DSA-C02 Exam Dumps : SnowPro Advanced: Data Scientist Certification Exam

Explanation

Holdout cross-validation method is suitable for very large dataset because it is the simplest and quicker to compute version of cross-validation.

Holdout method

In this method, the dataset is divided into two sets namely the training and the test set with the basic property that the training set is bigger than the test set. Later, the model is trained on the training dataset and evaluated using the test dataset.

Explanation

From the perspective of a user running a SQL statement, an external function behaves like any other UDF . External functions follow these rules:

External functions return a value.

External functions can accept parameters.

An external function can appear in any clause of a SQL statement in which other types of UDF can appear. For example:

1.select my_external_function_2(column_1, column_2)

2.from table_1;

1.select col1

2.from table_1

3.where my_external_function_3(col2) < 0;

1.create view view1 (col1) as

2.select my_external_function_5(col1)

3.from table9;

An external function can be part of a more complex expression:

1.select upper(zipcode_to_city_external_function(zipcode))

2.from address_table;

The returned value can be a compound value, such as a VARIANT that contains JSON.

External functions can be overloaded; two different functions can have the same name but different signatures (different numbers or data types of input parameters).