The answer is to enable the weighted option in AutoML NLP to handle class imbalance. This built-in feature automatically adjusts the loss function so that misclassifications of minority classes—like the neutral and negative categories in your 80/10/10 split—are penalized more heavily, forcing the model to pay greater attention to those underrepresented examples. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of AutoML’s native capabilities versus custom workarounds; a common trap is to suggest oversampling or custom loss functions, which are unnecessary here because the weighted option directly addresses the class imbalance within the AutoML framework. The key insight is that AutoML NLP’s weighted option is the simplest, most effective lever for improving recall on minority classes without leaving the managed service. Memory tip: think “weighted = weighted penalty for minority misses” to recall that it’s a loss-function adjustment, not a data augmentation trick.
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.
A financial services firm uses Vertex AI AutoML Natural Language to classify customer feedback into categories (positive, neutral, negative). They notice that the model performs poorly on neutral and negative classes, with high false negatives for negative. The dataset has 10,000 samples: 8,000 positive, 1,000 neutral, 1,000 negative. They have trained the model with automatic data split and default hyperparameters. Which course of action should they take to improve classification of minority classes?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable the 'weighted' option in AutoML NLP to handle class imbalance.
Option B is correct because AutoML Natural Language provides a built-in 'weighted' option that automatically adjusts the loss function to penalize misclassifications of minority classes more heavily, directly addressing the class imbalance without requiring custom model development. This is the simplest and most effective way to improve recall for the neutral and negative classes within the AutoML framework.
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 a custom model with a weighted loss function.
Why it's wrong here
This requires custom code, violating the low-code approach.
✓
Enable the 'weighted' option in AutoML NLP to handle class imbalance.
Why this is correct
This built-in option adjusts weights for minority classes, improving performance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of training node hours.
Why it's wrong here
More node hours may help but do not specifically address class imbalance.
✗
Set the data split to 50/25/25 for train/validation/test.
Why it's wrong here
Data split does not mitigate class imbalance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that any class imbalance problem requires a custom model or manual data augmentation, when in fact AutoML's built-in 'weighted' option is the prescribed low-code solution for such scenarios.
Detailed technical explanation
How to think about this question
Under the hood, AutoML's 'weighted' option applies a class-weighting scheme during loss computation, typically using inverse class frequencies or a smoothed variant, so that the gradient updates from minority class samples have a larger impact on model parameters. This is analogous to cost-sensitive learning, where the loss for a misclassified negative sample is multiplied by a weight proportional to the ratio of majority to minority samples (e.g., 8,000/1,000 = 8). In practice, this technique often improves recall for minority classes by 10-20% without requiring manual threshold tuning.
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: Enable the 'weighted' option in AutoML NLP to handle class imbalance. — Option B is correct because AutoML Natural Language provides a built-in 'weighted' option that automatically adjusts the loss function to penalize misclassifications of minority classes more heavily, directly addressing the class imbalance without requiring custom model development. This is the simplest and most effective way to improve recall for the neutral and negative classes within the AutoML framework.
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.
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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