PMLE Scaling Prototypes into ML Models Practice Question
A data scientist has a TensorFlow 2.x model trained on a single GPU. They want to scale training to multiple GPUs on a single Vertex AI machine without code changes. Which strategy should they use?
⚠ Common exam trap
The trap is choosing MultiWorkerMirroredStrategy for a single-machine multi-GPU scenario — candidates confuse 'multiple GPUs' with 'multiple workers' and overlook that MirroredStrategy is the single-host default.
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
✓
MirroredStrategy
MirroredStrategy is TensorFlow's default single-machine, multi-GPU strategy. It replicates the model on each GPU and synchronizes gradients via all-reduce, and it can be enabled with minimal code changes (typically just wrapping model creation in strategy.scope()), making it the right choice for scaling on one Vertex AI machine.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
MultiWorkerMirroredStrategy
Why it's wrong here
MultiWorkerMirroredStrategy coordinates synchronous training across several machines, each with its own workers, so it targets multi-node clusters rather than GPUs within one host. It is correct for distributed multi-worker jobs. The stem confines scaling to a single Vertex AI machine, which MirroredStrategy covers.
- ✗
TPUStrategy
Why it's wrong here
TPUStrategy targets TensorFlow runtimes on Cloud TPU devices, not GPUs, so it cannot distribute across the machine's GPUs. It is the right choice when training on TPU pods or single TPU boards. The stem specifies multiple GPUs on one Vertex AI machine, which MirroredStrategy addresses.
- ✗
CentralStorageStrategy
Why it's wrong here
CentralStorageStrategy places variables on the CPU and performs computation on one GPU, so it cannot distribute work across multiple GPUs. It suits single-GPU or CPU-bound training where variables must stay off accelerators. The stem requires multi-GPU scaling on one machine, which MirroredStrategy handles.
- ✓
MirroredStrategy
Why this is correct
MirroredStrategy replicates the model across all GPUs on one machine using all-reduce synchronisation, distributing the existing single-GPU code across devices with no code changes. This satisfies the stem's single-machine, multi-GPU scaling requirement, unlike distributed strategies spanning multiple hosts.
Go deeper
Related to this question
About these practice questions
This PMLE question is part of Courseiva's 775-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
easy- A.tf.distribute.ParameterServerStrategy
- ✓ B.tf.distribute.MirroredStrategy
- C.tf.distribute.TPUStrategy
- D.tf.distribute.MultiWorkerMirroredStrategy
Why B: 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.
Variation 2. You need to run a distributed training job on Vertex AI using TensorFlow with MirroredStrategy on a single machine with 4 GPUs. Which training configuration should you use?
medium- ✓ A.Use MirroredStrategy with a single workerPoolSpec containing a machine_type with 4 GPUs
- B.Use MultiWorkerMirroredStrategy with multiple workerPools
- C.Use MirroredStrategy with two workerPoolSpecs, each with 2 GPUs
- D.Use ParameterServerStrategy with a chief and a parameter server
Why A: MirroredStrategy is TensorFlow's single-machine, multi-GPU strategy that replicates the model across all GPUs on one host using all-reduce for gradient synchronization. On Vertex AI, this maps to a single workerPoolSpec whose machine type has 4 GPUs attached. No additional workers are needed because MirroredStrategy operates within one machine.
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.