Microsoft DP-750 Exam : Implementing Data Engineering Solutions Using Azure Databricks

DP-750
  • Exam Code: DP-750
  • Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
  • Updated: Sep 07, 2026
  • Q & A: 93 Questions and Answers

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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Deploy and maintain data pipelines and workloads30–35%- Monitor, troubleshoot, and maintain workloads
  • 1. Monitor performance, logs, and execution metrics
  • 2. Apply SDLC practices and version control
  • 3. Troubleshoot failures, repair and restart jobs
- 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
Set up and configure an Azure Databricks environment15–20%- Select and configure compute resources
  • 1. Configure cluster policies, instance pools, and libraries
  • 2. Manage workspace settings, permissions, and networking
  • 3. Choose compute types: serverless, job compute, SQL warehouse, classic compute
- Integrate with Azure services
  • 1. Connect to Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID
  • 2. Configure monitoring with Azure Monitor and diagnostic settings
Secure and govern Unity Catalog objects15–20%- Implement data governance and security
  • 1. Enforce data quality, lineage, and auditing
  • 2. Configure access control: row-level, column-level, attribute-based security
  • 3. Manage catalogs, schemas, tables, views, and volumes
- Manage data sharing and permissions
  • 1. Grant and revoke permissions, manage groups and service principals
  • 2. Set up external locations and storage credentials
Prepare and process data30–35%- Optimize and manage data storage
  • 1. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
  • 2. Implement lakehouse architecture and manage table versions
  • 3. Handle structured, semi-structured, and unstructured data
- Ingest and transform data
  • 1. Transform using Spark SQL, PySpark, Scala, and Delta Lake
  • 2. Implement schema enforcement, schema drift, and slowly changing dimensions
  • 3. Ingest batch and streaming data from multiple sources

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

Question 1

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two managed Delta tables named sales.schema1.table1 and sales.schema1.table2.
sales.schema1.table1 contains sales data from the current year.
sales.schema1.table2 contains historical data.
You need to load all the rows from sales.schema1.table1 into sales.schema1.table2. The solution must preserve any existing data in sales.schema1.table2 and minimize processing effort.
Which command should you run?

A. INSERT INTO sales.schema1.table2 SELECT * FROM sales.schema1.table1;
B. CREATE TABLE sales.schema1.table2 AS SELECT * FROM sales.schema1.table1;
C. CREATE OR REPLACE TABLE sales.schema1.table2 AS SELECT * FROM sales.schema1.table1;
D. INSERT OVERWRITE sales.schema1.table2 SELECT * FROM sales.schema1.table1;


Question 2

You need to configure resiliency for a job in Lakeflow Jobs named Job1 to meet the pipeline deployment and operation requirements.
What should you do?

A. Enable task-level retries on the ingestion task and leave downstream tasks unchanged.
B. Configure Job1 to start always from the first task when any task fails.
C. Enable a restart upon a job failure and set the retry count to 0.
D. Disable retries and configure Job1 to run manually only.


Question 3

You have an Azure Databricks workspace that uses Databricks SQL.
You have a table named sales_goals_source that contains the following columns:
* Salesperson
* Item
* 2019
* 2020
* 2021
You need to transform the year columns into rows and return the columns Salesperson, Item, Year, and Value.
How should you complete the SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
SELECT Salesperson, Item, Year, Value
FROM sales_goals_source
UNPIVOT
(
Value FOR [first dropdown] IN [second dropdown]
);


Question 4

You have an Azure Databricks workspace.
You have an Azure key vault named kv-secure that stores a secret named storageKey. The value of storageKey is managed and updated by the cloud security team at your company.
You need to enable a Databricks notebook named Notebook 1 to retrieve the value of storageKey securely at runtime. The solution must follow the principle of least privilege and always retrieve the latest value.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Question 5

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.dropna(subset=[ " order_amount " ])
Does this meet the goal?

A. No
B. Yes


Solutions:

Question 1
Answer: A
Question 2
Answer: A
Question 3
Answer: Only visible for members
Question 4
Answer: Only visible for members
Question 5
Answer: B

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