DSA-C02 Exam Questions & Answers

What is an External Function?

An external function calls code that is executed outside Snowflake.

The remotely executed code is known as a remote service.

Information sent to a remote service is usually relayed through a proxy service.

Snowflake stores security-related external function information in an API integration.

External Function:

An external function is a type of UDF. Unlike other UDFs, an external function does not contain its own code; instead, the external function calls code that is stored and executed outside Snowflake.

Inside Snowflake, the external function is stored as a database object that contains information that Snowflake uses to call the remote service. This stored information includes the URL of the proxy service that relays information to and from the remote service.

Remote Service:

The remotely executed code is known as a remote service.

The remote service must act like a function. For example, it must return a value.

Snowflake supports scalar external functions; the remote service must return exactly one row for each row received.

Proxy Service:

Snowflake does not call a remote service directly. Instead, Snowflake calls a proxy service, which relays the data to the remote service.

The proxy service can increase security by authenticating requests to the remote service.

The proxy service can support subscription-based billing for a remote service. For example, the proxy service can verify that a caller to the remote service is a paid subscriber.

The proxy service also relays the response from the remote service back to Snowflake.

Examples of proxy services include:

Amazon API Gateway.

Microsoft Azure API Management service.

API Integration:

An integration is a Snowflake object that provides an interface between Snowflake and third-party services. An API integration stores information, such as security information, that is needed to work with a proxy service or remote service.

An API integration is created with the CREATE API INTEGRATION command.

Users can write and call their own remote services, or call remote services written by third parties. These remote services can be written using any HTTP server stack, including cloud serverless compute services such as AWS Lambda.

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 ] <expr1> [, <delimiter> ] )

2. [ WITHIN GROUP ( <orderby_clause> ) ]

3. OVER ( [ PARTITION BY <expr2> ] )

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 ] <expr> [ , <expr2> ... ] ) OVER ( [ PARTITION BY <expr3> ] )

HASH_AGG(*) OVER ( [ PARTITION BY <expr3> ] )

To track changing data trends, create a data drift monitor that uses the training data as a baseline and the new data as a target.

Model drift and decay are concepts that describe the process during which the performance of a model deployed to production degrades on new, unseen data or the underlying assumptions about the data change.

These are important metrics to track once models are deployed to production. Models must be regularly re-trained on new data. This is referred to as refitting the model. This can be done either on a periodic basis, or, in an ideal scenario, retraining can be triggered when the performance of the model degrades below a certain pre-defined threshold.

Supervised Learning

Overview:

Supervised learning is a type of machine learning that uses labeled data to train machine learning models. In labeled data, the output is already known. The model just needs to map the inputs to the respective outputs.

Algorithms:

Some of the most popularly used supervised learning algorithms are:

* Linear Regression

* Logistic Regression

* Support Vector Machine

* K Nearest Neighbor

* Decision Tree

* Random Forest

* Naive Bayes

Working:

Supervised learning algorithms take labelled inputs and map them to the known outputs, which means you already know the target variable.

Supervised Learning methods need external supervision to train machine learning models. Hence, the name supervised. They need guidance and additional information to return the desired result.

Applications:

Supervised learning algorithms are generally used for solving classification and regression problems.

Few of the top supervised learning applications are weather prediction, sales forecasting, stock price analysis.

What is a Share?

Shares are named Snowflake objects that encapsulate all of the information required to share a database.

Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share using either or both of the following options:

Option 1: Grant privileges on objects to a share via a database role.

Option 2: Grant privileges on objects directly to a share.

You choose which accounts can consume data from the share by adding the accounts to the share.

After a database is created (in a consumer account) from a share, all the shared objects are accessible to users in the consumer account.

Shares are secure, configurable, and controlled completely by the provider account:

* New objects added to a share become immediately available to all consumers, providing real-time access to shared data.

Access to a share (or any of the objects in a share) can be revoked at any time.