Question 1

Which of the following cross validation versions may not be suitable for very large datasets with hundreds of thousands of samples?

Options :

A : k-fold cross-validation

B : Leave-one-out cross-validation

C : Holdout method

D : All of the above

Answer: B

Question 2

Which one is not the feature engineering techniques used in ML data science world?

Options :

A : Imputation

B : Binning

C : One hot encoding

D : Statistical

Answer: D

Question 3

Which of the following cross validation versions is suitable quicker cross-validation for very large datasets with hundreds of thousands of samples?

Options :

A : k-fold cross-validation

B : Leave-one-out cross-validation

C : Holdout method

D : All of the above

Answer: C

Question 4

As Data Scientist looking out to use Reader account, Which ones are the correct considerations about Reader Accounts for Third-Party Access?

Options :

A : Reader accounts (formerly known as read-only accounts) provide a quick, easy, and cost-effective way to share data without requiring the consumer to become a Snowflake customer.

B : Each reader account belongs to the provider account that created it.

C : Users in a reader account can query data that has been shared with the reader account, but cannot perform any of the DML tasks that are allowed in a full account, such as data loading, insert, update, and similar data manipulation operations.

D : Data sharing is only possible between Snowflake accounts.

Answer: D

Question 5

To return the contents of a DataFrame as a Pandas DataFrame, Which of the following method can be used in SnowPark API?

Options :

A : REPLACE_TO_PANDAS

B : SNOWPARK_TO_PANDAS

C : CONVERT_TO_PANDAS

D : TO_PANDAS

Answer: D

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