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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 1. Set up external locations and storage credentials 2. Grant and revoke permissions, manage groups and service principals - Implement data governance and security 1. Configure access control: row-level, column-level, attribute-based security 2. Manage catalogs, schemas, tables, views, and volumes 3. Enforce data quality, lineage, and auditing
Topic 2: Set up and configure an Azure Databricks environment	15–20%	- 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 - 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
Topic 3: Deploy and maintain data pipelines and workloads	30–35%	- Build and orchestrate pipelines 1. Design and implement Lakeflow Spark Declarative Pipelines 2. Configure Lakeflow Jobs: schedules, triggers, alerts, retries 3. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs - Monitor, troubleshoot, and maintain workloads 1. Monitor performance, logs, and execution metrics 2. Troubleshoot failures, repair and restart jobs 3. Apply SDLC practices and version control
Topic 4: 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. Handle structured, semi-structured, and unstructured data 2. Implement lakehouse architecture and manage table versions 3. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1.
Metastore1 contains a catalog named catalog 1.
You need to create a new schema named schema2 that meets the following requirements:
* Is contained in catalog1
* Uses abfss://containergstorageaccount.dfs.core.windows.net/data as the Managed location Which SQL statement should you execute?

A) CREATE SCHEMA catalog1.schema2
WITH DBPROPERTIES (LOCATION= ' abfss://[email protected]/data
' );
B) CREATE CATALOG schema2
MANAGED LOCATION ' abfss://[email protected]/data ' ;
C) CREATE SCHEMA catalog1.schema2
MANAGED LOCATION ' abfss://[email protected]/data ' ;
D) CREATE SCHEMA catalog1.schema2
LOCATION ' abfss://[email protected]/data ' ;

2. 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.

3. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to profile a table to meet the following requirements:
* The count of null values per column must be evaluated repeatedly as new records are added to the table.
* Changes in the count of null values must be observable over the progression of the dataset.
Which type of profile should you create?

A) time series
B) inference
C) snapshot

4. 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) continuous
B) file arrival
C) scheduled
D) manual

5. You have an Azure Databricks workspace named Workspace! that uses a Git repository. The repository contains a Databricks notebook named Notebook1.
From the main branch, you create a feature branch named Branch! and commit changes to Notebooks Another user commits changes to Notebook1 in main.
When you attempt to merge Branch! into main, the merge fails due to conflicts.
You need to merge Branch! into the main branch. The solution must ensure that Notebook1 includes all the changes from both the branches.
What should you do?

A) From Workspace1, clone Branch! as a new repository.
B) Apply the changes directly to the main branch.
C) From Workspace1, clone the mam branch as a new repository.
D) Apply the main branch changes to Branch! and resolve the conflicts.

Solutions:

Question # 1
Answer: C

Question # 2
Answer: D

Question # 3
Answer: A

Question # 4
Answer: B

Question # 5
Answer: D

Microsoft DP-750 : Implementing Data Engineering Solutions Using Azure Databricks

5.How to choose DP-750 test engine or DP-750 online test engine?