Snowflake DSA-C02 Practice Test - Questions Answers, Page 4

Data processing and analysis cannot happen without data profiling---reviewing source data for con-tent and quality. As data gets bigger and infrastructure moves to the cloud, data profiling is increasingly important.

What is data profiling?

Data profiling is the process of reviewing source data, understanding structure, content and interrelationships, and identifying potential for data projects.

Data profiling is a crucial part of:

* Data warehouse and business intelligence (DW/BI) projects---data profiling can uncover data quality issues in data sources, and what needs to be corrected in ETL.

* Data conversion and migration projects---data profiling can identify data quality issues, which you can handle in scripts and data integration tools copying data from source to target. It can also un-cover new requirements for the target system.

* Source system data quality projects---data profiling can highlight data which suffers from serious or numerous quality issues, and the source of the issues (e.g. user inputs, errors in interfaces, data corruption).

Data profiling involves:

* Collecting descriptive statistics like min, max, count and sum.

* Collecting data types, length and recurring patterns.

* Tagging data with keywords, descriptions or categories.

* Performing data quality assessment, risk of performing joins on the data.

* Discovering metadata and assessing its accuracy.

* Identifying distributions, key candidates, foreign-key candidates, functional dependencies, embedded value dependencies, and performing inter-table analysis.

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.

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> ] )

Count(*)

* is used to select all values including null.

NULL values are ignored unless all the records are NULL, in which case a NULL value is returned

What is Feature Engineering?

Feature engineering is the process of transforming raw data into features that are suitable for ma-chine learning models. In other words, it is the process of selecting, extracting, and transforming the most relevant features from the available data to build more accurate and efficient machine learning models.

The success of machine learning models heavily depends on the quality of the features used to train them. Feature engineering involves a set of techniques that enable us to create new features by combining or transforming the existing ones. These techniques help to highlight the most important pat-terns and relationships in the data, which in turn helps the machine learning model to learn from the data more effectively.

What is a Feature?

In the context of machine learning, a feature (also known as a variable or attribute) is an individual measurable property or characteristic of a data point that is used as input for a machine learning al-gorithm. Features can be numerical, categorical, or text-based, and they represent different aspects of the data that are relevant to the problem at hand.

For example, in a dataset of housing prices, features could include the number of bedrooms, the square footage, the location, and the age of the property. In a dataset of customer demographics, features could include age, gender, income level, and occupation.

The choice and quality of features are critical in machine learning, as they can greatly impact the ac-curacy and performance of the model.

Why do we Engineer Features?

We engineer features to improve the performance of machine learning models by providing them with relevant and informative input data. Raw data may contain noise, irrelevant information, or missing values, which can lead to inaccurate or biased model predictions. By engineering features, we can extract meaningful information from the raw data, create new variables that capture important patterns and relationships, and transform the data into a more suitable format for machine learning algorithms.

Feature engineering can also help in addressing issues such as overfitting, underfitting, and high di-mensionality. For example, by reducing the number of features, we can prevent the model from be-coming too complex or overfitting to the training data. By selecting the most relevant features, we can improve the model's accuracy and interpretability.

In addition, feature engineering is a crucial step in preparing data for analysis and decision-making in various fields, such as finance, healthcare, marketing, and social sciences. It can help uncover hidden insights, identify trends and patterns, and support data-driven decision-making.

We engineer features for various reasons, and some of the main reasons include:

Improve User Experience: The primary reason we engineer features is to enhance the user experience of a product or service. By adding new features, we can make the product more intuitive, efficient, and user-friendly, which can increase user satisfaction and engagement.

Competitive Advantage: Another reason we engineer features is to gain a competitive advantage in the marketplace. By offering unique and innovative features, we can differentiate our product from competitors and attract more customers.

Meet Customer Needs: We engineer features to meet the evolving needs of customers. By analyzing user feedback, market trends, and customer behavior, we can identify areas where new features could enhance the product's value and meet customer needs.

Increase Revenue: Features can also be engineered to generate more revenue. For example, a new feature that streamlines the checkout process can increase sales, or a feature that provides additional functionality could lead to more upsells or cross-sells.

Future-Proofing: Engineering features can also be done to future-proof a product or service. By an-ticipating future trends and potential customer needs, we can develop features that ensure the product remains relevant and useful in the long term.

Processes Involved in Feature Engineering

Feature engineering in Machine learning consists of mainly 5 processes: Feature Creation, Feature Transformation, Feature Extraction, Feature Selection, and Feature Scaling. It is an iterative process that requires experimentation and testing to find the best combination of features for a given problem. The success of a machine learning model largely depends on the quality of the features used in the model.

Feature Transformation

Feature Transformation is the process of transforming the features into a more suitable representation for the machine learning model. This is done to ensure that the model can effectively learn from the data.

Types of Feature Transformation:

Normalization: Rescaling the features to have a similar range, such as between 0 and 1, to prevent some features from dominating others.

Scaling: Rescaling the features to have a similar scale, such as having a standard deviation of 1, to make sure the model considers all features equally.

Encoding: Transforming categorical features into a numerical representation. Examples are one-hot encoding and label encoding.

Transformation: Transforming the features using mathematical operations to change the distribution or scale of the features. Examples are logarithmic, square root, and reciprocal transformations.

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.

Normalization is a scaling technique in Machine Learning applied during data preparation to change the values of numeric columns in the dataset to use a common scale. It is not necessary for all datasets in a model. It is required only when features of machine learning models have different ranges.

Scikit-Learn provides a transformer called MinMaxScaler for Normalization.

This technique uses minimum and max values for scaling of model.It is useful when feature distribution is unknown.It got affected by outliers.

To return the contents of a DataFrame as a Pandas DataFrame, use the to_pandas method.

For example:

1. >>> python_df = session.create_dataframe(['a', 'b', 'c'])

2. >>> pandas_df = python_df.to_pandas()