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Microsoft DP-750 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and maintain data pipelines and workloads | 30–35% | - Monitor, troubleshoot, and maintain workloads
|
| Topic 2: Prepare and process data | 30–35% | - Optimize and manage data storage
|
| Topic 3: Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
|
| Topic 4: Set up and configure an Azure Databricks environment | 15–20% | - Integrate with Azure services
|
Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:
You have a Lakeflow Spark Declarative Pipelines {SDP) pipeline in Azure Databricks. The pipeline ingests transaction data into a table named Table1.
You need to ensure that in the event of an invalid record, the pipeline continues to run. The solution must meet the following requirements:
* Invalid records must NOT be written to Table 1.
* Invalid records must be preserved for review.
* Minimize development effort
What should you do?
- A. Add a check constraint to Table1
- B. Define a pipeline expectation.
- C. Run were clauses in downstream queries to filter out invalid records.
- D. Implement advanced logic to quarantine the invalid records.
Correct Answer: B 🗳️
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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. A new Declarative Automation Bundles project structure is created.
- B. The bundle configuration is validated.
- C. Jobs or pipelines defined in the bundle are run.
- D. Bundle-defined resources are deployed to the Databricks workspace.
Correct Answer: A 🗳️
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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 have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to create an external volume named Volume1 in an existing schema. Volume1 must expose files from an Azure Storage container. The solution must meet the following requirements:
* Ensure that authentication does NOT require storing credentials in Databricks
* Ensure that users can access the files, but NOT modify the files.
* Follow the principle of least privilege
Which type of authentication should you configure, and which permission should you grant to the users? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
For authentication, a Managed Identity (via a Databricks Access Connector) is the right choice. The Access Connector wraps an Azure-managed identity so Databricks can authenticate to Azure Storage without any credentials being stored in the workspace. The cloud security team controls the identity through Azure RBAC
- there are no secrets to rotate or leak inside Databricks.
For the permission, READ FILES on the volume is exactly right. It allows users to read and list files through the volume path while blocking writes, deletes, and modifications. This is the minimum necessary access, honouring the principle of least privilege.
WRITE FILES would allow modifications, contradicting ' users can access but NOT modify. ' ALL PRIVILEGES grants far more than needed. Service principals with stored client secrets would mean credentials inside Databricks, violating the ' does not require storing credentials ' requirement.
Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/unity-catalog/volumes
You need to configure resiliency for a job in Lakeflow Jobs named Job1 to meet the pipeline deployment and operation requirements.
What should you do?
- A. Enable task-level retries on the ingestion task and leave downstream tasks unchanged.
- B. Configure Job1 to start always from the first task when any task fails.
- C. Disable retries and configure Job1 to run manually only.
- D. Enable a restart upon a job failure and set the retry count to 0.
Correct Answer: A 🗳️
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