- Exam Code: DP-750
- Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
- Updated: Oct 15, 2026
- Q & A: 93 Questions and Answers
Microsoft DP-750 Exam : Implementing Data Engineering Solutions Using Azure Databricks
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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:
Question #1
You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2. You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running How should you configure Job1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Two task-level settings solve this:
Task1 retry policy: configure Task1 with a maximum number of retries and a retry interval. When Task1 fails, Lakeflow Jobs automatically re-runs it up to the retry limit without any manual intervention. This handles transient infrastructure failures transparently.
Task2 run condition set to ' All succeeded ' with Task1 as its dependency: this means Task2 only starts when Task1 has succeeded. If Task1 fails and exhausts all retries, Task2 remains blocked - it never runs on data from a failed upstream ingestion. The dependency is declared in Task2 ' s ' Depends on ' setting in the job configuration.
These two settings are independent and composable. Task1 ' s retry policy gives it multiple chances to recover. Task2 ' s dependency and run condition ensure the downstream transformation only runs on clean, successfully ingested data.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#task-retries
Question #2
You have an Azure Databricks workspace.
Users report that a Databricks notebook that runs each day takes longer than expected to run.
When reading the Directed Acyclic Graph (DAG), you discover the following issues concerning the Apache Spark stage:
* Most tasks in the stage finish quickly.
* A few tasks in the stage run more slowly.
* The CPU is underutilized at the end of the stage.
* The slow tasks process many more input records.
* The stage is blocked while it waits for the few slow tasks.
What is the root cause of the issues?
- A. caching
- B. spilling
- C. skewing
- D. shuffling
Correct Answer: C 🗳️
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Question #3
You have an Azure Databricks workspace.
You have an Azure key vault named kv-secure that stores a secret named storageKey. The value of storageKey is managed and updated by the cloud security team at your company.
You need to enable a Databricks notebook named Notebook 1 to retrieve the value of storageKey securely at runtime. The solution must follow the principle of least privilege and always retrieve the latest value.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The solution is a Key Vault-backed secret scope combined with dbutils.secrets.get() in the notebook.
An Azure Key Vault-backed secret scope does not copy the secret value into Databricks. Every call to dbutils.
secrets.get() fetches the live current value directly from Key Vault at runtime. This means when the cloud security team rotates storageKey in Key Vault, the notebook automatically picks up the new value on its next run - no Databricks configuration changes needed. That satisfies ' always retrieve the latest value. ' The principle of least privilege is upheld because only the secret scope has read access to Key Vault through a service principal or managed identity, and individual users never see the raw secret value - Databricks redacts it in logs automatically.
A Databricks-backed scope stores a copy of the secret inside Databricks, so it wouldn ' t automatically reflect Key Vault updates. Hardcoding or using environment variables would expose the secret in plain text.
Reference: https://learn.microsoft.com/en-us/azure/databricks/security/secrets/secret-scopes
Question #4
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named finance, finance contains two schemas named default and procurement.
You need to create a table named assets in the procurement schema, assets must contain the following columns:
* asset.id
* asset, type
* asset_name
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:
The correct SQL statement uses the full three-part namespace finance.procurement.assets with the three specified columns.
In Unity Catalog, every object lives in a three-tier hierarchy: catalog # schema # table. Using the full path finance.procurement.assets guarantees the table lands in the right schema regardless of the session ' s current catalog or schema context. Omitting the catalog or schema name relies on the session default, which may not be finance.procurement - a silent mistake that ' s hard to catch.
The column names asset_id, asset_type, and asset_name must match the spec exactly. Unity Catalog applies access controls, lineage tracking, and tagging at the column level, so the names are meaningful beyond just the schema. Once created, any GRANT statements can target specific columns for fine-grained access control.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-ddl-create- table-using
Question #5
You manage Declarative Automation Bundles by using the Databricks CLI.
You run the following command in a terminal window.
databricks bundle init
What occurs when you run the command?
- A. Bundle-defined resources are deployed to the Databricks workspace.
- B. The bundle configuration is validated.
- C. A new Declarative Automation Bundles project structure is created.
- D. Jobs or pipelines defined in the bundle are run.
Correct Answer: C 🗳️
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