PMLE Scaling Prototypes into ML Models Practice Question
A machine learning engineer wants to use Vertex AI Vizier to tune three hyperparameters: learning rate (log scale), number of layers (integer), and optimizer (categorical). They have 50 parallel trials available. Which parameter specification types should they define?
⚠ Common exam trap
PMLE often tests the confusion between DISCRETE and CATEGORICAL parameter types, and between linear and log scales, causing candidates to pick specifications that do not match the hyperparameter's mathematical nature.
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
✓
learning_rate: DOUBLE (unit_log_scale), layers: INTEGER (unit_linear_scale), optimizer: CATEGORICAL
Vertex AI Vizier requires parameter specifications that match the nature of each hyperparameter. Learning rate is best explored on a logarithmic scale, so it should be a DOUBLE parameter with unit_log_scale. Number of layers is a discrete integer count, so it should be an INTEGER parameter with unit_linear_scale. Optimizer is a categorical choice among named algorithms, so it should be CATEGORICAL. This combination correctly reflects the mathematical and structural properties of each hyperparameter.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
learning_rate: CATEGORICAL, layers: INTEGER, optimizer: CATEGORICAL
Why it's wrong here
Learning rate is continuous, not categorical.
- ✓
learning_rate: DOUBLE (unit_log_scale), layers: INTEGER (unit_linear_scale), optimizer: CATEGORICAL
Why this is correct
Correct types and scales for the parameters.
- ✗
learning_rate: DOUBLE (unit_log_scale), layers: DOUBLE (unit_linear_scale), optimizer: DISCRETE
Why it's wrong here
Layers should be INTEGER, not DOUBLE; optimizer should be CATEGORICAL.
- ✗
learning_rate: DOUBLE (unit_linear_scale), layers: INTEGER (unit_linear_scale), optimizer: CATEGORICAL
Why it's wrong here
Linear scale for learning rate is incorrect; it should be log scale.
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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.