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PMLE Scaling Prototypes into ML Models Practice Question

Your PyTorch training script uses DistributedDataParallel (DDP) across 4 vertices each with 4 GPUs (16 GPUs total). You submit a Vertex AI custom training job. How should you configure the worker pool spec?

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

✓

Create one worker pool with 4 replicas, each with machine type having 4 GPUs

For DDP across multiple machines, use MultiWorkerMirroredStrategy equivalent in PyTorch: set replicas to 4, each with machine type having 4 GPUs. The TF_CONFIG env var is not needed; Vertex AI sets necessary environment variables for distributed training.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • ✓

    Create one worker pool with 4 replicas, each with machine type having 4 GPUs

    Why this is correct

    This matches the requirement: 4 workers, each with 4 GPUs.

  • ✗

    Create a chief worker pool with 1 replica (4 GPUs) and a parameter server pool with 4 replicas (no GPUs)

    Why it's wrong here

    DDP does not use parameter servers; this is for TensorFlow with PS strategy.

  • ✗

    Create 4 separate jobs, each with 1 replica and 4 GPUs

    Why it's wrong here

    Separate jobs are not coordinated; DDP requires a single job with multiple workers.

  • ✗

    Create one worker pool with 16 replicas, each with 1 GPU

    Why it's wrong here

    This would create 16 single-GPU nodes, not 4x4.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This PMLE 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 PMLE exam.