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.
A company uses AutoML Tables (Vertex AI AutoML for tabular data) to predict customer churn. Their dataset has 10,000 rows and 50 features. During training, they notice the model's performance is poor. Which approach is most likely to improve the model?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Enable automatic feature engineering transformations
AutoML can create new features from existing ones automatically.
Switch to BigQuery ML linear regression
Why wrong: Less flexible than AutoML for complex patterns.
Increase the training budget to 10 node hours
Why wrong: May not address root cause of poor feature representation.
Remove 20 features to reduce noise
Why wrong: Risk discarding informative features without analysis.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Enable automatic feature engineering transformations
AutoML Tables (Vertex AI AutoML for tabular data) includes automatic feature engineering transformations such as scaling, one-hot encoding, and feature cross creation. These transformations are essential for capturing non-linear relationships and interactions between features, which can significantly improve model performance when the default preprocessing is insufficient. Enabling this option directly addresses the poor performance by allowing the model to learn more complex patterns from the data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Enable automatic feature engineering transformations
Why this is correct
AutoML can create new features from existing ones automatically.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Switch to BigQuery ML linear regression
Why it's wrong here
Less flexible than AutoML for complex patterns.
Increase the training budget to 10 node hours
Why it's wrong here
May not address root cause of poor feature representation.
Remove 20 features to reduce noise
Why it's wrong here
Risk discarding informative features without analysis.
Common exam traps
Google Cloud often tests the misconception that increasing training budget or reducing features is a universal fix for poor model performance, when in fact the most impactful first step is to enable automatic feature engineering to let the model learn better representations from the data.
Detailed technical explanation
Under the hood, AutoML Tables uses neural architecture search (NAS) to find the best model architecture, but it relies on feature transformations to convert raw data into a format suitable for neural networks. Automatic feature engineering includes operations like log transforms, binning, and cross-column interactions (e.g., creating a feature for 'age * income'), which can capture domain-specific patterns that raw features miss. In a real-world churn scenario, a feature like 'total_usage / tenure' might be more predictive than either feature alone, and AutoML's automatic transformations can discover such combinations without manual effort.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Enable automatic feature engineering transformations — AutoML Tables (Vertex AI AutoML for tabular data) includes automatic feature engineering transformations such as scaling, one-hot encoding, and feature cross creation. These transformations are essential for capturing non-linear relationships and interactions between features, which can significantly improve model performance when the default preprocessing is insufficient. Enabling this option directly addresses the poor performance by allowing the model to learn more complex patterns from the data.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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