DSA-C02 Exam Dumps and Practice Questions

Type of visualization used for exploration:

* Correlation heatmap

* Class distributions by feature

* Two-Dimensional density plots.

All the visualizations are interactive, as is standard for Plotly.

For More details, please refer the below link:

https://towardsdatascience.com/data-exploration-understanding-and-visualization-72657f5eac41

Automatically Suspend Tasks After Failed Runs

Optionally suspend tasks automatically after a specified number of consecutive runs that either fail or time out. This feature can reduce costs by suspending tasks that consume Snowflake credits but fail to run to completion. Failed task runs include runs in which the SQL code in the task body either produces a user error or times out. Task runs that are skipped, canceled, or that fail due to a sys-tem error are considered indeterminate and are not included in the count of failed task runs.

Set the SUSPEND_TASK_AFTER_NUM_FAILURES = num parameter on a standalone task or the root task in a DAG. When the parameter is set to a value greater than 0, the following behavior applies to runs of the standalone task or DAG:

Standalone tasks are automatically suspended after the specified number of consecutive task runs either fail or time out.

The root task is automatically suspended after the run of any single task in a DAG fails or times out the specified number of times in consecutive runs.

The parameter can be set when creating a task (using CREATE TASK) or later (using ALTER TASK). The setting applies to tasks that rely on either Snowflake-managed compute resources (i.e. serverless compute model) or user-managed compute resources (i.e. a virtual warehouse).

The SUSPEND_TASK_AFTER_NUM_FAILURES parameter can also be set at the account, database, or schema level. The setting applies to all standalone or root tasks contained in the modified object. Note that explicitly setting the parameter at a lower (i.e. more granular) level overrides the parameter value set at a higher level.

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

It will Filters the row labelled r3.

Window Functions

A window function operates on a group (''window'') of related rows.

Each time a window function is called, it is passed a row (the current row in the window) and the window of rows that contain the current row. The window function returns one output row for each input row. The output depends on the individual row passed to the function and the values of the other rows in the window passed to the function.

Some window functions are order-sensitive. There are two main types of order-sensitive window functions:

Rank-related functions.

Window frame functions.

Rank-related functions list information based on the ''rank'' of a row. For example, if you rank stores in descending order by profit per year, the store with the most profit will be ranked 1; the second-most profitable store will be ranked 2, etc.

Window frame functions allow you to perform rolling operations, such as calculating a running total or a moving average, on a subset of the rows in the window.