What is the purpose of performing data quality checks before feature engineering?

To reduce the number of features in the dataset

To automatically generate new features

To ensure the data is accurate, complete, and consistent

To increase the size of the dataset

A data scientist is analyzing event logs from a web server. The 'timestamp' column is in ISO 8601 format with timezone offsets, e.g., '2023-05-01T12:34:56+02:00'. The scientist needs to compare event times across different timezones. What is the most appropriate approach?

Convert all timestamps to a common timezone (e.g., UTC) before analysis.

Keep the timestamps as strings and compare them lexicographically.

Remove the timezone offset from all timestamps.

Convert the timestamps to Unix time and then compare.

A data scientist is merging two customer tables. The merge results in duplicate rows because some customers appear in both tables with slightly different spellings of their names. The scientist wants to identify and handle these duplicates. Which approach is most appropriate?

Keep all rows and ignore the duplicates.

Use fuzzy matching on the name column to identify potential duplicates, then review them manually.

Replace all names with a standardized format and then remove duplicates.

Remove all rows with duplicate names using exact matching.

Why is it important to standardize date and time fields in a dataset?

To ensure consistent temporal analysis across records

To remove time zones from the data

To increase the number of date columns

To convert all dates to strings

A data scientist is preparing features for a k-nearest neighbors (KNN) model. The dataset includes a feature 'income' with a right-skewed distribution and a feature 'age' with a roughly normal distribution. The scientist wants to ensure that both features contribute equally to distance calculations. Which approach is most appropriate?

Remove outliers from 'income' and then apply min-max scaling to both.

Apply a log transformation to 'income', then standardize both features.

Apply min-max scaling to both features.

Standardize 'income' and apply min-max scaling to 'age'.

SnowflakeSnowPro Advanced — Data Scientist

Domain 3Objective 1

Prepare and Clean Data SNOWPRO-ADVANCED-DATA-SCIENTIST Practice Questions (Page 3)

Part of the Data Preparation and Feature Engineering domain, which makes up ~26% of our current practice bank.

22questions here

5free pages

10concepts

Questions 11–15

11foundation · easy
What is the purpose of performing data quality checks before feature engineering?
Select an answer first
12application · medium
A data scientist is analyzing event logs from a web server. The 'timestamp' column is in ISO 8601 format with timezone offsets, e.g., '2023-05-01T12:34:56+02:00'. The scientist needs to compare event times across different timezones. What is the most appropriate approach?
Select an answer first
13application · medium
A data scientist is merging two customer tables. The merge results in duplicate rows because some customers appear in both tables with slightly different spellings of their names. The scientist wants to identify and handle these duplicates. Which approach is most appropriate?
Select an answer first
14foundation · easy
Why is it important to standardize date and time fields in a dataset?
Select an answer first
15application · medium
A data scientist is preparing features for a k-nearest neighbors (KNN) model. The dataset includes a feature 'income' with a right-skewed distribution and a feature 'age' with a roughly normal distribution. The scientist wants to ensure that both features contribute equally to distance calculations. Which approach is most appropriate?
Select an answer first

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