A feature has a heavily right-skewed distribution. How does a log transformation typically affect the shape of this distribution?

It increases the skewness by amplifying the tail

It makes the distribution more symmetric and closer to normal

It has no effect on the shape of the distribution

It converts the distribution to a uniform distribution

Why does log transformation reduce the impact of outliers on model training?

Because it removes outliers from the dataset entirely

Because it converts outliers to negative values

Because it compresses large values, making extreme observations less dominant

Because it assigns lower weights to outlier observations

How can log transformation improve the performance of a linear regression model?

By making the relationship between features and target more linear

By removing the need for feature scaling

By converting the model to a non-linear model

By increasing the number of features in the model

What is the primary purpose of applying a log transformation to a feature with a wide range of values?

To expand the range so that small differences become more visible

To compress the range and reduce the influence of extreme values

To convert categorical data into numerical data

DatabricksCertified Machine Learning Associate

Domain 2Objective 9

Identify Scenarios Where Log Scale Transformation Is Appropriate MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 2)

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 6–10

6foundation · easy
A feature has a heavily right-skewed distribution. How does a log transformation typically affect the shape of this distribution?
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7foundation · easy
Why does log transformation reduce the impact of outliers on model training?
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8foundation · easy
How can log transformation improve the performance of a linear regression model?
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9foundation · easy
What is the primary purpose of applying a log transformation to a feature with a wide range of values?
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10foundation · easy
A model predicts log(y) for a house price y. The model outputs a value of 12.5 for a particular house. How should this prediction be converted to the original price scale?
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