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Objective 3.3

PMLE

Choosing appropriate hardware for training

Objective 3.3 sits in Scaling prototypes into ML models, which carries 21% of the ML Engineer exam. The questions below are original, written from the official objective title above, and each explanation cites the Google Cloud page it rests on.

Objective title verbatim from the official objectives. Google Cloud exam page ↗

A worked example

Shown solved, with the whole explanation open: this is what every question here carries.

3-3Scaling prototypes into ML models

A team prototypes a small model quickly, with a small effective batch size and several custom C++ operations in the graph. Which hardware suits that?

TPUs, for their matrix throughputMatrix throughput needs a matrix heavy workload.
GPUs, for their operator coverageThat choice suits medium to large models instead.
CPUs, for their flexibilityCorrect · your answerCorrect. General purpose silicon carries odd code.
TPU slices, for scaling outScaling out does not help a prototype run.

Correct.

Concept

Accelerators pay off only when the work is the kind they were built for. Where the model is small, the batch is small, or the code carries operations the accelerator has never seen, general purpose silicon finishes sooner.

Why C

CPUs are indicated for quick prototyping that requires maximum flexibility, simple models that do not take long to train, small models with small effective batch sizes, models with many custom TensorFlow operations written in C++, and models limited by available I/O or host networking bandwidth.

Source

CPUs Quick prototyping that requires maximum flexibility Simple models that don't take long to train Small models with small, effective batch sizes Models that contain many custom TensorFlow operations written in C++ Models that are limited by available I/O or the networking bandwidth of the host system

Introduction to Cloud TPU documentation, checked August 2026
#gcp#hardware#cpu#ml-training

Now you: objective 3.3 questions

No account needed. The explanation opens when you answer.

Sample question 1 of 3

3-3Scaling prototypes into ML models

A team trains a medium sized model with a large effective batch and several custom PyTorch operations that partly run on the host. Which accelerator fits?

Sample question 2 of 3

3-3Scaling prototypes into ML models

An engineer profiles a workload full of element-wise algebra and frequent branching and asks whether it belongs on a TPU. What applies here?

Sample question 3 of 3

3-3Scaling prototypes into ML models

A team finds a model breaks on the second batch after the first batch ran correctly on a TPU. Which property explains that?

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Read the sources

These are the official pages the questions above cite. Reading them is studying the objective from the primary source, which is what the explanations point you toward anyway.

More objectives in Scaling prototypes into ML models