Download Implementing Data Engineering Solutions Using Azure Databricks.DP-750.ExamTopics.2026-10-07.16q.vcex
| Vendor: | Microsoft |
| Exam Code: | DP-750 |
| Exam Name: | Implementing Data Engineering Solutions Using Azure Databricks |
| Date: | Oct 07, 2026 |
| File Size: | 427 KB |
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Demo Questions
Question 1
You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
You discover that out-of-memory (OOM) errors intermittently cause jobs running on Cluster1 to fail.
You need to identify the root cause of the failures by analyzing the runtime execution behavior.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct answer: To work with this question, an Exam Simulator is required.
Question 2
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct answer: To work with this question, an Exam Simulator is required.
Question 3
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1. Job1 contains multiple tasks.
Failures of non-critical tasks must be logged but must NOT trigger notifications. Notifications must be triggered only when critical tasks have failed, and Job1 has completed.
You need to configure the job alerting behavior.
What should trigger a notification?
- task success
- a job failure
- a task failure
- job success
Correct answer: C
Question 4
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data retention solution that meets the following requirements:
Deleted data must be retained for 30 days to support audits.
Deleted data that is older than 30 days must be removed permanently.
The solution must minimize administrative effort
Which two properties should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- delta.timeUntilArchived
- delta.enableDeletionVectors
- delta.logRetentionDuration
- delta.deletedFileRetentionDuration
- delta.autoOptimize.autoCompact
Correct answer: B, C
Question 5
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named catalog1.
You have a group named group1.
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: To work with this question, an Exam Simulator is required.
Question 6
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: To work with this question, an Exam Simulator is required.
Question 7
You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1.metastore1 contains:
A catalog named Sales -
A schema named Customers in the Sales catalog
A table named Customer_details in the Customers schema
You need to ensure that a user named User1 can update the data in Customer_details. The solution must meet the following requirements:
Ensure that User1 cannot create new tables.
Follow the principle of least privilege.
Which permission should you grant to User1 for each object? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct answer: To work with this question, an Exam Simulator is required.
Question 8
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will need to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions. When you are ready to answer a question, select the “Question” button to return to the question.
Overview
Company Information
Contoso, Inc. is a renewable energy provider that operates solar and wind farms across North America.
Existing Environment
Azure Environment
Contoso has a single Azure Databricks workspace named Workspace1 in the West US Azure region. Workspace1 is enabled for Unity Catalog.
Workspace1 contains all-purpose clusters for both development and production workloads.
The company's Azure environment contains:
In the West US, Central US, and East US Azure regions, Azure event hubs that stream telemetry data and an Azure Data Lake Storage Gen2 account in each region for each hub
A single Azure SQL database in the West US region that hosts enterprise resource planning (ERP) data
An Azure Database for PostgreSQL server in the West US region that stores operational maintenance data
Data Environment
Contoso ingests the following operational and business data:
Telemetry data: More than 40,000 IoT sensors across 28 sites emit JSON telemetry events every few seconds. Each site sends the events to the nearest event hub, which writes the data into the corresponding Data Lake Storage Gen2 account. These files frequently experience schema drift.
Maintenance logs: Maintenance systems generate historical repair logs, daily incremental updates, technician notes, and unstructured attachments that are stored in the Data Lake Storage Gen2 accounts.
Operational maintenance data: Structured operational maintenance data is stored on the Azure Database for PostgreSQL server.
External weather data: Hourly weather forecasts are retrieved from a REST API and written to the Data Lake Storage Gen2 accounts.
ERP data: Daily CSV extracts of 50 to 100 GB contain equipment metadata, work orders, and purchase order information.
Problem Statements
The company’s existing analytics environment has several issues:
Ingestion
Telemetry pipelines fall behind during peak loads.
Telemetry ingestion fails when schema drift occurs.
Streaming pipelines reprocess events after a pipeline restarts.
Compute
Production and development workloads run on the same all-purpose clusters.
Production and development workloads do NOT support autoscaling or workload isolation.
