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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
|
| Topic 2: Prepare and process data | 30–35% | - Optimize and manage data storage
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30–35% | - Monitor, troubleshoot, and maintain workloads
|
| Topic 4: Set up and configure an Azure Databricks environment | 15–20% | - Select and configure compute resources
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
1. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a daily batch data process that requires complex and highly customized Python transformations. The solution must minimize additional complexity.
What should you include in the solution?
A) a continuous job in Lakeflow Jobs
B) a Databricks notebook triggered by a job
C) an Azure Data Factory pipeline that has data flows
D) a Lakeflow Spark Declarative Pipelines (SDP) pipeline
2. You have an Apache Spark DataFrame named salesDF that contains the following columns:
* Product
* Region
* Sales
* Date
You need to create a pivot table that shows the total sales by product for each region.
How should you complete the PySpark code segment? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
3. 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.
4. You need to configure the telemetry pipeline to support the planned changes for pipeline orchestration and address the resiliency issues.
What should you do?
A) Use Lakeflow Jobs and define task dependencies between the ingestion, cleansing, and curation tasks.
B) Create a single Databricks notebook that contains all the ingestion, cleansing, and curation logic in sequential cells.
C) Use Azure Data Factory to orchestrate three separate Databricks notebook activities and define the task dependencies between each notebook activity.
D) Create three separate jobs and schedule them to run at staggered times.
5. You have an Azure Databricks workspace that uses serverless compute.
You need to ingest data by using Lakeflow Jobs. New records must be processed as soon as they become available.
Which type of job trigger should you use for the ingestion?
A) scheduled
B) continuous
C) file arrival
D) manual
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: Only visible for members | Question # 3 Answer: Only visible for members | Question # 4 Answer: A | Question # 5 Answer: B |
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