PDE Maintaining and Automating Data Workloads Practice Question
You are troubleshooting a Dataproc cluster that runs nightly Spark jobs. The jobs are failing with out-of-memory errors. You want to reduce costs while fixing the issue. Which combination of actions should you take? (Select the BEST answer.)
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
✓
Increase the number of preemptible worker nodes and use standard machine types for the master.
Preemptible workers are cheaper and can be used for worker nodes, but not for master nodes. Adding more preemptible workers can increase parallelism and reduce memory pressure per worker, but may cause more preemptions. Using high-memory master nodes is not necessary for worker memory issues. Using SSDs for scratch storage can improve performance but does not directly address OOM. Reducing worker count would exacerbate the problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SSD persistent disks for all nodes to improve I/O performance.
Why it's wrong here
SSDs improve I/O but do not increase memory. OOM is a memory issue, not I/O.
- ✗
Decrease the number of worker nodes and increase the size of each worker.
Why it's wrong here
This may increase cost if using larger machines, and decreasing parallelism might slow down jobs. It does not guarantee OOM resolution.
- ✗
Switch to a high-memory machine type for the master node only.
Why it's wrong here
OOM errors occur on workers, not the master. Increasing master memory does not resolve worker memory issues.
- ✓
Increase the number of preemptible worker nodes and use standard machine types for the master.
Why this is correct
More workers increase parallelism and memory capacity; preemptible workers are cost-effective. The master node remains standard to ensure stability.
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