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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prepare and process data	30–35%	- Ingest and transform data 1. Transform using Spark SQL, PySpark, Scala, and Delta Lake 2. Ingest batch and streaming data from multiple sources 3. Implement schema enforcement, schema drift, and slowly changing dimensions - 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
Topic 2: 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. Set up external locations and storage credentials 2. Grant and revoke permissions, manage groups and service principals
Topic 3: 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. Configure cluster policies, instance pools, and libraries 3. Manage workspace settings, permissions, and networking - 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 4: Deploy and maintain data pipelines and workloads	30–35%	- Monitor, troubleshoot, and maintain workloads 1. Troubleshoot failures, repair and restart jobs 2. Apply SDLC practices and version control 3. Monitor performance, logs, and execution metrics - 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

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. 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.

2. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a complex job named Job1 that contains eight tasks. Job1 takes multiple hours to complete.
During the last job run, the final task fails due to a transient issue.
You need to retry the last task without rerunning tasks that have already completed.
What should you do?

A) Repair the current job run.
B) Restart Job1.
C) Update the job parameters.
D) Disable and reenable the job schedule.

3. You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! processes raw data files stored in Azure Storage.
New files arrive at unpredictable intervals.
You need to ensure that Job1 starts automatically when new files arrive and does NOT consume compute resources when no data is available.
Which type of job trigger should you use?

A) scheduled
B) file arrival
C) manual
D) continuous

4. 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 Lakeflow Connect pipeline and connect it to DB1. Does this meet the goal?

A) Yes
B) No

5. You have an Azure Databricks workspace that contains multiple all-purpose clusters. You discover that some clusters remain idle for long periods after users finish their work. You need to reduce compute costs without affecting active workloads. What should you do?

A) Use spot instances.
B) Convert the clusters into job clusters
C) Enable autoscaling.
D) Configure automatic termination.

Solutions:

Question # 1
Answer: Only visible for members

Question # 2
Answer: A

Question # 3
Answer: B

Question # 4
Answer: B

Question # 5
Answer: D