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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Set up and configure an Azure Databricks environment	15–20%	- Select and configure compute resources 1. Configure cluster policies, instance pools, and libraries 2. Manage workspace settings, permissions, and networking 3. Choose compute types: serverless, job compute, SQL warehouse, classic compute - Integrate with Azure services 1. Connect to Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID 2. Configure monitoring with Azure Monitor and diagnostic settings
Deploy and maintain data pipelines and workloads	30–35%	- Monitor, troubleshoot, and maintain workloads 1. Apply SDLC practices and version control 2. Monitor performance, logs, and execution metrics 3. Troubleshoot failures, repair and restart jobs - 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
Secure and govern Unity Catalog objects	15–20%	- Implement data governance and security 1. Enforce data quality, lineage, and auditing 2. Manage catalogs, schemas, tables, views, and volumes 3. Configure access control: row-level, column-level, attribute-based security - Manage data sharing and permissions 1. Grant and revoke permissions, manage groups and service principals 2. Set up external locations and storage credentials
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. Transform using Spark SQL, PySpark, Scala, and Delta Lake 2. Implement schema enforcement, schema drift, and slowly changing dimensions 3. Ingest batch and streaming data from multiple sources

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

Question 1

You need to curate Unity Catalog objects that reference the ERP data. The solution must meet the governance requirements.
What should you do?

A. Create Delta tables directly inside the foreign catalog by running the CREATE TABLE AS SELECT (CTAS) command.
B. In the managed analytics catalog, create views that reference foreign catalog tables that use three-level naming.
C. Create a volume in the foreign catalog and store curated Delta tables in the volume.
D. Run the ALTER TABLE command on the foreign catalog tables to add new columns that are required for analytics.

Question 2

You have an Azure Databricks workspace.
You have an Apache Spark Structured Streaming job named Job! that processes data continuously and fails periodically due to transient errors You need to ensure that Job! meets the following requirements
* Resumes processing from the point that Job1 failed
* Minimizes how long it takes to restart Job!
* Minimizes the costs to restart Job!
What should you do?

A. Increase the minimum number of nodes in the cluster
B. Add an alert and manually restart Job1.
C. Implement checkpointing.
D. Decrease the retry interval.

Question 3

You have an Azure Databricks workspace that is enabled for Unity Catalog You have an Apache Spark Structured Streaming job that writes data to a Delta table.
After the cluster restarts, the streaming job reprocesses previously ingested data You need to prevent the streaming job from reprocessing the data after the cluster restarts.
What should you do?

A. Configure a watermark for the streaming query.
B. Enable change data feed (CDF) for the target table.
C. Increase the trigger interval of the streaming query.
D. Configure a checkpoint location for the streaming query.

Question 4

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named CatalogV Catalog1 contains a schema named Schema! and a table named Table1.
You need to ensure that access to the data in Table1 is controlled by using attribute based access control (ABAC).
What should you apply to Table1, and how should you control access for users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 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. skewing
C. caching
D. spilling

Solutions:

Question 1
Answer: B

Question 2
Answer: C

Question 3
Answer: D

Question 4
Answer: Only visible for members

Question 5
Answer: B