Implementing Data Engineering Solutions Using Azure Databricks: DP-750 Exam
"Implementing Data Engineering Solutions Using Azure Databricks", also known as DP-750 exam, is a Microsoft Certification. With the complete collection of questions and answers, PrepPDF has assembled to take you through 93 Q&As to your DP-750 Exam preparation. In the DP-750 exam resources, you will cover every field and category in Microsoft Certified: Fabric Data Engineer Associate Certification helping to ready you for your successful Microsoft Certification.
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- Exam Code: DP-750
- Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
- Certification Provider: Microsoft
- Corresponding Certification: Microsoft Certified: Fabric Data Engineer Associate
- Updated: Oct 17, 2026
- No. of Questions: 93 Questions & Answers with Testing Engine
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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
| Prepare and process data | 30-35% | - Ingest and transform data
|
| Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You need to configure the telemetry pipeline to support the planned changes for pipeline orchestration and address the resiliency issues.
What should you do?
- A. Create three separate jobs and schedule them to run at staggered times.
- B. Use Lakeflow Jobs and define task dependencies between the ingestion, cleansing, and curation tasks.
- C. Create a single Databricks notebook that contains all the ingestion, cleansing, and curation logic in sequential cells.
- D. Use Azure Data Factory to orchestrate three separate Databricks notebook activities and define the task dependencies between each notebook activity.
Correct Answer: B 🗳️
Explanation: Only visible for PrepPDF members. You can sign-up / login (it's free).
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Tabid.
Table! is written by batch jobs every hour and is queried frequently by filtering two columns named Customerld and EventDate.
You expect Table1 to grow significantly over time.
The rows in Table1 are frequently updated and deleted to support compliance requests.
You need to keep query performance consistent as Table1 grows. The solution must minimize update and deletion effort.
What should you include in the solution? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Two features work together to keep performance consistent and update costs low:
OPTIMIZE with ZORDER BY (CustomerId, EventDate). Z-Ordering co-locates rows with the same CustomerId and EventDate values in the same Parquet files. When a query filters on those columns, the Delta engine uses file statistics to skip files that can ' t possibly contain matching rows (data skipping). As the table grows, skipping scales proportionally - query time stays consistent.
Deletion Vectors (delta.enableDeletionVectors = true). When a row is updated or deleted, instead of rewriting the entire Parquet file, Delta marks the affected row in a small companion deletion vector file. This dramatically reduces write amplification for the frequent compliance-driven updates and deletions the question describes. Actual file rewrites are deferred to the next OPTIMIZE run.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/data-skipping
You need to configure compute for the ingestion of telemetry data. The solution must meet the data ingestion and processing requirements.
What should you do?
- A. Move the ingestion pipelines to shared compute.
- B. Enable Photon acceleration for a job compute cluster.
- C. Increase an all-purpose cluster to a larger fixed node type.
- D. Disable autoscaling for a job compute cluster.
Correct Answer: B 🗳️
Explanation: Only visible for PrepPDF members. You can sign-up / login (it's free).
You have an Azure Databricks workspace.
You have a streaming table named sales_order that is populated by using a Lakeflow Spark Declarative Pipelines (SDP) pipeline.
You need to create a new streaming table named sales_order_by_city that summarizes sales by city and calculates the total sales per city.
How should you complete the SQL statement? To answer, drag the appropriate values to the correct targets.
Each value 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.
Correct Answer:
Explanation:
CREATE OR REFRESH STREAMING TABLE
city
CREATE OR REFRESH STREAMING TABLE defines a streaming table managed by Lakeflow Spark Declarative Pipelines. When the pipeline refreshes, Databricks incrementally processes newly available source data and maintains the resulting table. CREATE OR REPLACE TABLE would create a conventional table and does not provide the required streaming-table semantics. The query selects city AS city and calculates SUM(sales) AS total_sales. Because the aggregation must produce one result for each city, the GROUP BY expression must be city. Neither SUM(sales) nor total_sales belongs in the grouping clause: the former is the aggregate calculation, while the latter is only the alias assigned to its result. This produces continuously maintained city-level sales totals from the sales_order source table.
You have an Azure Databticks workspace that is enabled for Unity Catalog and contains a catalog named catalog1.
You have a group named group!
You plan to create a schema named schema1 in catalog1.
You need to ensure that group1 meets the following requirements:
* Can create tables in schema1
* Can modify and query tables
* Cannot grant permissions for the schema and its objects
How should you complete the SQL statements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The correct SQL grants group1 the ability to work within the schema without delegating that ability to anyone else:
GRANT USE SCHEMA ON schema1 TO group1 - required as a prerequisite to access any object inside the schema.
GRANT CREATE TABLE ON SCHEMA schema1 TO group1 - allows creating new tables.
GRANT SELECT, MODIFY ON SCHEMA schema1 TO group1 - SELECT for queries, MODIFY for INSERT/UPDATE/DELETE operations.
Crucially, MANAGE is NOT granted. In Unity Catalog, MANAGE is what allows a principal to grant and revoke privileges on the schema and its objects. Leaving it out means group1 can do all the data work but cannot redistribute those permissions - precisely what the requirement ' Cannot grant permissions for the schema and its objects ' demands.
Reference: https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/manage- privileges/privileges
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