A data engineer is using scikit-learn's `SimpleImputer` to impute missing values in a dataset with three columns: 'category' (categorical), 'normal_num' (normally distributed), and 'skewed_num' (right-skewed). The engineer wants to apply the most appropriate imputation strategy for each column. Which approach is correct?

Use three separate SimpleImputer instances: most_frequent for category, mean for normal_num, and median for skewed_num

Use one SimpleImputer with strategy='median' for all columns

Use one SimpleImputer with strategy='most_frequent' for all columns

Use one SimpleImputer with strategy='mean' for all columns

A data engineer is cleaning a dataset with three columns: 'segment' (categorical: A, B, C), 'score' (continuous, normally distributed), and 'time_spent' (continuous, left-skewed). Each column has missing values. The engineer wants to minimize bias in the imputed values. Which imputation strategy is most appropriate for each column?

Median for segment, mean for score, mode for time_spent

Mode for segment, median for score, mean for time_spent

Mean for segment, mode for score, median for time_spent

Mode for segment, mean for score, median for time_spent

DatabricksCertified Machine Learning Associate

Domain 2Objective 6

Impute Missing Values with the Mode, Mean, or Median Value 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 12 practice questions to prepare you well beyond it. (estimate)

12questions here

3free pages

6concepts

Questions 6–10

6foundation · easy
Which pandas method provides a concise summary of a DataFrame that includes the count of non-null values per column, helping to identify columns with missing values?
Select an answer first
7foundation · easy
A data scientist suspects a DataFrame contains missing values. Which pandas method returns a Boolean mask of the same shape as the DataFrame, with True where a value is missing?
Select an answer first
8foundation · easy
In pandas, which method is used to replace missing values with a specified value or computed statistic?
Select an answer first
9expert · hard
A data engineer is using scikit-learn's `SimpleImputer` to impute missing values in a dataset with three columns: 'category' (categorical), 'normal_num' (normally distributed), and 'skewed_num' (right-skewed). The engineer wants to apply the most appropriate imputation strategy for each column. Which approach is correct?
Select an answer first
10expert · hard
A data engineer is cleaning a dataset with three columns: 'segment' (categorical: A, B, C), 'score' (continuous, normally distributed), and 'time_spent' (continuous, left-skewed). Each column has missing values. The engineer wants to minimize bias in the imputed values. Which imputation strategy is most appropriate for each column?
Select an answer first

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Impute Missing Values with the Mode, Mean, or Median Value MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 2)

Questions 6–10

Which pandas method provides a concise summary of a DataFrame that includes the count of non-null values per column, helping to identify columns with missing values?

A data scientist suspects a DataFrame contains missing values. Which pandas method returns a Boolean mask of the same shape as the DataFrame, with True where a value is missing?

In pandas, which method is used to replace missing values with a specified value or computed statistic?

Keep studying — Section 2: Data Processing