A data scientist is analyzing sensor readings from a manufacturing process. The readings include occasional spikes that are known to be measurement errors, and the scientist wants to remove these errors before modeling. Which approach is most appropriate?

Use the interquartile range (IQR) to identify and remove values outside 1.5 times the IQR.

Cap all values at the 1st and 99th percentiles.

Impute the spikes with the median of the entire dataset.

Apply a log transformation to the data.

A data scientist has cleaned a dataset and wants to verify that the cleaning process did not introduce any new issues before proceeding to feature engineering. Which action is most appropriate?

Visualize the distribution of each feature to confirm it looks reasonable.

Train a model and evaluate its performance to infer data quality.

Skip validation and proceed directly to feature engineering to save time.

Run a set of validation checks, such as checking for missing values, duplicate rows, and data type consistency.

A data scientist is building a ridge regression model. The dataset includes a categorical feature 'City' with 50 categories, some with very low frequency. The scientist wants to avoid high dimensionality and reduce the risk of overfitting. Which encoding strategy is most appropriate?

Apply target encoding with smoothing based on the target mean.

Apply label encoding to the categories.

Apply one-hot encoding to all 50 categories.

Group rare categories into an 'Other' category and apply one-hot encoding.

What does stemming do in text preprocessing?

Reduces words to their base or root form

Identifies the part of speech of each word

Converts text to a numerical vector

Removes punctuation and special characters

SnowflakeSnowPro Advanced — Data Scientist

Domain 3Objective 1

Prepare and Clean Data SNOWPRO-ADVANCED-DATA-SCIENTIST Practice Questions (Page 4)

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

22questions here

5free pages

10concepts

Questions 16–20

16application · medium
A data scientist is analyzing sensor readings from a manufacturing process. The readings include occasional spikes that are known to be measurement errors, and the scientist wants to remove these errors before modeling. Which approach is most appropriate?
Select an answer first
17foundation · medium
A data scientist has a Snowflake table column stored as VARCHAR that contains numeric values. They need to perform aggregate calculations on this column in a SQL query. Which Snowflake function should be used to convert the VARCHAR values to a numeric data type so that the aggregate functions work correctly?
Select an answer first
18application · medium
A data scientist has cleaned a dataset and wants to verify that the cleaning process did not introduce any new issues before proceeding to feature engineering. Which action is most appropriate?
Select an answer first
19expert · hard
A data scientist is building a ridge regression model. The dataset includes a categorical feature 'City' with 50 categories, some with very low frequency. The scientist wants to avoid high dimensionality and reduce the risk of overfitting. Which encoding strategy is most appropriate?
Select an answer first
20foundation · easy
What does stemming do in text preprocessing?
Select an answer first

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