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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
| Topic 2: Deploy and manage data pipelines and workloads | 30-35% | - Operational reliability
|
| Topic 3: Secure and govern data using Unity Catalog | 15-20% | - Data governance fundamentals
|
| Topic 4: Prepare and process data | 30-35% | - Data ingestion
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
1. You need to deploy Declarative Automation Bundles to a development environment. The solution must support automated and repeatable deployments across environments.
What should you use?
A) the Jobs UI
B) Git folders
C) the Databricks SDK for Python
D) the Databricks CLI
2. 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
3. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to ensure that data lineage is captured and can be reviewed for tables accessed by Databricks notebooks and jobs. The solution must minimize administrative effort.
Which compute configuration should you use to capture the data lineage, and what should you use to review the data lineage? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
4. You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
5. You have an Azure Databricks workspace.
Users report that a Databricks notebook that runs each day takes longer than expected to run.
When reading the Directed Acyclic Graph (DAG), you discover the following issues concerning the Apache Spark stage:
* Most tasks in the stage finish quickly.
* A few tasks in the stage run more slowly.
* The CPU is underutilized at the end of the stage.
* The slow tasks process many more input records.
* The stage is blocked while it waits for the few slow tasks.
What is the root cause of the issues?
A) shuffling
B) caching
C) skewing
D) spilling
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: Only visible for members | Question # 4 Answer: Only visible for members | Question # 5 Answer: C |






