The answer is to enable automatic feature engineering by leaving feature columns as raw data and to rely on AutoML’s recommended data split. This is correct because Vertex AI AutoML’s built-in split logic—typically an 80/10/10 train/validation/test ratio with stratification—prevents overfitting by ensuring the model is evaluated on truly unseen data, while automatic feature engineering transforms raw columns into optimal representations without manual intervention. On the Google Professional Machine Learning Engineer exam, this tests your understanding of low-code ML best practices, where a common trap is manually splitting data or pre-engineering features, which undermines AutoML’s automated optimization. Remember the mnemonic “RAW SPLIT”: leave features Raw and trust the AutoML Split—both reduce human error and align with the exam’s emphasis on scalable, hands-off pipelines.
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO are best practices for implementing a low-code ML solution using Vertex AI AutoML? (Choose 2)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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.
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI AutoML uses a neural architecture search (NAS) to find the optimal model architecture and applies automatic feature engineering (e.g., one-hot encoding, bucketization, cross features) based on the raw data. The recommended data split ensures that the validation and test sets are representative and not leaked into training, which is critical for reliable performance metrics. In a real-world scenario, using AutoML's default split avoids common pitfalls like data leakage from time-series or imbalanced datasets.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the AutoML recommended data split (train/validation/test) to avoid overfitting. — Option A is correct because 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.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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