hardMultiple Choice
PDE Practice Question: A company runs large batch prediction jobs on…
A company runs large batch prediction jobs on Vertex AI every day. They want to minimize costs while ensuring the jobs complete within a 4-hour window. The model requires significant memory. What is the most cost-effective approach?
⚠ Common exam trap
Google Cloud often tests the misconception that preemptible VMs are unreliable for any production workload, but the trap here is that batch prediction jobs are inherently fault-tolerant and can leverage preemptible VMs to drastically reduce costs without violating the completion window.
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 VMs with a machine type that meets memory requirements
Preemptible VMs (now called Spot VMs) are significantly cheaper than standard VMs (up to 60-80% discount) and are ideal for fault-tolerant batch prediction jobs that can handle interruptions. Since the job has a 4-hour window and the model requires significant memory, using preemptible VMs with a machine type that meets the memory requirements minimizes cost while allowing the job to complete if restarted within the time limit.
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 Cloud TPUs to accelerate predictions
Why it's wrong here
Cloud TPUs target training and high-throughput tensor workloads, and Vertex AI batch prediction on TPUs does not address the model's significant memory requirement or guarantee the four-hour window at lower cost. It is tempting because TPUs accelerate large-scale matrix computation, and would be correct for cost-efficient distributed training of compatible models.
- ✗
Use a smaller machine type (e.g., n1-standard-4) to reduce cost
Why it's wrong here
An n1-standard-4 provides only 15 GB of memory, insufficient for a model requiring significant memory, so the job would fail or spill rather than complete within four hours. It is tempting because shrinking machine size is the obvious cost lever, and would be correct for lightweight models with modest memory footprints.
- ✓
Use preemptible VMs with a machine type that meets memory requirements
Why this is correct
Preemptible VMs cost substantially less than standard VMs and suit batch workloads that tolerate interruption. Pairing them with a machine type meeting the model's memory requirement keeps the daily job within the four-hour window at minimal cost.
- ✗
Use standard VMs and reduce parallelization
Why it's wrong here
Standard VMs with reduced parallelisation lengthen runtime, risking breach of the four-hour window while offering no memory advantage over Vertex AI's managed batch prediction options. It is tempting because fewer parallel workers lowers compute spend, and would be correct when jobs are not time-constrained and cost dominates.
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