Exam DP-750: Implementing Data Engineering Solutions Using Azure Databricks (Video)

Description

Pass the DP750 and master data engineering with hands-on Azure Databricks projects.

The DP-750 exam validates the modern Azure Databricks toolkit: Lakeflow Spark Declarative Pipelines for low-code pipeline authoring, Lakeflow Connect for managed ingestion, Lakeflow Jobs for orchestration, Unity Catalog for fine-grained access control and lineage, AI/BI Genie for natural-language data discovery, Databricks Asset Bundles for CI/CD, and Photon for query acceleration. Newer features the exam emphasizes--liquid clustering, attribute-based access control, deletion vectors, and structured streaming with Auto Loader--reflect where production data engineering is heading. This certification matters because organizations are consolidating fragmented data stacks onto governed lakehouses, and Microsoft is signaling that Azure Databricks fluency is now a distinct, certifiable specialty alongside Fabric and Synapse skills.

This course covers all four skill domains measured on the exam in five lessons of approximately 60 to 75 minutes each. Each lesson is organized around a coherent learning chunk--workspace foundations, Unity Catalog governance, lakehouse data design, ingestion and transformation, production operations--rather than as a feature inventory. Concept-first framing on slides establishes the mental models for the exam tests, and hands-on demonstrations in the Azure Databricks workspace reinforce those concepts in working code. Every named skill in the official Microsoft Learn study guide is addressed; the coverage map is documented in a companion internal reference.

Learners finish the course with the conceptual grounding to reason through scenario-based exam questions and the platform of fluency to execute on the job.

The course is structured around five learning chunks rather than as a feature inventory. The Foundations: Workspaces, Compute, and Notebooks lesson establishes platform foundations. The Unity Catalog: Structure, Security, and Governance lesson covers Unity Catalog as the governance fabric every later lesson assumes. The Designing Data for the Lakehouse lesson develops the data design principles that shape every pipeline. The Ingesting and Transforming Data lesson is the largest lesson, covering ingestion, transformation, and quality--matching the largest exam domain. The Production Pipelines and Operations lesson closes the loop with production operations: orchestration, deployment, monitoring, and optimization. Demonstrations build cumulatively across lessons, so by the final lesson the learner is troubleshooting a pipeline they themselves built earlier.

Skill Level:

• Intermediate

Learn How To:

• Provision and configure an Azure Databricks workspace, choose appropriate compute for the task at hand, and work fluently in notebooks across SQL and Python
• Design and build the Unity Catalog object model--catalogs, schemas, volumes, tables, views, materialized views, foreign catalogs--with naming and isolation patterns that survive contact with production
• Secure and govern data using the full Unity Catalog toolkit: privilege grants, row filters, column masks, attribute-based access control, service principals, managed identities, Key Vault-backed secrets, lineage tracking, audit logs, retention policies, and Delta Sharing
• Reason about lakehouse data design--Delta Lake fundamentals, file formats, partitioning, liquid clustering, slowly changing dimensions, temporal tables, and the medallion architecture--as the conceptual backbone of every pipeline
• Ingest data through every supported path: Lakeflow Connect, notebook-based ingestion, SQL methods, change data capture, Spark Structured Streaming, Azure Event Hubs, and Auto Loader
• Cleanse, profile, and transform data using the full transformation toolkit, then enforce quality with validation checks, schema management, and pipeline expectations
• Build and ship production pipelines using Lakeflow Spark Declarative Pipelines and Lakeflow Jobs, with proper Git workflow, a complete testing strategy, and Asset Bundles for deployment via CLI or REST API
• Monitor, troubleshoot, and optimize workloads using the Spark UI, DAG analysis, OPTIMIZE/VACUUM, and Azure Monitor with Log Analytics
• Approach the DP-750 exam with the conceptual reasoning skills its scenario-based question format demands

Course requirement:

Pre-requisites:

• Working SQL skills (joins, aggregations, window functions)
• Basic Python (functions, dictionaries, list comprehensions)
• Familiarity with cloud computing concepts (storage, compute, identity)
• Basic Git usage (clone, commit, branch)
• Helpful but not required: prior exposure to Apache Spark, an Azure subscription for hands-on practice, prior experience with another DP-series exam

Who Should Take This Course:

Job titles:

• Azure Data Engineer
• Data Engineer (Azure Databricks)
• Senior Azure / Cloud Data Engineer
• Data Platform Engineer (Azure-focused)

About Pearson Video Training:

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Sample Content

Table of Contents

Introduction

Foundations: Workspaces, Compute, and Notebooks

                Course introduction and the DP-750 landscape

                The lakehouse pattern and why Azure Databricks exists

                Provisioning and navigating the workspace

                Configuring compute for the job at hand

                Working with notebooks across languages

Unity Catalog: Structure, Security, and Governance

                The Unity Catalog object model

                Creating and organizing catalog objects

                Permissions and fine-grained access control

                Identity, secrets, and authentication patterns

                Lineage, audit, and data discovery

                Sharing data securely

Designing Data for the Lakehouse

                Delta Lake and table design

                Partitioning, clustering, and storage optimization

                Change-tracking patterns: SCD and temporal tables

                The medallion architecture

Ingesting and Transforming Data

                Batch ingestion patterns

                Streaming ingestion patterns

                Cleansing, profiling, and core transformations

                Joins, set operations, and reshaping

                Loading data: merge, insert, append

                Data quality enforcement

                Spark optimization fundamentals

Production Pipelines and Operations

                Pipeline design: notebooks, declarative pipelines, and Lakeflow Jobs

                Orchestration: triggers, schedules, and error handling

                Git workflow and the testing strategy

                Asset Bundles and deployment

                Monitoring, troubleshooting, and cost management

                Performance: reading the Spark UI

Summary

Updates

Submit Errata