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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. 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 - Optimize and manage data storage 1. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize 2. Implement lakehouse architecture and manage table versions 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. Grant and revoke permissions, manage groups and service principals 2. Set up external locations and storage credentials
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. Manage workspace settings, permissions, and networking 3. Configure cluster policies, instance pools, and libraries - 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
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 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) Yes
B) No

2. You have an Azure Databticks workspace that contains an all-purpose compute cluster named Cluster1.
Cluser1 is used for
interactive development.
You need to configure Cluster1 to meet the following requirements:
* Automatically add and remove worker nodes based on workload demand
* Automatically shut down when the cluster has been idle for a specific period.
What should you configure for each requirement? To answer, drag the appropriate options to the correct requirements. Each option 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.
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) Decrease the retry interval.
B) Increase the minimum number of nodes in the cluster
C) Add an alert and manually restart Job1.
D) Implement checkpointing.

4. You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster! You need to configure Cluster1 to meet the following requirements;
* The cluster must scale up automatically when workloads increase.
* The cluster must scale down automatically when workloads decrease.
The solution must minimize costs.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) Apply a compute policy that enables users to manage the cluster settings.
B) Enable autoscaling for Cluster1.
C) Configure Cluster1 to terminate after 30 minutes of inactivity.
D) Disable Photon acceleration.
E) Specify a fixed number of workers.

5. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a data lifecycle and expiration solution that meets the following requirements
* Transaction logs and deleted data files that are older than 90 days must be removed from Delta tables to reclaim storage.
* All the tables must remain available for querying during the cleanup process.
* Administrative effort must be minimized
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.