Free Microsoft DP-750 Practice Test Questions MCQs

Stop wondering if you're ready. Our Microsoft DP-750 practice test is designed to identify your exact knowledge gaps. Validate your skills with Implementing Data Engineering Solutions Using Azure Databricks questions that mirror the real exam's format and difficulty. Build a personalized study plan based on your free DP-750 exam questions mcqs performance, focusing your effort where it matters most.

Targeted practice like this helps candidates feel significantly more prepared for Implementing Data Engineering Solutions Using Azure Databricks exam day.

2580+ already prepared
Updated On : 31-Aug-2026
58 Questions
Implementing Data Engineering Solutions Using Azure Databricks
4.9/5.0

Page 1 out of 6 Pages

Topic 1: Contoso Case Study

   

Topic 1, Contoso Case Study

Overview
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

Company information
Contoso, Inc. is a renewable energy provider that operates solar and wind farms across North America.

Data Environment
Contoso ingests the following operational and business data:v • Telemetry data: More than 40,000 loT 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 loT 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.

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 (Bl) 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 exactlyonce 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.

You have an Azure Databricks workspace that is enabled for Unity Catalog You have a complex job named Job1 that contains eight tasks. Job! takes multiple hours to complete During the last job run, the final task fails due to a transient issue. You need to retry the last task without rerunning tasks that have already completed. What should you do?

A. Update the job parameters.

B. Repair the current job run.

C. Restart Job!

D. Disable and reenable the job schedule.

B.   Repair the current job run.

You have an Azure Databricks workspace that is enabled for Unity Catalog. You need to recommend a pipeline that ingests files from cloud storage, performs cleansing and enrichment transformations, and writes created Delta tables for analytics. The solution must minimize development effort and provide built-in monitoring and automatic retries.
What should you include in the recommendation?

A. an Apache Spark Structured Streaming job

B. a Databricks notebook triggered by a scheduled job

C. a Lakeflow Spark Declarative Pipelines (SDPJ pipeline

D. an Azure Data Factory pipeline that uses data flows

C.   a Lakeflow Spark Declarative Pipelines (SDPJ pipeline

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.



You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named Catalog 1. Catalog 1 contains a table named Transactions. Transactions contains the following columns:

• transaction_id
• customet_name
• email address
• credit_card_number
• transaction_amount

You need to ensure that business analysts can query all the tows in the Transactions table. The solution must meet the following requirements:

• Prevent the analysts from seeing the full values in the email_address and credit_catd_number columns.
• Ensure that the analysts can see only the values after the @ character in each email address.
• Ensure that the analysts can see only the last four digits of each credit card number. • Enable the analysts to query the table without errors.
• Follow the principle of least privilege.

What should you do?

A. Grant the analysts the SELECT permission for the Transactions table and implement row-level filters.

B. Grant the analysts the select permission for columns that do NOT contain sensitive data.

C. Grant the analysts the select permission for the Transactions table and apply column masks to email_address and credit_card_number

D. Grant the analysts the select permission for the Transactions table and apply columnlevel encryption

C.   Grant the analysts the select permission for the Transactions table and apply column masks to email_address and credit_card_number

You have an Azure Databricks workspace that is enabled for Unity Catalog. You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes records to a Delta table named Table1 by using a data quality rule named rule1 You need to meet the following requirements: • Records that violate rule! must NOT be written to Table1. but the pipeline must continue processing valid records. • Data engineers must be able to review expectation metrics by using minimal development effort. What should you do? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point




You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Sales_orders. Sales.orders stores historical sales data.
You receive a daily CSV file daily that contains new sales records only. The file does NOT contain updates to existing rows You need to load the daily data into Sales.orders. The solution must meet the following requirements:

• Preserve the existing data.
• Add only the new records.
• Minimize processing effort.

Which command should include in the loading strategy?

A. INSERT OVERWRITE

B. UPDATE

C. INSERT INTO

C.   INSERT INTO

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.

A. delta.timeUntilArchived

B. delta.deletedFileRetentionDuration

C. delta.autoOptimize.autoCompact

D. delta.logRetentionDuration

E. delta.enableDeletionVectors

B.   delta.deletedFileRetentionDuration
D.   delta.logRetentionDuration

You have an Azure Databricks workspace named Workspace! that uses a Git repository. The repository contains a Databricks notebook named Notebook1. From the main branch, you create a feature branch named Branch! and commit changes to Notebooks Another user commits changes to Notebook1 in main. When you attempt to merge Branch! into main, the merge fails due to conflicts. You need to merge Branch! into the main branch. The solution must ensure that Notebook1 includes all the changes from both the branches. What should you do?

A. From Workspace1, clone Branch! as a new repository.

B. Apply the changes directly to the main branch.

C. From Workspace1, clone the mam branch as a new repository.

D. Apply the main branch changes to Branch! and resolve the conflicts.

D.   Apply the main branch changes to Branch! and resolve the conflicts.

You have an Azure Databricks workspace named Workspace1 that contains a takehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements: • The schemas and tables can be queried in Databricks.

• The schemas and tables appear alongside other Unity Catalog objects. • The data is NOT copied into Databricks-managed storage.
Solution: You create a new native catalog in Unity Catalog. Does this meet the goal?

A. Yes

B. No

B.   No

You need to deploy Databricks Asset Bundles to a development environment. The solution must support automated and repeatable deployments across environments. What should you use?

A. the Azure Developer CLI (azd)

B. Git folders

C. the Databricks CLI

D. the Azure Command-Line Interface (CLI)

C.   the Databricks CLI

Page 1 out of 6 Pages
Next
12

Implementing Data Engineering Solutions Using Azure Databricks Practice Exam Questions