PDE Designing Data Processing Systems Practice Question
A company uses Dataproc to run daily Spark ML jobs. The jobs run for 2 hours each day. The team wants to reduce costs without changing job characteristics. Which strategy is MOST cost-effective?
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
✓
Use preemptible instances for worker nodes
Preemptible VMs are up to 80% cheaper and can handle job interruptions as Spark is fault-tolerant. Single-node is for testing, not production. High-availability is for long-running clusters with HA requirements. Standard nodes are more expensive.
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 a single-node cluster to eliminate overhead
Why it's wrong here
Single-node clusters are for development, not fault-tolerant production jobs.
- ✗
Enable high-availability mode to avoid restarts
Why it's wrong here
HA mode increases cost by adding redundant masters, not cost-saving.
- ✓
Use preemptible instances for worker nodes
Why this is correct
Preemptible instances are cheap and Spark handles preemptions via fault tolerance.
- ✗
Increase the number of standard workers to finish faster
Why it's wrong here
More nodes increase cost; the job already runs only 2 hours.
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