Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Merge conflicts in Databricks notebooks occur when multiple users edit the same notebook simultaneously or when Git sync detects...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like PWC. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Merge conflicts in Databricks notebooks occur when multiple users edit the same notebook simultaneously or when Git sync detects conflicting changes. Resolution steps: (1) Open the conflicted notebook and look for conflict markers (<<<<<<< HEAD, =======, >>>>>>> branch). (2) Manually edit to keep desired code from each version. (3) Remove conflict markers and save. Best practice: Use Databricks Repos with Git integration—create feature branches, make changes in isolation, and merge via pull requests to catch conflicts early. Enable 'Rebase' or 'Merge' strategy in workspace settings. Document resolution in commit messages. For production, establish a review process: one primary owner per notebook, use Databricks job orchestration for automated runs, and avoid editing production notebooks directly.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Merging without understanding both versions. Pro-Move: 'Feature branches; PR review; one owner per prod notebook.'
Some links below are affiliate links. If you buy through them we may earn a small commission at no extra cost to you — it helps keep DataEngPrep free.
According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.