hardMultiple Select
PMLE Practice Question: Which TWO are best practices for implementing a…
Which TWO are best practices for implementing a low-code ML solution using Vertex AI AutoML? (Choose 2)
⚠ Common exam trap
Google Cloud often tests the misconception that manual preprocessing (like imputation or normalization) is required for AutoML, when in fact AutoML is designed to handle these steps automatically, and manual intervention can degrade performance or cause errors.
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
✓
Use the AutoML recommended data split (train/validation/test) to avoid overfitting.
AutoML's recommended data split (train/validation/test) is designed to prevent overfitting by ensuring the model is evaluated on unseen data. AutoML automatically handles the split ratio (e.g., 80/10/10) and stratification, which is a best practice for low-code ML solutions where manual split logic is error-prone.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the AutoML recommended data split (train/validation/test) to avoid overfitting.
Why this is correct
Why A is correct: AutoML optimizes split for best performance.
- ✗
Impute missing values manually before uploading the dataset.
Why it's wrong here
Why B is wrong: AutoML handles missing values automatically.
- ✗
Normalize numerical features to zero mean and unit variance.
Why it's wrong here
Why C is wrong: AutoML automatically normalizes features.
- ✓
Enable automatic feature engineering by leaving feature columns as raw data.
Why this is correct
Why D is correct: AutoML performs feature engineering automatically.
- ✗
Export the data and train a custom model with a different architecture.
Why it's wrong here
Why E is wrong: This defeats the purpose of low-code AutoML.
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 →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.