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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
- 1. Choose compute types: serverless, job compute, SQL warehouse, classic compute
- 2. Manage workspace settings, permissions, and networking
- 3. Configure cluster policies, instance pools, and libraries
- 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
|
| Topic 2: Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
- 1. Set up external locations and storage credentials
- 2. Grant and revoke permissions, manage groups and service principals
- Implement data governance and security
- 1. Configure access control: row-level, column-level, attribute-based security
- 2. Manage catalogs, schemas, tables, views, and volumes
- 3. Enforce data quality, lineage, and auditing
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30–35% | - 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
- Build and orchestrate pipelines
- 1. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
- 2. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
- 3. Design and implement Lakeflow Spark Declarative Pipelines
|
| Topic 4: Prepare and process data | 30–35% | - Optimize and manage data storage
- 1. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
- 2. Handle structured, semi-structured, and unstructured data
- 3. Implement lakehouse architecture and manage table versions
- 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
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
1. 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) Apache Spark read APIs
B) COPY INTO
C) file upload
D) Auto Loader
2. You have an Azure Databricks workspace named Workspace1 that contains a lakehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
* The schemas and tables can be queried in Databricks.
* The schemas and tables appear alongside other Unity Catalog objects.
* The data is NOT copied into Databricks-managed storage.
Solution: You create a foreign catalog in Catalog Explorer.
Does this meet the goal?
A) No
B) Yes
3. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to create an external volume named Volume1 in an existing schema. Volume1 must expose files from an Azure Storage container. The solution must meet the following requirements:
* Ensure that authentication does NOT require storing credentials in Databricks
* Ensure that users can access the files, but NOT modify the files.
* Follow the principle of least privilege
Which type of authentication should you configure, and which permission should you grant to the users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

4. You need to configure compute for the ingestion of telemetry data. The solution must meet the data ingestion and processing requirements.
What should you do?
A) Increase an all-purpose cluster to a larger fixed node type.
B) Enable Photon acceleration for a job compute cluster.
C) Disable autoscaling for a job compute cluster.
D) Move the ingestion pipelines to shared compute.
5. You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
You discover that out of- memory (OOM) errors intermittently cause jobs running on Cluster1 to fail.
You need to identify the root cause of the failures by analyzing the runtime execution behavior. What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions:
Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: Only visible for members | Question # 4 Answer: B | Question # 5 Answer: Only visible for members |