easyMultiple Choice
PDE Practice Question: A data engineer notices that Spark jobs on the…
Exhibit
Refer to the exhibit.
```
# gcloud dataproc clusters describe output
clusterName: my-cluster
config:
softwareConfig:
imageVersion: '2.0-debian10'
gceClusterConfig:
zoneUri: projects/my-project/zones/us-central1-a
internalIpOnly: false
masterConfig:
machineTypeUri: n1-standard-4
numInstances: 1
workerConfig:
machineTypeUri: n1-standard-4
numInstances: 10
preemptibility: ON
secondaryWorkerConfig:
numInstances: 0
status:
state: RUNNING
```A data engineer notices that Spark jobs on the Dataproc cluster shown often fail with executor lost errors. What is the most likely reason?
⚠ Common exam trap
The trap here is that candidates may overlook the 'all 10 workers are preemptible' detail and instead focus on common misconfigurations like single-zone risk or autoscaling, but the explicit mention of preemptible VMs is the key indicator of frequent, unpredictable executor loss.
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
✓
All 10 workers are preemptible and can be reclaimed by Compute Engine at any time.
Preemptible VMs in Google Compute Engine can be terminated at any time due to resource contention or other factors, with only 30 seconds notice. If all 10 worker nodes are preemptible, Spark executors running on them will be frequently lost, causing job failures. This is the most direct cause of 'executor lost' errors in a Dataproc cluster.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
All 10 workers are preemptible and can be reclaimed by Compute Engine at any time.
Why this is correct
Preemptible VMs are reclaimed by Compute Engine within 24 hours, and typically much sooner under capacity pressure. With all 10 workers preemptible, any reclamation kills executors mid-task, producing the executor lost failures described. The stem's constraint — every worker being preemptible — removes any stable capacity to absorb those losses.
- ✗
The master node has only 4 vCPUs, which may be insufficient for job coordination.
Why it's wrong here
Executor lost errors originate from worker containers dying, not from the master node's vCPU count, which governs driver and resource-negotiation capacity rather than executor survival. It is tempting because undersized masters do cause scheduling slowness, so a small master looks like a plausible bottleneck.
- ✗
The cluster is in a single zone, so a zone failure could cause all workers to shut down.
Why it's wrong here
A single-zone layout only matters during an actual zone outage; executor lost errors recurring during normal job execution point to worker resource exhaustion or preemption instead. It is tempting because zone redundancy is a genuine resilience concern, so single-zone clusters look fragile.
- ✗
Autoscaling is enabled and scaling down is causing workers to be removed during job execution.
Why it's wrong here
Autoscaling down removes workers mid-job, but Dataproc's graceful decommissioning drains running tasks first, so executor lost errors more likely stem from insufficient memory or disk on workers. It is tempting because scaling events do terminate nodes, and would be correct if decommissioning were disabled.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.