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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Set up and configure an Azure Databricks environment | 15–20% | - 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
- 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
|
| Deploy and maintain data pipelines and workloads | 30–35% | - Build and orchestrate pipelines
- 1. Design and implement Lakeflow Spark Declarative Pipelines
- 2. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
- 3. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
- Monitor, troubleshoot, and maintain workloads
- 1. Apply SDLC practices and version control
- 2. Troubleshoot failures, repair and restart jobs
- 3. Monitor performance, logs, and execution metrics
|
| Prepare and process data | 30–35% | - Optimize and manage data storage
- 1. Implement lakehouse architecture and manage table versions
- 2. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
- 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
|
| Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
- 1. Grant and revoke permissions, manage groups and service principals
- 2. Set up external locations and storage credentials
- 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
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
1. 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) Enable Photon acceleration for a job compute cluster.
B) Increase an all-purpose cluster to a larger fixed node type.
C) Move the ingestion pipelines to shared compute.
D) Disable autoscaling for a job compute cluster.
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) Grant workspace access to external users.
C) Use Delta Sharing.
D) Create a Lakeflow Connect pipeline.
E) Create a SQL warehouse for external users.
3. You need to deploy Databricks Asset Bundles to a development environment. The solution must support automated and repeatable deployments across environments.
What should you use?
A) the Azure Command-Line Interface (CLI)
B) Git folders
C) the Azure Developer CLI (azd)
D) the Databricks CLI
4. 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.filter(df.order_amount.isNotNull())
Does this meet the goal?
A) No
B) Yes
5. You use Declarative Automation Bundles to manage two jobs and an app.
You need to deploy the bundle to development and production environments. The solution must meet the following requirements:
* Deploy the app to both environments.
* Deploy only one job to development.
* Minimize administrative effort.
What should you use?
A) a resources node in a databricks.yml file
B) a variables node in a databricks.yml file
C) separate databricks.yml files for each environment
D) a targets node in a databricks.yml file
Solutions:
Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: D |