The answer is to add more complex features by including polynomial expansions. This is the correct choice because high bias in BigQuery ML logistic regression signals underfitting, where the model is too simplistic to capture the underlying patterns in the data. By introducing polynomial expansions—such as feature crosses in the TRANSFORM clause—you increase model complexity, enabling the linear logistic regression to learn non-linear decision boundaries and directly address the bias. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of the bias-variance tradeoff and how to diagnose underfitting from evaluation metrics like low training accuracy. A common trap is to add more training data or increase regularization, but those actions reduce variance, not bias. Remember the memory tip: “Bias is a sign your model is too shy—give it polynomial features to help it try.”
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.
Exhibit
CREATE OR REPLACE MODEL `mydataset.my_model`
TRANSFORM(
feature1,
feature2,
ML.IMPUTER(feature3) OVER (feature1) AS feature3_imputed,
ML.STANDARD_SCALER(feature4) OVER () AS feature4_scaled
)
OPTIONS(
model_type='logistic_reg',
input_label_cols=['label']
)
AS
SELECT * FROM `mydataset.mytable`
Refer to the exhibit. A data scientist runs the above BigQuery ML query to create a logistic regression model. After training, the model is evaluated using ML.EVALUATE. The evaluation shows poor performance with high bias. Which action would most likely improve the model's performance?
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.
CREATE OR REPLACE MODEL `mydataset.my_model`
TRANSFORM(
feature1,
feature2,
ML.IMPUTER(feature3) OVER (feature1) AS feature3_imputed,
ML.STANDARD_SCALER(feature4) OVER () AS feature4_scaled
)
OPTIONS(
model_type='logistic_reg',
input_label_cols=['label']
)
AS
SELECT * FROM `mydataset.mytable`
A
Remove the TRANSFORM clause and use raw features.
Why wrong: Removing the TRANSFORM clause would remove necessary preprocessing like imputation and scaling, which could worsen performance.
B
Change the model_type to 'linear_reg'.
Why wrong: Linear regression is for regression tasks, not classification, and would not be appropriate for a binary label.
C
Add more complex features by including polynomial expansions.
Polynomial expansions increase model complexity, allowing it to learn non-linear patterns from the data, which addresses high bias.
D
Increase the number of training iterations by setting MAX_ITERATIONS.
Why wrong: Increasing iterations may help with convergence but does not address the fundamental underfitting issue caused by the model's limited capacity.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add more complex features by including polynomial expansions.
High bias indicates the model is underfitting the data, meaning it is too simple to capture underlying patterns. Adding polynomial expansions (feature crosses) in the TRANSFORM clause increases model complexity, allowing the logistic regression to learn non-linear decision boundaries, which directly addresses underfitting.
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.
✗
Remove the TRANSFORM clause and use raw features.
Why it's wrong here
Removing the TRANSFORM clause would remove necessary preprocessing like imputation and scaling, which could worsen performance.
✗
Change the model_type to 'linear_reg'.
Why it's wrong here
Linear regression is for regression tasks, not classification, and would not be appropriate for a binary label.
✓
Add more complex features by including polynomial expansions.
Why this is correct
Polynomial expansions increase model complexity, allowing it to learn non-linear patterns from the data, which addresses high bias.
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.
✗
Increase the number of training iterations by setting MAX_ITERATIONS.
Why it's wrong here
Increasing iterations may help with convergence but does not address the fundamental underfitting issue caused by the model's limited capacity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between bias and variance; the trap here is that candidates might confuse high bias (underfitting) with high variance (overfitting) and incorrectly choose to simplify the model or increase iterations, rather than adding complexity.
Detailed technical explanation
How to think about this question
In BigQuery ML, the TRANSFORM clause allows you to apply feature engineering like polynomial expansion (e.g., ML.POLYNOMIAL_EXPAND) that creates interaction terms and higher-degree features. This is critical for logistic regression, which is inherently linear in the feature space; without such transformations, the model cannot capture non-linear relationships, leading to systematic underfitting (high bias). In practice, adding polynomial features must be balanced with regularization to avoid overfitting, but for high bias, increasing complexity is the correct first step.
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: Add more complex features by including polynomial expansions. — High bias indicates the model is underfitting the data, meaning it is too simple to capture underlying patterns. Adding polynomial expansions (feature crosses) in the TRANSFORM clause increases model complexity, allowing the logistic regression to learn non-linear decision boundaries, which directly addresses underfitting.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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