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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Prepare and process data	30–35%	- Ingest and transform data 1. Ingest batch and streaming data from multiple sources 2. Transform using Spark SQL, PySpark, Scala, and Delta Lake 3. Implement schema enforcement, schema drift, and slowly changing dimensions - Optimize and manage data storage 1. Implement lakehouse architecture and manage table versions 2. Handle structured, semi-structured, and unstructured data 3. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
Set up and configure an Azure Databricks environment	15–20%	- Select and configure compute resources 1. Manage workspace settings, permissions, and networking 2. Configure cluster policies, instance pools, and libraries 3. Choose compute types: serverless, job compute, SQL warehouse, classic compute - Integrate with Azure services 1. Configure monitoring with Azure Monitor and diagnostic settings 2. Connect to Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID
Deploy and maintain data pipelines and workloads	30–35%	- Build and orchestrate pipelines 1. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs 2. Configure Lakeflow Jobs: schedules, triggers, alerts, retries 3. Design and implement Lakeflow Spark Declarative Pipelines - Monitor, troubleshoot, and maintain workloads 1. Monitor performance, logs, and execution metrics 2. Troubleshoot failures, repair and restart jobs 3. Apply SDLC practices and version control
Secure and govern Unity Catalog objects	15–20%	- Implement data governance and security 1. Manage catalogs, schemas, tables, views, and volumes 2. Configure access control: row-level, column-level, attribute-based security 3. Enforce data quality, lineage, and auditing - Manage data sharing and permissions 1. Grant and revoke permissions, manage groups and service principals 2. Set up external locations and storage credentials

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data retention solution that meets the following requirements:
Deleted data must be retained for 30 days to support audits.
Deleted data that is older than 30 days must be removed permanently.
The solution must minimize administrative effort.
Which two properties should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) delta.logRetentionDuration
B) delta.timeUntilArchived
C) delta.deletedFileRetentionDuration
D) delta.autoOptimize.autoCompact
E) delta.enableDeletionVectors

2. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have 500 GB of sales data stored as multiple CSV files in cloud storage.
You plan to load the data into a Delta table.
You need to ingest the bulk data by using a solution that meets the following requirements:
* Minimize how long it takes to implement the solution.
* Minimize the amount of custom code required.
What should you use?

A) Auto Loader
B) Apache Spark read APIs
C) file upload
D) COPY INTO

3. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a daily batch data process that requires complex and highly customized Python transformations. The solution must minimize additional complexity.
What should you include in the solution?

A) a Lakeflow Spark Declarative Pipelines (SDP) pipeline
B) a Databricks notebook triggered by a job
C) a continuous job in Lakeflow Jobs
D) an Azure Data Factory pipeline that has data flows

4. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.
Table1 stores customer profile data.
Business users must analyze how customer profile records change over time. They must also be able to query earlier versions of the table.
You need to implement a solution that:
* Maintains persistent historical versions of customer profile records for long-term analysis.
* Allows users to query earlier versions of the Delta table.
* Minimizes maintenance effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

5. You have an Azure Databricks workspace.
You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming The solution must authenticate to Event Hubs and read the event payload.
How should you complete the PySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.