Databricks DE-ASSOC 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 Databricks Data Engineer Associate (DE-ASSOC) topics such as ingestion, Delta tables, Lakeflow pipelines, production jobs, Unity Catalog, lineage, and sharing. The prompts emphasize Databricks-native platform decisions rather than generic Spark memorization.

Where these questions fit in the DE-ASSOC guide

The sample set below is part of the Databricks DE-ASSOC guide path:

• Databricks certification hub
• DE-ASSOC exam guide
• DE-ASSOC cheat sheet
• DE-ASSOC study plan
• DE-ASSOC FAQ
• DE-ASSOC resources
• DE-ASSOC glossary

DE-ASSOC data engineering sample questions

Work through each prompt before opening the explanation. DE-ASSOC questions usually reward answers that make ingestion, transformation, pipeline operation, and governance repeatable, observable, and platform-native.

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Question 1

Topic: Incremental file ingestion

A data engineering team receives new JSON files every few minutes in cloud storage. They need reliable incremental ingestion into Delta tables, schema handling, and production-friendly recovery after failures. Which Databricks pattern is strongest?

• A. List the storage path manually in a notebook and append all files every morning.
• B. Use Auto Loader or a Lakeflow ingestion pattern that tracks discovered files, handles schema evolution intentionally, and writes to Delta.
• C. Copy files into a local driver folder before reading them into Spark.
• D. Load the same path with a one-time CSV reader because JSON and CSV ingestion behave the same.

Best answer: B

Explanation: Incremental cloud-file ingestion is a core Auto Loader and Lakeflow use case. The key clues are continuous file arrival, recovery, schema handling, and Delta as the managed storage target.

Why the other choices are weaker:

• A risks duplicate work, missed files, and weak failure recovery.
• C is not a scalable Databricks ingestion pattern.
• D ignores format, schema, and incremental-processing requirements.

What this tests: Auto Loader, Lakeflow, incremental ingestion, schema evolution, and Delta write patterns.

Related topics: Auto Loader; Lakeflow; Delta Lake; Ingestion

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Question 2

Topic: Bronze to silver transformation

A pipeline stores raw events in a bronze table. Downstream analysts need a cleaned table with parsed timestamps, deduplicated records, and standardized column names while preserving the raw landing history. What design best matches the medallion pattern?

• A. Overwrite the bronze table with cleaned records so only one table is needed.
• B. Give analysts direct access to the raw JSON files and let each dashboard clean the data separately.
• C. Create a silver table from bronze using deterministic transformations, quality checks, and clear lineage from raw to cleaned data.
• D. Move the raw files into an archive and rebuild all reports from local extracts.

Best answer: C

Explanation: The medallion pattern separates raw preservation from cleaned, validated, analysis-ready tables. Bronze remains the raw landing layer; silver adds structure and quality.

Why the other choices are weaker:

• A destroys the raw recovery and audit layer.
• B duplicates transformation logic and weakens consistency.
• D moves away from governed, repeatable lakehouse workflow.

What this tests: medallion architecture, bronze and silver responsibilities, transformations, quality checks, and lineage.

Related topics: Medallion; Bronze; Silver; Lineage

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Question 3

Topic: Production job failure

A scheduled Databricks workflow failed after one task timed out. Upstream tasks completed successfully and their outputs are valid. The team wants to recover quickly without rerunning every successful task. What is the best operational response?

• A. Delete all outputs and manually run every notebook in the workspace.
• B. Disable job alerts because the next run might succeed.
• C. Increase the cluster size for all jobs without inspecting task-level failure details.
• D. Use the workflow repair or rerun capability for the failed task path after checking logs and dependencies.

Best answer: D

Explanation: Production workflow operations are task-aware. Repairing or rerunning only the failed path after reviewing logs preserves completed work and targets the actual failure.

Why the other choices are weaker:

• A is slow and risks unnecessary reprocessing.
• B hides operational risk instead of fixing it.
• C guesses at capacity without evidence from the failed task.

What this tests: workflows, task dependencies, repair runs, logs, and production pipeline operations.

Related topics: Workflows; Jobs; Repair run; Operations

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Question 4

Topic: Governing shared tables

Multiple teams need access to curated sales tables. The platform team must centralize permissions, provide lineage, and avoid each workspace maintaining its own disconnected access rules. Which Databricks capability is the best anchor?

• A. Unity Catalog with catalogs, schemas, table permissions, lineage, and governed sharing patterns.
• B. Notebook-level comments that describe who should use each table.
• C. Local files copied to each team’s cluster driver.
• D. One shared personal access token embedded in every notebook.

Best answer: A

Explanation: Unity Catalog is the Databricks governance layer for structured objects, permissions, lineage, and sharing. The stem is about governed access, not informal documentation.

Why the other choices are weaker:

• B documents intent but does not enforce access.
• C bypasses centralized governance and lineage.
• D is insecure and destroys accountability.

What this tests: Unity Catalog, catalogs and schemas, permissions, lineage, sharing, and access governance.

Related topics: Unity Catalog; Governance; Lineage; Permissions

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