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Google ACE Practice Question: A company runs a big data processing pipeline on…
A company runs a big data processing pipeline on a Dataproc cluster. To reduce costs, they use a primary cluster with one master node (standard) and 20 worker nodes all using preemptible VMs. Recently, jobs running during peak business hours are failing with 'Task failed' errors. You notice that many preemptible VMs are reclaimed during the middle of these jobs. The jobs are long-running MapReduce tasks that write intermediate results to the cluster's HDFS. What should you do to improve job reliability without significantly increasing costs?
⚠ Common exam trap
Google Cloud often tests the misconception that simply adding more preemptible nodes or upgrading instance types will solve reliability issues, when the real solution is to configure graceful decommissioning to handle preemption gracefully.
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
✓
Enable graceful decommissioning for the preemptible instances.
Enabling graceful decommissioning for preemptible instances allows YARN to handle node loss more gracefully. When a preemptible VM is reclaimed, YARN can wait for running containers to finish before shutting down the node, reducing task failures. This improves job reliability without adding cost, as it leverages existing preemptible VMs more effectively.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable graceful decommissioning for the preemptible instances.
Why this is correct
Enable graceful decommissioning so Dataproc marks a preempted node as decommissioning before shutting it down. YARN then stops placing new containers on that node and waits up to `dataproc:yarn.preemptible.graceful-decommission.timeout` for in-flight tasks to finish or spill shuffle data elsewhere. This prevents sudden task loss because YARN's NodeManager is given time to complete ongoing work instead of being killed immediately.
- ✗
Increase the number of preemptible worker nodes to 40.
Why it's wrong here
Simply increasing preemptible workers to 40 does not alter how each VM reacts to a preemption event; every preemptible VM is still terminated by Compute Engine after a short notice. The cluster gets more parallelism, but also more nodes that can fail at once, and with no graceful shutdown, each preemption kills all containers running on that node. This is a horizontal scaling change, not a fault-tolerance fix.
- ✗
Use a higher preemptible instance type (e.g., n1-highmem-2 instead of n1-standard-2).
Why it's wrong here
Upgrading from n1-standard-2 to n1-highmem-2 only changes the amount of memory and CPU available per worker; it does not change the VM's lifecycle category. Preemptible VMs of every machine type are reclaimed by Compute Engine whenever capacity is needed, so high-memory workers are just as susceptible to preemption. This choice might improve per-node job performance, but does nothing to handle the termination event gracefully.
- ✗
Switch to standard worker nodes with committed use discounts.
Why it's wrong here
Switching to standard worker nodes with committed-use discounts eliminates preemption risk because standard VMs run indefinitely unless you stop or delete them. However, it requires a 1- or 3-year commitment, raises the per-hour cost relative to preemptible VMs, and changes the cluster economics significantly. It also ignores that Dataproc's graceful decommissioning already solves the failure problem for preemptible workers, making this an unnecessarily expensive workaround.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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