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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Deploy and maintain data pipelines and workloads30–35%- 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
- 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 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. Enforce data quality, lineage, and auditing
  • 3. Configure access control: row-level, column-level, attribute-based security
Prepare and process data30–35%- 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
- Ingest and transform data
  • 1. Ingest batch and streaming data from multiple sources
  • 2. Transform using Spark SQL, PySpark, Scala, and Delta Lake
  • 3. Implement schema enforcement, schema drift, and slowly changing dimensions
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. Manage workspace settings, permissions, and networking
  • 2. Choose compute types: serverless, job compute, SQL warehouse, classic compute
  • 3. Configure cluster policies, instance pools, and libraries

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

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


2. 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.


3. You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! contains three tasks named Task1, Task2. and Task3.
If Task1 fails, Task2 and Task3 must be prevented from running. Successfully completed tasks must NOT rerun during recovery.
You need to configure Job1 to support controlled failure handling and recovery What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


4. You have a Lakeflow Spark Declarative Pipelines {SDP) pipeline in Azure Databricks. The pipeline ingests transaction data into a table named Table1.
You need to ensure that in the event of an invalid record, the pipeline continues to run. The solution must meet the following requirements:
* Invalid records must NOT be written to Table 1.
* Invalid records must be preserved for review.
* Minimize development effort
What should you do?

A) Implement advanced logic to quarantine the invalid records.
B) Define a pipeline expectation.
C) Add a check constraint to Table1
D) Run were clauses in downstream queries to filter out invalid records.


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) No
B) Yes


Solutions:

Question # 1
Answer: B,C
Question # 2
Answer: Only visible for members
Question # 3
Answer: Only visible for members
Question # 4
Answer: B
Question # 5
Answer: A

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