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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Deploy and maintain data pipelines and workloads	30–35%	- Monitor, troubleshoot, and maintain workloads 1. Monitor performance, logs, and execution metrics 2. Apply SDLC practices and version control 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
Set up and configure an Azure Databricks environment	15–20%	- 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. Configure cluster policies, instance pools, and libraries 2. Choose compute types: serverless, job compute, SQL warehouse, classic compute 3. Manage workspace settings, permissions, and networking
Prepare and process data	30–35%	- 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. 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
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. 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

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q61-Q66):

NEW QUESTION # 61
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.deletedFileRetentionDuration table property to 10 days
B. Run the vacuum command on Table1.
C. Reduce the deleted file retention period to one day.
D. Set the delta. logRetentionDuration table property to 30 days.
E. Run the OPTIMIZE command on Table1

Answer: A,B

Explanation:
The correct answers are B and E.
The goal is to reduce storage costs while preserving 30 days of time travel. Two actions are needed:
Set delta.deletedFileRetentionDuration to a value aligned with the 30-day requirement (Option B). Note: the answer option states '10 days' which would be insufficient for 30-day time travel - in practice this property should be set to at least 30 days. This property defines the retention floor: VACUUM will not delete any file newer than this threshold.
Run VACUUM on Table1 (Option E). VACUUM physically removes unreferenced data files older than the retention duration from storage. Without running VACUUM, obsolete files accumulate indefinitely regardless of the retention property setting - the property tells VACUUM what to keep; VACUUM is what does the actual cleanup.
Option C (OPTIMIZE) compacts small files for better query performance but never deletes anything. Option D (logRetentionDuration) keeps the transaction log for time travel but doesn't free up the data file storage.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-vacuum

NEW QUESTION # 62
Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to create an external volume named Volume1 in an existing schema. Volume1 must expose files from an Azure Storage container. The solution must meet the following requirements:
- Ensure that authentication does NOT require storing credentials in
Databricks.
- Ensure that users can access the files, but NOT modify the files.
- Follow the principle of least privilege.
Which type of authentication should you configure, and which permission should you grant to the users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 63
What does the VACUUM command do in Delta Lake?

A. Compresses JSON files
B. Rewrites table schema
C. Deletes old unreferenced files
D. Optimizes query performance

Answer: C

Explanation:
VACUUM removes old, unreferenced files that are no longer needed after updates or deletes. It helps manage storage but does not improve query performance directly. OPTIMIZE handles performance improvements. Schema changes are unrelated.

NEW QUESTION # 64
Which ingestion option should you recommend for each data source? To answer, drag the appropriate options to the correct data sources. 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.

Answer:

Explanation:

Explanation:
The right ingestion tool depends on the source characteristics:
File-based telemetry and maintenance data # Auto Loader (cloudFiles). It monitors ADLS Gen2 for new arrivals, handles schema inference and evolution for the frequent schema drift, and requires minimal operational effort.
Real-time telemetry from Event Hubs # Spark Structured Streaming with the azure-eventhubs-spark connector. This provides exactly-once semantics and checkpoint-based recovery, satisfying 'resume processing after failures without reprocessing.' Structured maintenance data from PostgreSQL # JDBC connector. Databricks supports direct JDBC reads from relational databases with pushdown predicates.
Daily CSV ERP extracts (50-100 GB) # COPY INTO or Auto Loader. Both support idempotent incremental batch loading into Delta tables with minimal code.
Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/auto-loader/

NEW QUESTION # 65
You have an Azure Databricks workspace named Workspace1 that contains a cluster named Cluster1.
You need to enable a user named User1 to install a Maven package on all the nodes of Cluster1. The solution must follow the principle of least privilege.
What should you do?

A. Grant User1 the CAN MANAGE permission for Cluster1.
B. Assign User1 the workspace admins role for Workspace1.
C. Grant User1 the CAN RESTART permission for Cluster1.
D. Assign User1 the Contributor role for the resource group that contains Workspace1.

Answer: A

Explanation:
Installing a Maven package on every node requires changing Cluster1's library configuration. The CAN MANAGE permission grants the cluster-level management capabilities required to add or remove libraries without giving User1 broader control over the entire workspace or Azure resource group. CAN RESTART allows a user to restart the cluster but does not provide sufficient authority to change its library configuration.
Assigning the Azure Contributor role would grant excessive control over the resource group and does not represent the appropriate Databricks object-level permission. Making User1 a workspace administrator would provide substantially more privileges than necessary. Granting CAN MANAGE directly on Cluster1 therefore satisfies the operational requirement while limiting User1's authority to the specific compute resource that must be modified.

NEW QUESTION # 66
......

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