Question 1

Which ones are the known limitations of using External function?

Options :

A :

Currently, external functions cannot be shared with data consumers via Secure Data Sharing.

B :

Currently, external functions must be scalar functions. A scalar external function re-turns a single value for each input row.

C :

External functions have more overhead than internal functions (both built-in functions and internal UDFs) and usually execute more slowly

D :

An external function accessed through an AWS API Gateway private endpoint can be accessed only from a Snowflake VPC (Virtual Private Cloud) on AWS and in the same AWS region.

Answer: A,B,C,D

Question 2

Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share us-ing Which of the following options?

Options :

A : Grant privileges on objects to a share via Account role.

B : Grant privileges on objects directly to a share.

C : Grant privileges on objects to a share via a database role.

D : Grant privileges on objects to a share via a third-party role.

Answer: B,C

Question 3

Which one is not the types of Feature Engineering Transformation?

Options :

A : Scaling

B : Encoding

C : Aggregation

D : D. Normalization

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

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