Snowflake DSA-C02 Dumps Questions Answers

Explanation

What is a Window?

A window is a group of related rows. For example, a window might be defined based on timestamps, with all rows in the same month grouped in the same window. Or a window might be defined based on location, with all rows from a particular city grouped in the same window.

A window can consist of zero, one, or multiple rows. For simplicity, Snowflake documentation usually says that a window contains multiple rows.

What is a Window Function?

A window function is any function that operates over a window of rows.

A window function is generally passed two parameters:

A row. More precisely, a window function is passed 0 or more expressions. In almost all cases, at least one of those expressions references a column in that row. (Most window functions require at least one column or expression, but a few window functions, such as some rank-related functions, do not required an explicit column or expression.)

A window of related rows that includes that row. The window can be the entire table, or a subset of the rows in the table.

For non-window functions, all arguments are usually passed explicitly to the function, for example:

MY_FUNCTION(argument1, argument2, …)

Window functions behave differently; although the current row is passed as an argument the normal way, the window is passed through a separate clause, called an OVER clause. The syntax of the OVER clause is documented later.

LISTAGG

Returns the concatenated input values, separated by the delimiter string.

Window function

1.LISTAGG( [ DISTINCT ] [, ] )

2.[ WITHIN GROUP ( ) ]

3.OVER ( [ PARTITION BY ] )

HASH_AGG

Returns an aggregate signed 64-bit hash value over the (unordered) set of input rows. HASH_AGG never returns NULL, even if no input is provided. Empty input “hashes” to 0.

Window function

HASH_AGG( [ DISTINCT ] [ , ... ] ) OVER ( [ PARTITION BY ] )

HASH_AGG(*) OVER ( [ PARTITION BY ] )

Explanation

A stream object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change,so that actions can be taken using the changed data. This process is referred to as change data capture (CDC). An individual table stream tracks the changes made to rows in a source table. A table stream (also referred to as simply a “stream”) makes a “change table” available of what changed, at therow level, between two transactional points of time in a table. This allows querying and consuming a sequence of change records in a transactional fashion.

Streams can be created to query change data on the following objects:

· Standard tables, including shared tables.

· Views, including secure views

· Directory tables

· Event tables

Explanation

Supervised learning methodology applies conditional probability of all the variables with respective the dependent variable and generally conditional probability of variables is nothing but a basic method of estimating the statistics for few random experiments.

Conditional probability is thus the likelihood of an event or outcome occurring based on the occurrence of some other event or prior outcome. Two events are said tobe independent if one event occurring does not affect the probability that the other event will occur.