PMLE Scaling Prototypes into ML Models Practice Question
You have a TensorFlow training script that runs on a single machine. To speed up training on Vertex AI with 8 GPUs on a single machine, which strategy should you use?
⚠ Common exam trap
The trap is confusing single-machine multi-GPU (MirroredStrategy) with multi-machine multi-GPU (MultiWorkerMirroredStrategy) — candidates who skim the question miss the 'single machine' qualifier and pick the multi-worker variant.
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
✓
tf.distribute.MirroredStrategy
tf.distribute.MirroredStrategy is designed for synchronous, data-parallel training across multiple GPUs on a single machine. It replicates the model on each GPU, splits each batch across replicas, and uses all-reduce (via NCCL) to aggregate gradients, which is exactly the scenario described: 8 GPUs on one machine. This is the canonical strategy for single-node multi-GPU TensorFlow 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.
- ✗
tf.distribute.ParameterServerStrategy
Why it's wrong here
ParameterServerStrategy places variables on parameter servers and workers compute updates, a topology designed for asynchronous, multi-machine clusters. On one machine with eight GPUs it introduces unnecessary server-worker separation. It is tempting as a scaling pattern, but it suits large distributed clusters, not a single host's local devices.
- ✓
tf.distribute.MirroredStrategy
Why this is correct
MirroredStrategy performs synchronous, all-reduce data-parallel training across multiple GPUs within one machine, replicating the model on each device and aggregating gradients. This directly satisfies the stem's constraint of 8 GPUs on a single machine, where MultiWorkerMirroredStrategy would add unnecessary cross-machine networking overhead.
- ✗
tf.distribute.TPUStrategy
Why it's wrong here
TPUStrategy targets Tensor Processing Unit pods and their specific collective communication topology; it cannot drive NVIDIA GPUs on a Vertex AI single machine. It is tempting because TPUs also accelerate training, but that requires TPU VMs. For eight local GPUs, MirroredStrategy replicates variables across the devices on that host.
- ✗
tf.distribute.MultiWorkerMirroredStrategy
Why it's wrong here
MultiWorkerMirroredStrategy coordinates gradients across multiple machines, each with its own workers, so it adds cross-host communication overhead without benefit on one host. It is tempting because it mirrors variables across workers, but that is for multi-node clusters; single-machine eight-GPU training needs MirroredStrategy, which mirrors across local devices only.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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