easyMultiple Choice
PDE Practice Question: A data engineer needs to process a large dataset…
A data engineer needs to process a large dataset (500 TB) stored in Cloud Storage using Dataproc. The processing job requires reading the entire dataset and writing results back to Cloud Storage. The job is expected to run for 6 hours. Which configuration minimizes cost?
⚠ Common exam trap
Google Cloud often tests the misconception that local SSDs always improve performance for data processing jobs, but in Dataproc, when data resides in Cloud Storage, the bottleneck is network throughput, not local disk speed, making SSDs an unnecessary cost.
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 a cluster with a mix of standard and preemptible VMs.
Preemptible VMs cost about 80% less than standard VMs, and mixing them with standard VMs provides fault tolerance for the job's 6-hour duration. Since the job reads and writes to Cloud Storage (not local HDFS), local SSDs are unnecessary, and a single-node cluster would lack the parallelism needed to process 500 TB efficiently within 6 hours. Using a mix of standard (for critical master/worker nodes) and preemptible VMs (for worker nodes) minimizes cost while ensuring job completion.
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 with standard VMs.
Why it's wrong here
A single standard VM cannot parallelise 500 TB within six hours; the job would run far longer, and Dataproc's one-hour minimum billing makes the extended runtime costly. It is tempting because single-node clusters suit small datasets or development testing, and that would be correct when data volume is modest and cluster startup overhead dominates.
- ✗
Use a cluster with local SSDs for faster I/O.
Why it's wrong here
Local SSDs add per-GB and per-instance charges while the job reads from and writes to Cloud Storage, so the data never resides on those disks. It is tempting because local SSDs accelerate shuffle-heavy Spark workloads, and they would be correct when intermediate shuffle data or temporary scratch files dominate I/O rather than GCS reads and writes.
- ✓
Use a cluster with a mix of standard and preemptible VMs.
Why this is correct
Preemptible VMs cost substantially less than standard VMs, and mixing them with standard workers keeps the six-hour job running while cutting compute spend. Since the workload is fault-tolerant batch processing, preemption risk is acceptable, minimising cost.
- ✗
Use a cluster with n1-highmem-32 instances and 1000 cores.
Why it's wrong here
High-memory instances and 1000 cores provision capacity far beyond what a six-hour batch job needs, and Dataproc bills per vCPU-hour, so the oversized cluster inflates cost. It is tempting because memory-intensive workloads such as large in-memory joins genuinely benefit from highmem shapes, making them correct when the job is memory-bound rather than throughput-bound.
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