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

Exam Code: DP-750

Exam Name: Implementing Data Engineering Solutions Using Azure Databricks

Updated: Oct 15, 2026

Q & A: 93 Questions and Answers

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Microsoft DP-750 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	Implementing Data Engineering Solutions Using Azure Databricks
Exam Number:	DP-750
Related Certifications:	Microsoft Certified: Azure Data Engineer Associate (DP-203) Microsoft Certified: Fabric Data Engineer Associate (DP-700)
Exam Format:	Scenario-based questions, Interactive items, Case studies, Multiple choice
Exam Duration:	100 minutes
Passing Score:	700
Available Languages:	English
Recommended Training:	Microsoft Learn DP-750 Study Guide DP-750 Training Course (DP-750T00)
Exam Registration:	Pearson VUE Exam Scheduling Official Microsoft Certification Page
Sample Questions:
Exam Way:	Online proctored exam via Pearson VUE
Pre Condition:	Recommended experience with Azure Databricks, SQL, Python, and basic Azure services (Entra ID, Data Factory, Key Vault, Azure Storage).
Official Syllabus URL:	https://learn.microsoft.com/en-us/credentials/certifications/implementing-data-engineering-solutions-using-azure-databricks/

Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Secure and govern data using Unity Catalog	15-20%	- Access control and policies 1. Attribute-based access control (ABAC) 2. Tags and policy enforcement 3. Row-level and column-level security - Data governance fundamentals 1. Catalog, schema, and table management 2. Data lineage and auditing
Deploy and manage data pipelines and workloads	30-35%	- Pipeline design and orchestration 1. Notebook-based vs declarative pipelines 2. Databricks Jobs and Workflows - 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
Configure and manage Azure Databricks environments	15-20%	- Workspace and compute configuration 1. Autoscaling, termination, and performance tuning 2. Cluster types and configuration (job, all-purpose, serverless) 3. Runtime, Spark, and Photon configuration - Security and authentication setup 1. Service principals and managed identities 2. Access control for compute resources 3. Azure Key Vault integration
Prepare and process data	30-35%	- Data quality and validation 1. Pipeline expectations and data quality constraints 2. Handling nulls, duplicates, and missing data 3. Schema enforcement and validation rules - 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. Joins, aggregations, and normalization/denormalization 3. SQL and PySpark transformations

DP-750 Exam FAQ: What Candidates Ask About Microsoft Implementing Data Engineering Solutions Using Azure Databricks

What is the DP-750 exam?

The DP-750 exam, officially known as Microsoft Implementing Data Engineering Solutions Using Azure Databricks, is the Microsoft test that leads to the Microsoft Certified: Azure Databricks Data Engineer Associate (Beta) certification at the Professional level. Passing it validates the skills employers expect from a certified professional. It is also associated with related credentials such as Microsoft Certified: Azure Data Engineer Associate (DP-203), Microsoft Certified: Fabric Data Engineer Associate (DP-700).

Are there any prerequisites for the DP-750 exam?

Recommended experience with Azure Databricks, SQL, Python, and basic Azure services (Entra ID, Data Factory, Key Vault, Azure Storage).

Entry requirements can change, so confirm the latest conditions on the official exam page: https://learn.microsoft.com/en-us/credentials/certifications/implementing-data-engineering-solutions-using-azure-databricks/.

How do I register for the DP-750 exam?

You can book the DP-750 exam through the official registration channels below:

Exam delivery: Online proctored exam via Pearson VUE. Seats at popular test centers fill quickly, so schedule early once your preparation is on track.

What official training is recommended for the DP-750 exam?

Microsoft recommends the following training options for Microsoft Implementing Data Engineering Solutions Using Azure Databricks candidates:

Pair any course with the 93 practice questions from Pass4cram to measure how ready you really are before paying the exam fee.

Can I try the DP-750 practice questions before buying?

Yes. A free PDF demo of the Microsoft Implementing Data Engineering Solutions Using Azure Databricks questions is available, so you can check the question style and answer quality before you pay. Every purchase also includes 365 days of free updates, and if the product expires you can renew the update service at a 50% discount from your member zone.

What happens if I fail the DP-750 exam, and how is my order delivered?

If you take the corresponding DP-750 exam within 60 days of purchase and do not pass, you can apply for a full refund under the 100% Money Back Guarantee: submit a scan of your enrollment slip and your official Score Report (PDF) within 2 days of the exam date, and the claim is processed within 7 days. Attempts made within 3 days of purchase, downloads without an actual exam attempt, free materials, and expired orders are not eligible, and the candidate name must match the payer name. Prefer new material instead of a refund? You can exchange your purchase for two free products of equal value and keep the update service on your original product. As for delivery, the files are available for instant download and are also emailed to you within one minute of payment — if nothing arrives within 2 hours, contact customer service. There is no limit on how many computers you can install the product on.

