Quick Answer
The answer is to examine the data for outliers and missing values. This is the correct first step because a high RMSE in BigQuery ML linear regression almost always signals that the training data contains anomalies that disproportionately skew the model’s predictions, as linear regression assumes normally distributed residuals and is highly sensitive to extreme values. On the Google Professional Machine Learning Engineer exam, this question tests your ability to prioritize data quality over model tuning—a common trap is jumping straight to feature engineering or regularization, when the root cause is often dirty data. Remember the “Garbage In, Garbage Out” principle: before adjusting any hyperparameters, always validate your dataset’s integrity. A useful memory tip is “RMSE high? Check the data first—outliers and nulls are the worst.”
This PMLE practice question tests your understanding of architecting low-code ml solutions. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 retailer uses BigQuery ML to build a linear regression model for sales forecasting. The model's evaluation shows high RMSE. Which step should they take first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Use a more complex model like XGBoost
Why wrong: Wrong: Should first examine data; model complexity may not fix data issues.
Increase the number of features
Why wrong: Wrong: Could introduce noise; not a first step.
Set a larger training budget
Why wrong: Wrong: Budget is not a primary fix for high RMSE.
Examine the data for outliers and missing values
Correct: Data quality inspection is the first step.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Examine the data for outliers and missing values
High RMSE in a linear regression model often indicates issues with data quality, such as outliers or missing values, which can disproportionately skew the model's predictions. BigQuery ML's linear regression is sensitive to such anomalies, so examining and cleaning the data is the most appropriate first step before considering model complexity or feature engineering.
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.
Use a more complex model like XGBoost
Why it's wrong here
Wrong: Should first examine data; model complexity may not fix data issues.
Increase the number of features
Why it's wrong here
Wrong: Could introduce noise; not a first step.
Set a larger training budget
Why it's wrong here
Wrong: Budget is not a primary fix for high RMSE.
Examine the data for outliers and missing values
Why this is correct
Correct: Data quality inspection is the first step.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the misconception that high RMSE is always a model complexity issue, leading candidates to jump to advanced algorithms or feature engineering without considering fundamental data quality checks.
Detailed technical explanation
In BigQuery ML, linear regression uses ordinary least squares (OLS) estimation, which is highly sensitive to outliers because it minimizes the sum of squared residuals—a single extreme value can significantly inflate RMSE. Missing values in BigQuery ML are typically handled by dropping rows or using default imputation, which can introduce bias; manual inspection allows for more robust techniques like median imputation or domain-specific handling. A real-world scenario is retail sales data with seasonal spikes or data entry errors, where cleaning outliers first often reduces RMSE by 20-30% without any model changes.
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: Examine the data for outliers and missing values — High RMSE in a linear regression model often indicates issues with data quality, such as outliers or missing values, which can disproportionately skew the model's predictions. BigQuery ML's linear regression is sensitive to such anomalies, so examining and cleaning the data is the most appropriate first step before considering model complexity or feature engineering.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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