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

Exam Code: DP-750
Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
Updated: Oct 18, 2026
Q & A: 93 Questions and Answers

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

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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Configure and manage Azure Databricks environments	15-20%	- Security and authentication setup 1. Service principals and managed identities 2. Azure Key Vault integration 3. Access control for compute resources - Workspace and compute configuration 1. Runtime, Spark, and Photon configuration 2. Autoscaling, termination, and performance tuning 3. Cluster types and configuration (job, all-purpose, serverless)
Topic 2: Prepare and process data	30-35%	- Data ingestion 1. Streaming ingestion using Spark Structured Streaming 2. Batch ingestion using COPY INTO and CTAS 3. Auto Loader and CDC ingestion patterns - Data transformation and modeling 1. Delta Lake table design and SCD patterns 2. SQL and PySpark transformations 3. Joins, aggregations, and normalization/denormalization - Data quality and validation 1. Pipeline expectations and data quality constraints 2. Schema enforcement and validation rules 3. Handling nulls, duplicates, and missing data
Topic 3: Secure and govern data using Unity Catalog	15-20%	- Access control and policies 1. Tags and policy enforcement 2. Attribute-based access control (ABAC) 3. Row-level and column-level security - Data governance fundamentals 1. Catalog, schema, and table management 2. Data lineage and auditing
Topic 4: Deploy and manage data pipelines and workloads	30-35%	- Operational reliability 1. Monitoring and logging (Azure Monitor integration) 2. Error handling and retries - Lakehouse architecture operations 1. Delta Lake optimization and clustering strategies 2. Delta Live Tables pipelines - Pipeline design and orchestration 1. Notebook-based vs declarative pipelines 2. Databricks Jobs and Workflows

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

Question #1

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.

Reveal Solution Discussion 0

Correct Answer:

Explanation:
Two configurations are needed:
Task dependency with ' All succeeded ' run condition: Set Task2 and Task3 to depend on Task1. Change the run condition on Task2 and Task3 to ' All succeeded ' - this means they only execute when all their upstream dependencies (Task1) have succeeded. If Task1 fails, both downstream tasks are skipped automatically, not run with failed inputs.
Repair run for recovery: Lakeflow Jobs ' Repair Run feature lets you re-execute only the tasks that failed (Task1 in this case) and their dependents (Task2 and Task3 if they were skipped), while skipping Task1 and any other tasks that already completed successfully. Successfully completed tasks are never re-executed during repair - their results are reused as-is.
Together these provide both controlled failure propagation (nothing runs downstream of a failure) and efficient recovery.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/repair-job-failures

Question #2

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-fillna(0, subset=[ ' order_amount ' ])
Does this meet the goal?

A. Yes
B. No

Reveal Solution Discussion 0

Correct Answer: B 🗳️

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

Question #3

You have an Azure Databricks workspace
You are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically. You need to configure compute for the pipeline. The solution must minimize operational costs and effort. What should you use?

A. a job cluster that uses autoscaling
B. an all-purpose cluster that uses autoscaling
C. the existing SQL warehouse
D. a single-node, all-purpose cluster

Reveal Solution Discussion 0

Correct Answer: A 🗳️

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

Question #4

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data retention solution that meets the following requirements:
Deleted data must be retained for 30 days to support audits.
Deleted data that is older than 30 days must be removed permanently.
The solution must minimize administrative effort.
Which two properties should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. delta.deletedFileRetentionDuration
B. delta.autoOptimize.autoCompact
C. delta.enableDeletionVectors
D. delta.timeUntilArchived
E. delta.logRetentionDuration

Reveal Solution Discussion 0

Correct Answer: A,E 🗳️

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

Question #5

You have an Azure Databricks workspace that is enabled for Unity Catalog. You plan to run the following PySpark code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Reveal Solution Discussion 0

Correct Answer:

Explanation:
For HOTSPOT questions, each statement must be evaluated against the actual PySpark code shown in the answer area. Key evaluation principles:
DataFrames are immutable - every transformation returns a new DataFrame; the original is unchanged.
Transformations (filter, select, groupBy, join) are lazy and only execute when an action (show, count, write) is called.
Null handling: df.filter(col != None) is incorrect in PySpark due to SQL null semantics; use col.isNotNull() or dropna() instead. Schema changes: using mergeSchema=true or schema evolution handles new columns.
Write modes: ' overwrite ' replaces existing data; ' append ' adds to it.
Always check whether the code uses the correct Delta format (.format( ' delta ' )), Unity Catalog three-part naming, and whether write operations include a checkpointLocation for streaming queries. Evaluate each statement strictly on what the code does, not on what it might intend to do.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics

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

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