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

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Total 108 questions
Question 1

Your company has Sales, Finance, and HR departments.

You have an Azure Databricks workspace that is enabled for Unity Catalog.

You need to implement Unity Catalog to meet the following requirements:

Access to Unity Catalog for each department must be managed independently from that of the other departments.

The data assets of each department must be logically isolated from those of the other departments.

The solution must support centralized governance.

What should you do for each department?



Answer : A

Create a separate catalog for each department, such as Sales, Finance, and HR. A catalog is the primary unit of data organization and logical isolation in Unity Catalog. Each catalog can contain its own schemas, tables, views, volumes, functions, and models. Catalog-level ownership and permissions can be assigned independently, allowing each department's access to be administered without granting access to the other departments' assets. Microsoft Learn

All three catalogs remain registered in the same Unity Catalog metastore, preserving centralized governance, auditing, lineage, discovery, and access-control administration. A separate storage account is unnecessary because the requirement specifies logical rather than physical isolation. Dynamic views provide row- or column-level filtering but do not create an independent organizational and permission boundary for all departmental assets. Creating separate workspaces would add administrative complexity and is unnecessary because catalogs provide the required isolation within a centrally governed metastore.


Question 2

You have an Azure Databricks solution that was developed by multiple engineers and deployed to development, test, and production environments.

You need to implement an application lifecycle management (ALM) process that meets the following requirements:

Deploys and provides source control for Databricks notebooks, workflows, and jobs in a consistent and repeatable way.

Parameterizes the target deployment environment.

What should you include in the solution?



Answer : A

Declarative Automation Bundles provide an infrastructure-as-code approach for defining, deploying, and managing complete Azure Databricks projects. A bundle can contain notebooks and other source files together with resource definitions for Lakeflow Jobs, pipelines, dashboards, models, and related workspace resources. Storing the bundle in Git provides source control, collaboration, code review, and integration with automated CI/CD workflows.

Bundle targets can define environment-specific values and overrides for development, test, and production. The same source-controlled project can therefore be deployed repeatedly to different environments without manually modifying its core definitions. This directly satisfies the requirement to parameterize the target deployment environment. Microsoft Learn

Git folders provide source control but manual promotion is inconsistent and error-prone. Terraform is primarily intended for provisioning infrastructure and workspace resources rather than packaging the entire Databricks application lifecycle, including its source files. Exporting and importing workspaces is also a manual process that does not provide a reliable parameterized deployment model. Therefore, Declarative Automation Bundles stored in Git are the appropriate solution.


Question 3

You need to curate Unity Catalog objects that reference the ERP data. The solution must meet the governance requirements.

What should you do?



Answer : D

A foreign catalog mirrors the external ERP database and provides read-only access through Lakehouse Federation. Consequently, it cannot host managed volumes or locally created Delta tables, and its foreign tables cannot be altered to add analytics columns. The appropriate design is to create governed views in the managed analytics catalog and have those views reference the foreign tables through fully qualified catalog.schema.table names. This preserves the ERP data in its source system, avoids duplication, and exposes standardized curated objects through Unity Catalog. Permissions can then be granted on the managed views while access to underlying foreign objects remains controlled. Unity Catalog uses a three-level namespace, and foreign catalogs are specifically intended to make external database data queryable without copying it into Databricks-managed storage. Microsoft Learn


Question 4

You have an Azure Databricks workspace and a remote Git repository named Repo1. Repo1 contains two branches named main and Branch1.

You are on a development team that works in Repo1.

You commit changes to Branch1 and must merge the changes into main.

Before completing the merge, you need to meet the following requirements:

* Ensure that Branch1 includes the changes committed to main since Branch1 was created.

* Ensure that merge conflicts are detected and resolved.

What should you do first?



Answer : B

The latest remote changes from main must first be retrieved so that the development environment has the current main-branch state. After pulling those updates, main can be merged into Branch1, and any conflicts can be detected and resolved before Branch1 is proposed for integration into main. Immediately merging a stale local copy of main into Branch1 could omit commits added remotely after Branch1 was created. Pulling Branch1 only synchronizes the feature branch and does not retrieve the required main-branch changes. Creating a pull request before updating and testing Branch1 would defer conflict discovery until later in the integration process. Pulling the latest main changes is therefore the correct first operation in the sequence. Microsoft Learn


Question 5

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Table1. Table! is used as a dimension table and contains the following columns:

* id

* name

* address

Vou need to apply a Type 1 slowly changing dimension (SCD) approach when updates are processed in the name and address columns.

What will occur when an update is processed?



Answer : D

A Type 1 slowly changing dimension maintains only the current state of each dimension record. When the name or address associated with an existing id changes, the corresponding values in the existing row are overwritten with the new values. The previous name and address values are not retained, and no additional historical row or validity-period columns are created. Microsoft Learn

The id column acts as the identifier used to locate the existing dimension record; it is not the attribute being changed in this scenario. Adding a new row for every change and preserving the earlier row with effective dates describes a Type 2 SCD implementation. Type 2 supports historical and point-in-time analysis, whereas Type 1 is appropriate when only the latest attribute values are required or when previous values do not need to be audited.


Question 6

You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1. Job1 contains multiple tasks. Some tasks are business-critical, and others are not.

You need to configure alerting so that:

Failures in non-critical tasks do not send notifications.

Notifications are sent only for failures in the critical parts of the workflow.

What should trigger a notification?



Answer : A

The notification should be triggered by a task failure and configured only on the business-critical tasks. Lakeflow Jobs supports notifications at both the overall job level and the individual task level. By attaching failure notifications specifically to the critical tasks, a notification is sent when one of those tasks fails, while failures in non-critical tasks do not generate unnecessary alerts. Microsoft Learn

A job-level failure notification is too broad because it evaluates the result of the entire workflow. If a non-critical task causes Job1 to enter a failed state, the job-level notification could be sent even though the failed task was not considered business-critical. Job-success and task-success notifications report successful execution and do not satisfy the failure-alerting requirement.


Question 7

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?



Answer : D

Task-level retries allow the ingestion task to recover automatically from transient failures without rerunning unrelated tasks or restarting the complete workflow. Downstream tasks remain governed by their dependencies and start only after ingestion succeeds. This provides focused failure recovery and reduces unnecessary compute consumption. Disabling retries and relying on manual execution directly contradicts the requirement for resilient, automated pipeline operation. Setting the retry count to zero also prevents automatic retry behavior. Restarting the workflow from the first task whenever any task fails would repeat completed processing, increase costs, and potentially reingest data unnecessarily. Lakeflow Jobs supports individual retry policies for tasks, including the number of retries and delay between attempts, making option D the most controlled and operationally efficient configuration. Microsoft Learn


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