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Microsoft DP-750 Valid Braindumps - Implementing Data Engineering Solutions Using Azure Databricks

DP-750
  • Exam Code: DP-750
  • Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
  • Updated: Aug 03, 2026
  • Q & A: 93 Questions and Answers
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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Set up and configure an Azure Databricks environment15–20%- Integrate with Azure services
  • 1. Configure monitoring with Azure Monitor and diagnostic settings
  • 2. Connect to Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID
- 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
Secure and govern Unity Catalog objects15–20%- Manage data sharing and permissions
  • 1. Grant and revoke permissions, manage groups and service principals
  • 2. Set up external locations and storage credentials
- 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
Prepare and process data30–35%- Ingest and transform data
  • 1. Transform using Spark SQL, PySpark, Scala, and Delta Lake
  • 2. Ingest batch and streaming data from multiple sources
  • 3. Implement schema enforcement, schema drift, and slowly changing dimensions
- Optimize and manage data storage
  • 1. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize
  • 2. Handle structured, semi-structured, and unstructured data
  • 3. Implement lakehouse architecture and manage table versions
Deploy and maintain data pipelines and workloads30–35%- Build and orchestrate pipelines
  • 1. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
  • 2. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
  • 3. Design and implement Lakeflow Spark Declarative Pipelines
- 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

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 != None)
Does this meet the goal?

A) Yes
B) No


2. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have 500 GB of sales data stored as multiple CSV files in cloud storage.
You plan to load the data into a Delta table.
You need to ingest the bulk data by using a solution that meets the following requirements:
* Minimize how long it takes to implement the solution.
* Minimize the amount of custom code required.
What should you use?

A) file upload
B) COPY INTO
C) Apache Spark read APIs
D) Auto Loader


3. You have an Azure Databricks workspace that contains a Delta table named Table 1. Table 1 has accumulated obsolete files.
You need to reduce storage costs. The solution must preserve 30 days of time travel history. Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) Run the OPTIMIZE command on Table1
B) Set the delta. logRetentionDuration table property to 30 days.
C) Set the delta.deletedFileRetentionDuration table property to 10 days
D) Reduce the deleted file retention period to one day.
E) Run the vacuum command on Table1.


4. You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1. Job1 contains multiple tasks.
Failures of non-critical tasks must be logged but must NOT trigger notifications. Notifications must be triggered only when critical tasks have failed, and Job1 has completed You need to configure the job alerting behavior.
What should trigger a notification?

A) a job failure
B) a task failure
C) task success
D) job success


5. You have an Azure Databricks workspace named Workspace1 that contains a takehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
* The schemas and tables can be queried in Databricks.
* The schemas and tables appear alongside other Unity Catalog objects.
* The data is NOT copied into Databricks-managed storage.
Solution: You create a new native catalog in Unity Catalog. Does this meet the goal?

A) Yes
B) No


Solutions:

Question # 1
Answer: B
Question # 2
Answer: B
Question # 3
Answer: C,E
Question # 4
Answer: A
Question # 5
Answer: B

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