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

Exam Code: DP-750
Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
Updated: Sep 13, 2026
Q & A: 93 Questions and Answers

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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Configure and manage Azure Databricks environments	15-20%	- Workspace and compute configuration 1. Runtime, Spark, and Photon configuration 2. Cluster types and configuration (job, all-purpose, serverless) 3. Autoscaling, termination, and performance tuning - Security and authentication setup 1. Service principals and managed identities 2. Azure Key Vault integration 3. Access control for compute resources
Secure and govern data using Unity Catalog	15-20%	- Data governance fundamentals 1. Catalog, schema, and table management 2. Data lineage and auditing - Access control and policies 1. Tags and policy enforcement 2. Attribute-based access control (ABAC) 3. Row-level and column-level security
Deploy and manage data pipelines and workloads	30-35%	- Operational reliability 1. Error handling and retries 2. Monitoring and logging (Azure Monitor integration) - Lakehouse architecture operations 1. Delta Live Tables pipelines 2. Delta Lake optimization and clustering strategies - Pipeline design and orchestration 1. Databricks Jobs and Workflows 2. Notebook-based vs declarative pipelines
Prepare and process data	30-35%	- Data transformation and modeling 1. SQL and PySpark transformations 2. Delta Lake table design and SCD patterns 3. Joins, aggregations, and normalization/denormalization - Data ingestion 1. Auto Loader and CDC ingestion patterns 2. Batch ingestion using COPY INTO and CTAS 3. Streaming ingestion using Spark Structured Streaming - Data quality and validation 1. Pipeline expectations and data quality constraints 2. Handling nulls, duplicates, and missing data 3. Schema enforcement and validation rules

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

Question #1

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.

Reveal Solution Discussion 0

Correct Answer:

Explanation:
Two actions are needed to reclaim storage while keeping tables queryable:
Set delta.deletedFileRetentionDuration and delta.logRetentionDuration to 90 days on each table. These properties define the retention floor - VACUUM will not touch anything newer than this threshold, so no data needed for time travel within 90 days can be accidentally removed.
Run VACUUM on each table. VACUUM is the Delta Lake command that physically removes data files and transaction log entries older than the retention duration. Importantly, VACUUM runs as a background operation - it uses Delta Lake ' s MVCC (multi-version concurrency control) to ensure that concurrent reads against the table continue uninterrupted while cleanup happens. Tables are fully available throughout.
OPTIMIZE compacts small files for query performance but doesn ' t delete anything. Manually deleting files outside the Delta protocol would corrupt the table.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-vacuum

Question #2

You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 runs every hour.
Occasionally, Job1 takes longer than one hour to complete.
You need to configure the job scheduling behavior to meet the following requirements:
* Overlapping runs must be prevented to avoid data corruption.
* Scheduled runs must not be discarded when another run is already active.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Reveal Solution Discussion 0

Correct Answer:

Explanation:
Concurrency setting: Limit concurrent runs to one.
Execution behavior: Queue the new run.
Limiting concurrent runs to one ensures that only one instance of Job1 can execute at a time. This prevents two hourly runs from simultaneously modifying the same tables, files, checkpoints, or downstream systems, thereby reducing the risk of duplicate processing and data corruption. When a scheduled trigger occurs while an earlier execution is still active, queueing the new run preserves that execution and starts it after the active run finishes. Allowing concurrent runs would violate the non-overlap requirement. Restarting the job during an overlap could interrupt partially completed work. Canceling the new run or skipping it would avoid simultaneous execution, but the scheduled processing interval could be lost. Single-run concurrency combined with queueing therefore serializes the executions without discarding scheduled work.

Question #3

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.
Table1 stores customer profile data.
Business users must analyze how customer profile records change over time. They must also be able to query earlier versions of the table.
You need to implement a solution that:
* Maintains persistent historical versions of customer profile records for long-term analysis.
* Allows users to query earlier versions of the Delta table.
* Minimizes maintenance effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Reveal Solution Discussion 0

Correct Answer:

Explanation:
To record historical changes: Implement a Type 2 slowly changing dimension (SCD).
To support temporal analysis: Use Delta Lake time travel.
A Type 2 slowly changing dimension preserves customer-profile history by inserting a new record whenever a tracked attribute changes instead of overwriting the existing record. Effective dates, expiration dates, version values, or current-record indicators can identify which version applied during a particular period. This provides persistent business history for long-term analysis. Delta Lake time travel supports temporal analysis of the physical table by allowing users to query an earlier version with VERSION AS OF or TIMESTAMP AS OF. Time travel is useful for auditing and reproducing previous results, but its availability depends on retained Delta log entries and data files. Therefore, it should not replace a Type 2 SCD for permanent customer history. Together, the two features satisfy the historical-record and earlier-version requirements.

Question #4

You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a daily batch data process that requires complex and highly customized Python transformations. The solution must minimize additional complexity.
What should you include in the solution?

A. an Azure Data Factory pipeline that has data flows
B. a Lakeflow Spark Declarative Pipelines (SDP) pipeline
C. a Databricks notebook triggered by a job
D. a continuous job in Lakeflow Jobs

Reveal Solution Discussion 0

Correct Answer: C 🗳️

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Question #5

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Sales_orders. Sales.orders stores historical sales data.
You receive a daily CSV file daily that contains new sales records only. The file does NOT contain updates to existing rows You need to load the daily data into Sales.orders. The solution must meet the following requirements:
* Preserve the existing data.
* Add only the new records.
* Minimize processing effort.
Which command should include in the loading strategy?

A. UPDATE
B. INSERT OVERWRITE
C. INSERT INTO

Reveal Solution Discussion 0

Correct Answer: C 🗳️

Explanation: Only visible for VCEEngine members. You can sign-up / login (it's free).

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

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