Notebook
Micro-batch Size
Schema Inference and Evolution
Time Aggregations, Watermarking
Writing Streams
Trigger Intervals
Delta Table Stream Reads, Writes

Ch 22: Auto Loader

Reading Streams with Auto Loader
Reading a Data Stream
Manually Cancel your Data Streams
Writing to a Data Stream
Workspace Modules

Ch 23: Lake Flow Declarative Pipelines

Delta LIVE Tables
Data Generator Notebook
Pipeline Clusters
Databricks CLI
Data Quality Checks
Streaming Dataset “Simulator”
Streaming Live Tables

Ch 24: Security: ACLs

Overview of ACLs
Workspace Users, Groups
Workspace Access Control
Cluster Access Control
Groups & LakeBridge

Ch 25: Azure Databricks – 1

Azure Account
Azure Subscription
Azure Resource Group
Azure Databricks
Azure Regions
Azure IAM & Microsoft Entra
Azure Databricks Deployment

Ch 26: Azure Databricks – 2

Classic Compute
Serverless Compute
Notebooks & Jobs
DBFS & Alternatives
External Tables
Photon Acceleration
DBU & Pricing
Unity Catalog Features

Ch 27: Realtime Project Ecommerce / Banking / Sales

Detailed Project Requirements
Project Solutions
Project FAQs
Project Flow
Interview Questions & Answers
Resume Guidance (1:1)

DP-750: Implementing Data Engineering Solutions Using Azure Databricks

Certification Dumps
Sample Certifications

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What prerequisites do I need before attempting DP-750?

There are no formal prerequisites, but you should have hands-on experience with Azure Databricks, solid proficiency in SQL and Python, familiarity with Unity Catalog, and working knowledge of Git and SDLC practices. Experience with Azure Data Factory, Microsoft Entra, and Azure Monitor is also beneficial.

What is the passing score for DP-750?

A minimum score of 700 out of 1000 is required to pass the exam. Score reports are provided immediately upon completion at a Pearson VUE testing centre or via online proctoring.

How long is the certification valid, and how do I renew it?

Microsoft associate certifications are valid for one year. You can renew for free by passing an online renewal assessment on Microsoft Learn before the expiry date — no need to retake the full proctored exam.

Which Azure services appear in the exam?

The exam primarily covers Azure Databricks and Unity Catalog. Supporting services include Azure Data Factory (for ingestion), Microsoft Entra (for authentication and identity), Azure Event Hubs (for streaming ingestion), and Azure Monitor with Log Analytics (for observability and alerting).

Is there a free practice assessment available?

Yes. Microsoft provides a free practice assessment on Microsoft Learn that mirrors the exam format. It is a great way to identify knowledge gaps before sitting the real exam. You can access it from the official DP-750 certification page.

What topics carry the most weight in the exam?

The two heaviest domains — each at 30–35% — are “Prepare and process data” and “Deploy and maintain data pipelines and workloads”. Together they account for up to 70% of the exam. Focus especially on Lakeflow Jobs, Lakeflow Spark Declarative Pipelines, Delta Lake optimisation, and workload monitoring.