Try 12 Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) sample questions and practice-test preview prompts on Azure Databricks environments, Unity Catalog governance, pipelines, processing, security, and workload maintenance.
DP-750 is the beta exam for Microsoft Certified: Azure Databricks Data Engineer Associate. It focuses on Azure Databricks environments, Unity Catalog governance, data processing, pipelines, and workload maintenance.
IT Mastery coverage for DP-750 is under review. Use this page to try 12 original sample questions, review the exam snapshot, route fit, and closest live Azure and Databricks practice paths.
Practice option: Sample questions available
Start with the 12 sample questions on this page. Dedicated practice for DP-750: Azure Databricks Data Engineer Associate is not currently included as a full web-app practice page; enter your email to get updates when full practice becomes available or expands for this exam.
Need live practice now? See currently available IT Mastery exam pages.
| Area | What to review |
|---|---|
| Environment setup | workspace configuration, compute, integration, access, and platform setup |
| Governance | Unity Catalog, security, data quality, and policy-aware access |
| Data processing | SQL, Python, transformation, modeling, and workload design |
| Pipelines | deployment, maintenance, troubleshooting, and operational workflow |
Try these 12 original sample questions for Microsoft DP-750. They are designed for self-assessment and are not official exam questions.
Topic: workspace setup
A data team needs an Azure Databricks workspace integrated with corporate identity and controlled access. What should be planned?
Best answer: A
Explanation: DP-750-style setup questions focus on secure workspace configuration, access, and integration with Azure controls.
What this tests: Planning Azure Databricks environment setup.
Topic: Unity Catalog
A company wants centralized governance for tables, permissions, lineage, and data discovery. Which Databricks capability is most relevant?
Best answer: B
Explanation: Unity Catalog provides governance across data and AI assets, including permissions, discovery, and lineage-oriented controls.
What this tests: Recognizing Unity Catalog governance use cases.
Topic: data pipeline design
A batch pipeline ingests raw files, validates records, and writes curated tables for analytics. What should the engineer design?
Best answer: C
Explanation: Databricks data-engineering work often organizes pipelines into layered processing with validation and curated outputs.
What this tests: Designing data processing stages.
Topic: compute choice
A scheduled production job needs reliable execution and controlled cost. What should be selected carefully?
Best answer: D
Explanation: Production workloads should use controlled compute configuration. Policies and sizing help balance reliability and cost.
What this tests: Choosing Databricks compute for jobs.
Topic: data quality
A pipeline sometimes loads duplicate customer records. What should be added?
Best answer: A
Explanation: Data engineering pipelines should include validation and handling for known data-quality issues. Duplicates should not be silently accepted.
What this tests: Applying data-quality controls.
Topic: access control
Analysts should query curated tables but not raw sensitive files. What should the governance model use?
Best answer: B
Explanation: Least-privilege access should be applied through catalog, schema, table, and storage controls where appropriate.
What this tests: Implementing governed access in Databricks.
Topic: troubleshooting jobs
A nightly job fails after a library upgrade. What should the engineer inspect?
Best answer: C
Explanation: Job failures should be investigated using run evidence, environment changes, dependencies, code, and data. Library changes are a likely clue.
What this tests: Troubleshooting Databricks pipeline failures.
Topic: incremental processing
A pipeline reprocesses all history every hour even though only new files arrive. What should be evaluated?
Best answer: D
Explanation: Incremental processing can reduce cost and latency when only new or changed data must be processed. Checkpointing helps reliability.
What this tests: Optimizing data pipeline design.
Topic: monitoring and maintenance
Which signals are useful for maintaining Azure Databricks workloads?
Best answer: A
Explanation: Data-engineering operations need workload health, quality, performance, and cost signals. These guide maintenance and optimization.
What this tests: Selecting useful Databricks operational metrics.
Topic: SQL and Python roles
A team uses SQL for transformations and Python for custom processing. What should the engineer ensure?
Best answer: B
Explanation: Databricks commonly supports mixed-language workloads. The operational design should handle dependencies, testing, and permissions consistently.
What this tests: Managing multi-language Databricks workflows.
Topic: route comparison
A learner wants Microsoft-specific Azure Databricks validation rather than the vendor-neutral Databricks certification route. Which page is closer?
Best answer: C
Explanation: DP-750 is the Microsoft Azure Databricks route. Databricks vendor exams may still be useful, but the issuer and scope differ.
What this tests: Distinguishing Microsoft and Databricks certification routes.
Topic: security boundary
A pipeline needs to read from Azure storage without embedding account keys in notebooks. What should be preferred?
Best answer: D
Explanation: Production data pipelines should avoid embedded secrets and use controlled identity and credential management patterns.
What this tests: Securing data access from Databricks workloads.
Use this map to connect the sample questions to the decision pattern Microsoft usually tests for this route.
flowchart LR
S1["Ingest source data"] --> S2
S2["Transform with notebooks and jobs"] --> S3
S3["Store governed lakehouse data"] --> S4
S4["Optimize pipelines and clusters"] --> S5
S5["Secure workspace access"] --> S6
S6["Monitor quality and cost"]
| Cue | What to remember |
|---|---|
| Lakehouse flow | Connect ingestion, transformation, quality checks, and curated tables into a maintainable pipeline. |
| Compute | Choose clusters, jobs, and autoscaling settings based on workload shape and cost. |
| Data quality | Validate schemas, handle bad records, and track pipeline failures. |
| Governance | Use permissions, catalog controls, secrets, and workspace boundaries carefully. |
| Optimization | Watch partitioning, file sizes, caching, job retries, and query performance. |
Use this page to review DP-750 sample questions and use the Notify me form for updates. The related pages below help you compare adjacent IT Mastery Azure Databricks practice options before choosing what to study next.