**DAG Shows**: (1) Stage boundaries—wide vs. narrow. (2) Task count and parallelism. (3) Shuffle read/write. (4) Skew—task duration variance. (5) Cached RDDs (green). (6) Stage execution time. (7) Data flow.
**Use For**: Bottlenecks (slowest stage), skew (one long task), unnecessary shuffles, optimization targets.
**Why Critical**: Without DAG, optimization is guesswork....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like PWC. The answer also includes follow-up discussion points that interviewers commonly explore.
Continue Reading the Full Answer
Unlock the complete expert answer with code examples, trade-offs, and pro tips - plus 1,863+ more.
Or upgrade to Platform Pro - $39
Engineers who used these answers got offers at
AmazonDatabricksSnowflakeGoogleMeta
According 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.