A feature contains both positive values and zeros. Which approach allows a log transformation to be applied?

Use log(x) and ignore the zeros

Use log(x) with negative values set to zero

Replace zeros with a very small positive number and use log(x)

Use log(1 + x) instead of log(x)

A data engineer is building a model to predict customer lifetime value (CLV). The CLV feature is heavily right-skewed, with a few customers having extremely high values. The model is a linear regression. Which statement best describes the benefit of applying a log transformation to the CLV feature?

It increases the range of the feature, making the model more sensitive to small changes.

It guarantees the model will have zero residuals.

It converts the feature to a normal distribution, which is required for linear regression.

It compresses the wide range and reduces the influence of extreme high-value customers.

A data scientist observes that a feature's histogram shows a peak near zero and a long tail extending toward large positive values. What does this indicate about the appropriateness of log transformation?

The data is uniform, so log transformation would have no effect

The data is right-skewed, so log transformation is likely appropriate

The data is symmetric, so log transformation would introduce skewness

The data is left-skewed, so log transformation is not appropriate

DatabricksCertified Machine Learning Associate

Domain 2Objective 9

Identify Scenarios Where Log Scale Transformation Is Appropriate MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 1)

Part of the Section 2: Data Processing domain, which makes up ~27% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~9–16 in this domain), expect 1–2 from this objective — we provide 10 practice questions to prepare you well beyond it. (estimate)

10questions here

2free pages

6concepts

Questions 1–5

1foundation · easy
Which mathematical expression correctly represents a log scale transformation applied to a feature value x?
Select an answer first
2foundation · easy
A feature contains both positive values and zeros. Which approach allows a log transformation to be applied?
Select an answer first
3application · medium
A data engineer is building a model to predict customer lifetime value (CLV). The CLV feature is heavily right-skewed, with a few customers having extremely high values. The model is a linear regression. Which statement best describes the benefit of applying a log transformation to the CLV feature?
Select an answer first
4foundation · easy
A data scientist observes that a feature's histogram shows a peak near zero and a long tail extending toward large positive values. What does this indicate about the appropriateness of log transformation?
Select an answer first
5foundation · easy
A data scientist wants to apply a log transformation to a feature that contains some zero values. Which form of the transformation is appropriate?
Select an answer first

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Identify Scenarios Where Log Scale Transformation Is Appropriate MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 1)

Questions 1–5

Which mathematical expression correctly represents a log scale transformation applied to a feature value x?

A feature contains both positive values and zeros. Which approach allows a log transformation to be applied?

A data engineer is building a model to predict customer lifetime value (CLV). The CLV feature is heavily right-skewed, with a few customers having extremely high values. The model is a linear regression. Which statement best describes the benefit of applying a log transformation to the CLV feature?

A data scientist observes that a feature's histogram shows a peak near zero and a long tail extending toward large positive values. What does this indicate about the appropriateness of log transformation?

A data scientist wants to apply a log transformation to a feature that contains some zero values. Which form of the transformation is appropriate?

Keep studying — Section 2: Data Processing