Microsoft DP-750 Exam Practice Questions, practice question pack
MICROSOFT · DP-750

Microsoft DP-750 Exam Practice Questions

108 questionsPDF by emailUpdated September 2026

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Three of the 108 questions in this pack

Question 1

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 hubA single Azure SQL database in the West US region that hosts enterprise resource planning (ERP) dataAn Azure Database for PostgreSQL server in the West US region that stores operational maintenance dataData 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 multistep 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 need to configure compute for the ingestion of telemetry data. The solution must meet the data ingestion and processing requirements. What should you do?

  1. Move the ingestion pipelines to shared compute.
  2. Enable Photon acceleration for a job compute cluster.
  3. Increase an all-purpose cluster to a larger fixed node type.
  4. Disable autoscaling for a job compute cluster.
Show answer and explanation

Correct answer: B. Enable Photon acceleration for a job compute cluster.

For production ingestion workloads that must handle telemetry spikes with auto-scaling capabilities, Photon acceleration on a job compute cluster provides the performance optimization needed. Job clusters are designed for scheduled, non-interactive pipelines and support autoscaling during bursty workloads. Photon accelerates Spark SQL operations, improving throughput during peak loads when schema drift handling and data processing demands are highest.

Why the other options are wrong

  • A. Shared compute prevents production workloads from being isolated and doesn't address the requirement for scheduled, non-interactive pipeline execution.
  • C. Increasing an all-purpose cluster to a larger fixed node type wastes resources during off-peak periods and doesn't provide the autoscaling needed for bursty telemetry spikes.
  • D. Disabling autoscaling removes the ability to handle automatic scaling during peak telemetry loads, directly contradicting the scalable ingestion requirement.

Question 2

You have an Azure Databricks workspace. You are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically. You need to configure compute for the pipeline. The solution must minimize operational costs and effort. What should you use?

  1. the existing SQL warehouse
  2. an all-purpose cluster that uses autoscaling
  3. a job cluster that uses autoscaling
  4. a single-node, all-purpose cluster
Show answer and explanation

Correct answer: C. a job cluster that uses autoscaling

A job cluster with autoscaling is the optimal choice for Lakehouse Spark Declarative Pipelines. Job clusters are created on-demand for scheduled pipelines, incurring costs only during execution, and autoscaling ensures efficient resource utilization during variable workloads. This minimizes both operational costs and administrative effort compared to maintaining persistent clusters.

Why the other options are wrong

  • A. SQL warehouses are designed for BI queries, not Spark-based transformation pipelines.
  • B. An all-purpose cluster with autoscaling requires persistent maintenance and incurs continuous costs even when idle, increasing operational overhead.
  • D. A single-node all-purpose cluster cannot scale and will become a bottleneck for pipeline workloads, also requiring persistent management.

Question 3

You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1, metastore1 contains a catalog named catalog1.You need to create a new schema named schema2 that meets the following requirements:Is contained in catalog1 -Uses abfss://[email protected]/data as the managed locationWhich SQL statement should you execute?

  1. CREATE SCHEMA catalog1.schema2 -LOCATION ‘abfss://[email protected]/data’;
  2. CREATE SCHEMA catalog1.schema2 -MANAGED LOCATION ‘abfss://[email protected]/data’;
  3. CREATE CATALOG schema2 -MANAGED LOCATION ‘abfss://[email protected]/data’;
  4. CREATE SCHEMA catalog1.schema2 -WITH DBPROPERTIES (LOCATION-’abfss://[email protected]/data’);
Show answer and explanation

Correct answer: B. CREATE SCHEMA catalog1.schema2 -MANAGED LOCATION ‘abfss://[email protected]/data’;

LOCATION ‘abfss://[email protected]/data’; The CREATE SCHEMA statement with the MANAGED LOCATION clause is the correct syntax for creating a schema in Unity Catalog with a specified managed location. The MANAGED LOCATION parameter defines where Delta tables created in this schema will be stored by default, allowing external storage configuration within the catalog structure.

Why the other options are wrong

  • A. The LOCATION clause without MANAGED is not valid syntax for setting a managed location in Unity Catalog schema creation.
  • C. CREATE CATALOG creates a new catalog, not a schema; schemas must be created with CREATE SCHEMA within an existing catalog.
  • D. DBPROPERTIES uses hyphens in invalid syntax and does not support location configuration; the MANAGED LOCATION clause is the correct approach.

See all 10 free questions Get the full pack, US$39

108 practice questions for Microsoft Azure Databricks Data Engineer (DP-750), with full explanations.

Every question comes with the correct answer, the reasoning behind it, and a short note on why each wrong option is wrong. Work through it once with the answers, then again with the questions-only copy under exam conditions.

  • 108 questions mapped to the DP-750 exam objectives
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
  • Instant delivery by email the moment you check out
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

A DP-750 attempt costs US$165 in the US. This pack is US$39, paid once.

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Last updated September 2026 · 108 questions

What makes the DP-750 hard

DP-750 is Microsoft’s own Databricks exam, launched in March 2026. It overlaps with the Databricks Certified Data Engineer Associate exam but is not the same one: this version is written around the Azure side of the platform, so Microsoft Entra, Azure Data Factory, Key Vault secrets, managed identities, Event Hubs ingestion and Azure Monitor log streaming all appear alongside the Databricks material.

Two thirds of the exam is Prepare and process data plus Deploy and maintain pipelines, and the vocabulary is the current Lakeflow generation: Lakeflow Connect for ingestion, Lakeflow Spark Declarative Pipelines with Auto Loader and expectations, Lakeflow Jobs with triggers, alerts and automatic restarts, and Databricks Asset Bundles deployed through the CLI or REST. Unity Catalog governance is a domain of its own, with attribute-based access control through tags and policies, row filters and column masks, lineage in Catalog Explorer and Delta Sharing.

Performance questions expect candidates to read a DAG and the Spark UI for skew, spill and shuffle, and to know liquid clustering, Z-ordering, deletion vectors, OPTIMIZE and VACUUM. Because the exam is only a few months old, there is very little study material in circulation, and anyone with Databricks experience from a couple of years back will lose marks on the Lakeflow naming alone.

About the exam

DP-750 (Implementing Data Engineering Solutions Using Azure Databricks) leads to Microsoft Certified: Azure Databricks Data Engineer Associate. It covers configuring Azure Databricks compute and Unity Catalog objects, securing and governing Unity Catalog, ingesting, transforming and validating data, and building, deploying and troubleshooting Lakeflow pipelines and jobs. Associate level, no prerequisites. Skills measured as of 11 March 2026.

Exam domains

  • Set up and configure an Azure Databricks environment: 15 to 20%
  • Secure and govern Unity Catalog objects: 15 to 20%
  • Prepare and process data: 30 to 35%
  • Deploy and maintain data pipelines and workloads: 30 to 35%

Passing score 700 out of 1000, US$165 in the US, priced by local currency elsewhere, online proctored or test centre, renews annually for free.

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Questions before you buy

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108 practice questions as a PDF, each with the correct answer, a full explanation and a note on why the other options are wrong, plus a separate questions-only PDF for timed practice.

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