**Steps**: (1) Workflows > Runs; find failed run. (2) Task tree—identify failed task (red). (3) Logs—stdout, stderr, notebook output. (4) Cluster events if task never started. (5) Reproduce—run notebook with same parameters. (6) Job config—cluster, libraries, params. (7) Delta...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like TCS. While less common, it tests deeper understanding that distinguishes strong candidates.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
Steps: (1) Workflows > Runs; find failed run. (2) Task tree—identify failed task (red). (3) Logs—stdout, stderr, notebook output. (4) Cluster events if task never started. (5) Reproduce—run notebook with same parameters. (6) Job config—cluster, libraries, params. (7) Delta history for data issues.
Why Systematically: Random checks waste time. Logs first; config second; data third.
Scalability Trade-offs: Large logs; use log delivery to S3 for retention. Alerts on failure reduce manual triage.
Cost Implications: Fast triage = less engineer time. Structured logging and dbutils.notebook.exit() for programmatic checks.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.