Fabric DP-700 Sample Questions with Explanations

These original sample questions are designed to help you check how the exam topics appear in decision-style prompts. They are not taken from the live exam.

Use these sample questions as a guided self-assessment for Microsoft Certified: Fabric Data Engineer Associate (DP-700) topics such as ingestion, transformation, lakehouse and warehouse design, orchestration, quality checks, security, governance, performance, and cost. Each prompt asks you to choose a data engineering decision rather than memorize a tool name.

Where these questions fit in the DP-700 guide

The sample set below is part of the Microsoft DP-700 guide path:

• Microsoft certification hub
• DP-700 exam guide
• DP-700 cheat sheet
• DP-700 study plan
• DP-700 FAQ
• DP-700 resources
• DP-700 glossary

DP-700 data engineering sample questions

Work through each prompt before opening the explanation. DP-700 rewards clean data flow: ingest, validate, transform, govern, serve, monitor, and optimize.

---

Question 1

Topic: Designing a curated lakehouse layer

A retail team lands daily sales files into OneLake. Analysts need raw replay capability, cleaned conformed tables, and a serving layer optimized for BI reports. The team also needs lineage between each layer. Which design is strongest?

• A. Load all files directly into the report dataset and overwrite it each morning.
• B. Use separate raw, curated, and serving layers in a lakehouse flow, with transformations and lineage tracked between layers.
• C. Create one warehouse table for every incoming file and let report authors join them manually.
• D. Store the source files in a folder and ask analysts to connect directly to the files from reports.

Best answer: B

Explanation: The scenario requires replay, cleansing, conformance, serving performance, and lineage. A staged lakehouse design keeps raw data recoverable, separates transformation responsibilities, and gives BI consumers a stable serving shape.

Why the other choices are weaker:

• A removes raw replay and hides transformation history.
• C creates fragmentation and shifts modeling burden to every report author.
• D skips data quality, schema management, and serving optimization.

What this tests: Choosing a layered Fabric data design that supports raw retention, curated transformation, serving, and lineage.

Related topics: Lakehouse; Medallion flow; Lineage; Serving layer

---

Question 2

Topic: Pipeline quality gate

A nightly pipeline loads supplier data into a curated table. If required columns are missing or row counts fall below an expected threshold, the team wants the run to stop before downstream semantic models refresh. What should the pipeline include?

• A. A data-quality validation step before downstream refresh, with failure handling and alerts.
• B. A larger compute capacity so the pipeline can complete faster.
• C. A manual email asking analysts to check reports the next morning.
• D. A report-level filter that hides missing supplier rows from users.

Best answer: A

Explanation: The requirement is an orchestration gate. Validating schema and row counts before refresh protects downstream assets from bad data and gives operators a clear failure signal.

Why the other choices are weaker:

• B changes performance but does not verify correctness.
• C catches issues too late and relies on manual inspection.
• D hides symptoms from reports instead of stopping a bad pipeline run.

What this tests: Applying quality checks, dependency control, and alerting to data pipeline orchestration.

Related topics: Data quality; Pipeline orchestration; Failure handling; Semantic model refresh

---

Question 3

Topic: Incremental processing decision

A fact table contains several billion rows. Only the most recent two days change after the initial load. Refreshing the entire table is causing capacity pressure and late report availability. Which approach best addresses the issue?

• A. Use incremental processing so only new or changed partitions are refreshed when possible.
• B. Run the full refresh more often so each run has less work.
• C. Duplicate the table into multiple workspaces and refresh all copies separately.
• D. Remove historical data from the model because older rows never matter.

Best answer: A

Explanation: When the change window is small, incremental processing reduces unnecessary work and protects capacity. The key is to refresh the changed portion while preserving historical data for reporting.

Why the other choices are weaker:

• B usually increases total work and does not change the inefficient pattern.
• C multiplies storage and refresh cost without solving the root issue.
• D may break historical reporting requirements and is not implied by the scenario.

What this tests: Recognizing when incremental refresh or partition-aware processing is the right optimization lever.

Related topics: Incremental refresh; Capacity pressure; Performance; Fact tables

---

Question 4

Topic: Securing a shared data product

A Fabric workspace contains engineering notebooks, raw customer files, curated tables, and a certified semantic model. Business users should consume reports and the certified model, but they should not edit notebooks or browse raw files. Which access pattern is strongest?

• A. Make all business users workspace admins so permission issues do not block them.
• B. Grant only the required item-level or role-based access to the certified assets, while keeping raw and engineering assets restricted.
• C. Email exported data files to business users after each refresh.
• D. Move all data into one table and rely on naming conventions to prevent misuse.

Best answer: B

Explanation: The access model should separate engineering workspace responsibilities from consumer access. Business users receive what they need to consume certified data products, while raw files and notebooks remain restricted.

Why the other choices are weaker:

• A violates least privilege and exposes authoring assets.
• C bypasses governance, lineage, and controlled access.
• D relies on convention instead of permissions and clear data-product boundaries.

What this tests: Applying Fabric security and governance boundaries around workspaces, items, raw data, and certified outputs.

Related topics: Security; Governance; Certified assets; Least privilege

Independent study note

Tech Exam Lexicon and IT Mastery are independent study tools. They are not affiliated with, endorsed by, or sponsored by Microsoft or any certification body.