Diagnosing cluster startup failures, library conflicts, and out-of-memory errors

Cluster failures fall into three buckets: startup issues (cloud resource limits, init scripts, network/permissions), library conflicts (dependency version clashes or scope mismatches), and out-of-memory errors (driver vs executor memory pressure). Diagnosis relies on reading cluster event logs, driver/executor logs, and Ganglia/metrics UI (or the newer cluster metrics tab) rather than guessing.

Must-know

• Cluster startup failures often show as 'Pending' then terminate; check the Event Log tab first for reasons like cloud provider quota limits, invalid instance types, or failed init scripts before checking logs.
• Init script failures are a common startup cause; script output/errors are captured in cluster logs (DBFS or cloud storage logging destination) and should be checked line by line.
• Library conflicts typically arise from mixing cluster-installed (init script/UI) libraries with notebook-scoped (%pip, %conda) libraries, or from incompatible versions across the same cluster; notebook-scoped installs affect only the attached notebook's REPL.
• Driver out-of-memory usually results from collect(), toPandas(), or broadcasting large datasets to the driver, which has limited memory compared to executors; the fix is to avoid pulling large data to the driver or increase driver node size.
• Executor OOM often stems from data skew, overly large partitions, or insufficient shuffle partitions; repartitioning, salting skewed keys, or increasing executor memory/nodes are standard remedies.
• The Spark UI (Storage, Executors, and SQL tabs) and cluster metrics are the primary tools to confirm memory pressure, spill to disk, or GC overhead before resizing a cluster or changing code.

Coverage checked against the published exam guide on Jul 26, 2026.

These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.