Governance
The ERP data is duplicated across systems and development teams.
Naming conventions are inconsistent across development teams, regions, and products.
Ownership of the IoT sensors changes over time, and analysts must track the full history of the ownership.
Occasionally, equipment manufacturers must correct data-entry mistakes in equipment names. Historical values are NOT required.
Pipeline operations
Pipelines lack resiliency, alerting, and centralized scheduling.
Requirements
Planned Changes
Contoso plans to implement the following changes:
Implement scalable data pipeline orchestration.
Create a managed analytics catalog in Unity Catalog.
Implement a consistent approach to creating curated datasets.
Establish a centralized governance model across ingestion, cleansed, and curated layers.
Grant data engineers access to the ERP tables by using minimal development effort.
Adopt a compute strategy that isolates production workloads and supports autoscaling.
Adopt a slowly changing dimension (SCD) approach to address current data modeling issues.
Technical Requirements
Contoso identifies the following environment and compute requirements:
Ensure that production ingestion workloads run on compute clusters that can scale automatically during telemetry spikes.
Provide fast and consistent performance for business intelligence (BI) workloads.
Prevent development activity from affecting production pipelines.
Production ingestion workloads must run as scheduled, non-interactive pipelines rather than on shared interactive development clusters.
Contoso identifies the following data ingestion and processing requirements:
Auto-scale ingestion pipelines to handle bursty workloads.
Handle schema drift for the maintenance and telemetry data.
Ingest file-based telemetry data by using minimal operational effort.
Store all the ingested data in a format that supports incremental processing.
Support the continuous ingestion of telemetry data from the event hubs by using exactly-once semantics.
Support the ingestion of the structured maintenance data from the Azure Database for PostgreSQL server.
Build a new telemetry pipeline that ingests raw events from the event hubs, cleanses the data, and publishes curated tables to Unity Catalog.
Ensure that the Apache Spark Structured Streaming pipelines reading from the event hubs write the data into a managed Delta table named telemetry.raw_events. The pipelines must support schema drift and resume processing after failures without reprocessing the data.
Contoso identifies the following data modeling and optimization requirements:
Build curated tables that standardize business logic.
Overwrite equipment metadata attributes, such as name, manufacturer, model, and commissioning date, when the attributes change. Historical values are NOT required.
Contoso identifies the following pipeline deployment and operation requirements:
Orchestrate multi-step ingestion and transformation workflows.
Define a clear execution order and dependencies.
Automatically retry failed steps and notify operators.
Schedule ingestion and transformation workloads consistently.
Governance Requirements
Contoso identifies the following governance requirements:
Centralize the metadata catalog.
Provide isolated development areas that follow standard naming conventions.
Establish a consistent structure for organizing raw, cleansed, and curated data.
Provide a read-only mechanism to reference the ERP data through a foreign catalog.
Business Requirements
Contoso identifies the following business requirements:
Improve ingestion reliability and reduce operational effort.
Standardize data definitions across development teams.
Which ingestion option should you recommend for each data source? To answer, drag the appropriate options to the correct data sources. Each option 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: To work with this question, an Exam Simulator is required.
Question 9
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer profile data.
Business users must analyze how customer profile records change over time. They must also be able to query earlier versions of the table.
You need to implement a solution that:
Maintains persistent historical versions of customer profile records for long-term analysis.
Allows users to query earlier versions of the Delta table.
Minimizes maintenance effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct answer: To work with this question, an Exam Simulator is required.
Question 10
You have an Azure Databricks workspace named Workspace1.
You create a compute cluster named Cluster1 that will be used to ingest data.
You need to install the required libraries on Cluster1. The solution must use Unity Catalog for access control.
What should you do?
- Install the libraries by using pip3.
- Create a custom dependency management script and run the script from a Databricks notebook.
- Upload the libraries to Workspace1 and install the libraries on Cluster1.
- Install the libraries on Cluster1 and manually restart the cluster.
Correct answer: C
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