Objective 4.2
PMLEScaling online model serving
Objective 4.2 sits in Serving and scaling models, which carries 20% 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.
A team wants GPU accelerated online serving for an XGBoost model exported from training. What does the accelerator rule say?
Correct.
Concept
An accelerator is only usable when the serving code knows how to reach it. A framework whose runtime has no device path gains nothing from attached hardware, so the platform refuses the combination outright.
Why A
You can only use GPUs when the model resource is based on a TensorFlow SavedModel, or when you use a custom container designed to take advantage of GPUs. GPUs cannot be used for scikit-learn or XGBoost models.
Source
Configure compute resources for inference, Agent Platform documentation, checked August 2026You can only use GPUs when your Model resource is based on a TensorFlow SavedModel , or when you use a custom container that has been designed to take advantage of GPUs. You can't use GPUs for scikit-learn or XGBoost models.
Now you: objective 4.2 questions
No account needed. The explanation opens when you answer.
Sample question 1 of 3
A team benchmarks a model on a single core machine type and the result looks worse than expected. What does the platform reserve on each replica?
Sample question 2 of 3
A team assumes the cheapest machine type per hour will give the cheapest serving bill. What does the cost guidance point out?
Sample question 3 of 3
An engineer wants to mix two accelerator models behind one deployed model to spread capacity around. What does the rule allow?
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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.