- A
Set max_categorical_features=100 in the model options.
Why wrong: max_categorical_features is not a parameter in BigQuery ML linear regression.
- B
Use TRANSFORM clause with ML.FEATURE_CROSS or manual hashing.
TRANSFORM allows custom feature engineering including hashing for high-cardinality features.
- C
Use the OPTIONS(ENCODE='ONE_HOT_ENCODING') parameter in the model options.
Why wrong: ENCODE is not a valid model option in BigQuery ML.
- D
The model automatically handles high-cardinality features without any additional steps.
Why wrong: BigQuery ML automatically handles low-cardinality features but may not scale for 1000+ categories; manual handling is recommended.
Question 386 of 1,000
PDE Preparing and Using Data for Analysis Practice Question
This PDE practice question tests your understanding of preparing and using data for analysis. 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 data engineer wants to train a linear regression model in BigQuery ML to predict sales. The training data includes a categorical feature with 1000+ unique values. Which method is most appropriate to handle this feature in the CREATE MODEL statement?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use TRANSFORM clause with ML.FEATURE_CROSS or manual hashing.
BigQuery ML automatically one-hot encodes categorical features with fewer than a threshold of unique values. For high-cardinality features, you can use TRANSFORM to apply feature engineering like hashing or bucketizing.
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.
- ✗
Set max_categorical_features=100 in the model options.
Why it's wrong here
max_categorical_features is not a parameter in BigQuery ML linear regression.
- ✓
Use TRANSFORM clause with ML.FEATURE_CROSS or manual hashing.
Why this is correct
TRANSFORM allows custom feature engineering including hashing for high-cardinality features.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the OPTIONS(ENCODE='ONE_HOT_ENCODING') parameter in the model options.
Why it's wrong here
ENCODE is not a valid model option in BigQuery ML.
- ✗
The model automatically handles high-cardinality features without any additional steps.
Why it's wrong here
BigQuery ML automatically handles low-cardinality features but may not scale for 1000+ categories; manual handling is recommended.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Preparing and Using Data for Analysis — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Preparing and Using Data for Analysis — This question tests Preparing and Using Data for Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use TRANSFORM clause with ML.FEATURE_CROSS or manual hashing. — BigQuery ML automatically one-hot encodes categorical features with fewer than a threshold of unique values. For high-cardinality features, you can use TRANSFORM to apply feature engineering like hashing or bucketizing.
What should I do if I get this PDE question wrong?
Identify which PDE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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