hardMultiple ChoiceObjective-mapped
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
TPUs are for training, not prediction.
- ✗
Use a smaller machine type (e.g., n1-standard-4) to reduce cost
Why it's wrong here
Insufficient memory may cause job failures.
- ✓
Use preemptible VMs with a machine type that meets memory requirements
Why this is correct
Preemptible VMs are much cheaper and restartable, suitable for batch jobs.
- ✗
Use standard VMs and reduce parallelization
Why it's wrong here
Standard VMs are more expensive and reducing parallelization may increase run time.
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