Snowflake DSA-C02 dumps - 100% Pass Guarantee!

Correct Answer: B

Explanation:

Options for Sharing in Snowflake

You can share data in Snowflake using one of the following options:

a Listing, in which you offer a share and additional metadata as a data product to one or more ac-counts,

a Direct Share, in which you directly share specific database objects (a share) to another account in your region,

a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.

Correct Answer: D

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 ] )

Correct Answer: B

Explanation:Feature Scaling

Feature Scaling is the process of transforming the features so that they have a similar scale. This is important in machine learning because the scale of the features can affect the performance of the model.

Types of Feature Scaling:

Min-Max Scaling: Rescaling the features to a specific range, such as between 0 and 1, by subtracting the minimum value and dividing by the range. Standard Scaling: Rescaling the features to have a mean of 0 and a standard deviation of 1

by subtracting the mean and dividing by the standard deviation. Robust Scaling: Rescaling the features to be robust to outliers by dividing them by the interquartile range.

Benefits of Feature Scaling:

Improves Model Performance: By transforming the features to have a similar scale, the model can learn from all features equally and avoid being dominated by a few large features.

Increases Model Robustness: By transforming the features to be robust to outliers, the model can become more robust to anomalies.

Improves Computational Efficiency: Many machine learning algorithms, such as k-nearest neighbors, are sensitive to the scale of the features and perform better with scaled features.

Improves Model Interpretability: By transforming the features to have a similar scale, it can be easier to understand the model's predictions.

Correct Answer: D

Explanation:

Hex -- collaborative data science and analytics platform Denodo -- data virtualization and federation platform DvSum -- data catalog and data intelligence platform Diyotta -- data integration and migration

Correct Answer: D

Explanation:

All are correct except a standard-only stream tracks row inserts only. A standard (i.e. delta) stream tracks all DML changes to the source object, including inserts, up-dates, and deletes (including table truncates).