What topics are covered in the DP-750 exam?

The official Microsoft Implementing Data Engineering Solutions Using Azure Databricks outline is organized into 4 domains. The first three are:

Configure and manage Azure Databricks environments — 15-20% of the exam
Secure and govern data using Unity Catalog — 15-20% of the exam
Deploy and manage data pipelines and workloads — 30-35% of the exam

See the complete exam topics section above for the full outline and the weighting of every domain.

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

Question #1

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named db1.sales_orders.
dbl sales_orders is updated nightly and has change data feed (CDF) enabled.
You need to ingest all the changes from the dbl.sales.ordets table, including inserts, updates, and deletes, into a downstream pipeline.
How should you complete the PsySpark code segment? 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:
When Change Data Feed (CDF) is enabled on a Delta table, reading the full change stream - inserts, updates, and deletes - requires this pattern:
spark.readStream.format( ' delta ' ).option( ' readChangeFeed ' , ' true ' ).table( ' db1.sales_orders ' ) The readChangeFeed option switches the reader from the default ' new rows only ' mode to a mode that returns all change events. Each row in the resulting DataFrame includes a _change_type column (insert, update_preimage, update_postimage, delete) so downstream processing can distinguish what happened to each record.
Without readChangeFeed = true, streaming a Delta table only surfaces newly appended rows. Deletes and updates are invisible, making it unsuitable for true CDC pipelines. The stream also supports startingVersion or startingTimestamp options to begin from a specific point in table history rather than the current moment.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-change-data-feed

Question #2

You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to ensure that data lineage is captured and can be reviewed for tables accessed by Databricks notebooks and jobs. The solution must minimize administrative effort.
Which compute configuration should you use to capture the data lineage, and what should you use to review the data lineage? 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:
Data lineage in Unity Catalog is captured automatically - but only when jobs and notebooks run on clusters that are Unity Catalog-aware. Specifically, clusters must use ' Shared ' or ' Single User ' access mode. Clusters set to ' No Isolation Shared ' or legacy ' High Concurrency ' mode do not emit lineage events to the Unity Catalog lineage service.
No instrumentation, logging code, or external tools are required. The lineage service operates transparently, intercepting read and write operations at the Spark plan level and recording the table-to-table and column-to- column relationships.
To review captured lineage, open Catalog Explorer, navigate to the table, and select the Lineage tab. This shows the upstream sources that populate the table and the downstream consumers that read from it - all as an interactive graph, with no additional tooling needed. This built-in visibility is one of the core governance benefits Unity Catalog provides.
Reference: https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/data-lineage

Question #3

You have an Azure Databricks workspace named Workspace1 that contains a lakehouse 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 Databricks access connector.
Does this meet the goal?

A. Yes
B. No

Reveal Solution Discussion 0

Correct Answer: B 🗳️

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Question #4

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Payments.
Payments stores transaction data and contains a column named payment_amount of the Decimal data type.
You must enforce the following business rule:
payment_amount must be between 0 and 10,000, inclusive
You need to ensure that records that violate the rule are rejected when data is written to the Payments table.
What should you do?

A. Configure row-level security (RLS) to hide records that have invalid values.
B. Add a CHECK constraint to payment_amount to enforce the allowed range.
C. Create a table update trigger.
D. Run SELECT statements to filter out records that have invalid values in payment_amount.

Reveal Solution Discussion 0

Correct Answer: B 🗳️

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Question #5

You have an Azure Databricks workspace.
You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming The solution must authenticate to Event Hubs and read the event payload.
How should you complete the PySpark code segment? 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:
Reading from Azure Event Hubs in Spark Structured Streaming requires three things:
An EventHubsConf object built with the Event Hubs connection string (eventhubs.connectionString). This object is then converted to a map with .toMap before being passed to Spark.
spark.readStream.format( ' eventhubs ' ).options(**ehConf).load() to create the streaming DataFrame. The ' eventhubs ' format is provided by the azure-eventhubs-spark connector library.
A cast( ' string ' ) on the body column to decode the binary payload. Event Hubs delivers messages with the raw event bytes in a column called body - without the cast, you get binary data rather than the readable JSON or text payload.
This is the standard, documented integration pattern for connecting Azure Databricks to Event Hubs with Structured Streaming, providing the checkpoint-based exactly-once semantics required by the Contoso telemetry pipeline.
Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/storage/events/eventhubs

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