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

Explanation

A stream stores an offset for the source object and not any actual table columns or data. When que-ried, a stream accesses and returns the historic data in the same shape as the source object (i.e. the same column names and ordering) with the following additional columns:

METADATA$ACTION

Indicates the DML operation (INSERT, DELETE) recorded.

METADATA$ISUPDATE

Indicates whether the operation was part of an UPDATE statement. Updates to rows in the source object are represented as a pair of DELETE and INSERT records inthe stream with a metadata column METADATA$ISUPDATE values set to TRUE.

Note that streams record the differences between two offsets. If a row is added and then updated in the current offset, the delta change is a new row. The METADATA$ISUPDATE row records a FALSE value.

METADATA$ROW_ID

Specifies the unique and immutable ID for the row, which can be used to track changes to specific rows over time.

Explanation

Streamlit is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way. It is an open source library that assists data scientists and academics to develop Machine Learning (ML) visualization dashboards in a short period of time. We can build and deploy powerful data applications with just a few lines of code.

Why Streamlit?

Currently, real-world applications are in high demand and developers are developing new libraries and frameworks to make on-the-go dashboards easier to build and deploy. Streamlit is a library that reduces your dashboard development time from days to hours. Following are some reasons to choose the Streamlit:

It is a free and open-source library.

Installing Streamlit is as simple as installing any other python package

It is easy to learn because you won’t need any web development experience, only a basic under-standing of Python is enough to build a data application.

It is compatible with almost all machine learning frameworks, including Tensorflow and Pytorch, Scikit-learn, and visualization libraries such as Seaborn, Altair, Plotly, and many others.

Explanation

A scalar function (UDF) returns one output row for each input row. The returned row consists of a single column/value

Explanation

If you want to provide a share to many accounts, you might want to use a listing or a data ex-change.

Explanation

The model will not generalize well to unseen data because it overfits the test set. Tuning model hyper-parameters to a test set means that the hyper-parameters may overfit to that test set. If the same test set is used to estimate performance, it will produce an overestimate. The test set should be used only for testing, not for parameter tuning.

Using a separate validation set for tuning and test set for measuring performance provides unbiased, realistic measurement of performance.

What are hyper-parameters?

Hyper-parameters are parameters whose values control the learning process and determine the values of model parameters that a learning algorithm ends up learning. We can’t calculate their values from the data.

Example: Number of clusters in clustering, number of hidden layers in a neural network, and depth of a tree are some of the examples of hyper-parameters.

What is the hyper-parameter tuning?

Hyper-parameter tuning is the process of choosing the right combination of hyper-parameters that maximizes the model performance. It works by running multiple trials in a single training process. Each trial is a complete execution of your training application with values for your chosen hyper-parameters, set within the limits you specify. This process once finished will give you the set of hyper-parameter values that are best suited for the model to give optimal results.

Explanation

What is linear regression?

Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.

Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatoryvariable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.

A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).

For linear regression Y=a+bx+error.

If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.

For linear regression Y=a+bx+error. If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.

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

The area under the ROC curve (AUC) is a common evaluation metric for binary classification, which measures the performance of a classifier at different threshold values for the predicted probabilities. Other common metrics include accuracy, precision, recall, and F1 score, which are based on the confusion matrix of true positives, false positives, true negatives, and false negatives.

ExplanationWhat 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.