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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
| 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. Design and implement Lakeflow Spark Declarative Pipelines
- 3. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
- Monitor, troubleshoot, and maintain workloads
- 1. Troubleshoot failures, repair and restart jobs
- 2. Monitor performance, logs, and execution metrics
- 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. Enforce data quality, lineage, and auditing
- 3. Configure access control: row-level, column-level, attribute-based security
- Manage data sharing and permissions
- 1. Set up external locations and storage credentials
- 2. Grant and revoke permissions, manage groups and service principals
|
| 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. Connect to Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID
- 2. Configure monitoring with Azure Monitor and diagnostic settings
|
| Prepare and process data | 30–35% | - 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. Implement schema enforcement, schema drift, and slowly changing dimensions
- 2. Ingest batch and streaming data from multiple sources
- 3. Transform using Spark SQL, PySpark, Scala, and Delta Lake
|
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 a Delta table named db1.sales_orders.
dbl sales_orders is updated nightly and has change data feed (CDF) enabled.
You need to ingest all the changes from the dbl.sales.ordets table, including inserts, updates, and deletes, into a downstream pipeline.
How should you complete the PsySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 2
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to share curated data with an external organization. The solution must meet the following requirements:
* The organization will use its own compute platform to query the data.
* Access to the data must be centrally governed by using Unity Catalog.
* Administrative effort must be minimized.
What should you do?
A. Create a pipeline to move the data to an SFTP server.
B. Create a SQL warehouse for external users.
C. Create a Lakeflow Connect pipeline.
D. Grant workspace access to external users.
E. Use Delta Sharing.
Question 3
You have an Azure Databricks workspace that contains a Delta table named Customer.
A job named Job1 performs frequent upserts into Customer.
You discover that Job1 has created many small Parquet files in Customer, and the small files are degrading query performance.
You need to improve query performance for the current data already stored in Customer. The solution must not affect the travel for the Customer table.
What should you do?
A. Set the delta.deletedFileRetentionDuration table property to 1 day.
B. Run the OPTIMIZE command on the Customer table.
C. Set the delta.autoOptimize.optimizeWrite Apache Spark configuration to true.
D. Run the VACUUM command on the Customer table.
Question 4
You have an Azure Databricks workspace named Workspace1.
You create a compute cluster named Cluser1 that will be used to ingest data.
You need to install the required libraries on Cluster1. The solution must use Unity Catalog for access control.
What should you do?
A. Upload the libraries to a Unity Catalog schema and install the libraries on Cluster1.
B. Install the libraries by using pip from a notebook attached to Cluster1.
C. Install the libraries on Cluster1 and manually restart the cluster.
D. Upload the libraries to a Unity Catalog volume and install the libraries on Cluster1.
Question 5
You have an Azure Databricks workspace named Workspace1. You create a compute cluster named Cluser1 that will be used to ingest data.
You need to install the required libraries on Cluster 1. The solution must use Unity Catalog for access control.
What should you do?
A. Create a custom dependency management script and run the script from a Databricks notebook.
B. Upload the libraries to Workspace1 and install the libraries on Cluster1.
C. Install the libraries on Cluster1 and manually restart the cluster.
D. Install the libraries by using pip3.
Solutions:
Question 1 Answer: Only visible for members | Question 2 Answer: E | Question 3 Answer: B | Question 4 Answer: D | Question 5 Answer: D |