hardMultiple Select
PDE Practice Question: A company uses Cloud Dataproc for large-scale…
A company uses Cloud Dataproc for large-scale Spark jobs. They notice that some jobs are failing due to insufficient memory on the worker nodes. They want to improve memory management without over-provisioning. Which three configurations should they apply? (Choose 3)
⚠ Common exam trap
Google Cloud often tests the distinction between memory management and storage optimization, so candidates mistakenly choose local SSDs (option D) thinking they help with memory, when in fact they only improve disk I/O for shuffle operations.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set spark.executor.memory to a value that fits within the node memory
Setting spark.executor.memory to a value that fits within the node memory ensures that each executor does not exceed the available RAM on a worker node, preventing out-of-memory (OOM) errors. This configuration directly controls the heap size allocated to each executor, and when combined with spark.executor.cores and spark.executor.instances, it allows precise memory budgeting per node. Over-provisioning is avoided by calculating the maximum safe executor memory as (node memory - OS overhead - HDFS cache) / number of executors per node.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set spark.executor.memory to a value that fits within the node memory
Why this is correct
Prevents out-of-memory errors by ensuring executor memory fits worker capacity.
- ✓
Enable Spark dynamic allocation
Why this is correct
Dynamic allocation adjusts executors based on workload, improving resource utilization.
- ✓
Use custom machine types with high memory ratios
Why this is correct
Custom machines with more memory per CPU reduce memory pressure.
- ✗
Use local SSDs for temporary storage
Why it's wrong here
Local SSDs improve disk I/O, not memory management.
- ✗
Use preemptible worker nodes for volatile tasks
Why it's wrong here
Preemptible nodes are cost-effective but can be terminated; they don't improve memory management.
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