Snowflake SnowPro Advanced: Data Scientist Certification Exam DSA-C02 Exam Dumps: Updated Questions & Answers (October 2025)

Explanation

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.

Explanation

Count(*)

* is used to select all values including null.

Explanation

Leave-one-out cross-validation (LOO cross-validation) is not suitable for very large datasets due to the fact that this validation technique requires one model for every sample in the training set to be created and evaluated.

Cross validation

It is a technique to evaluate a machine learning model and it is the basis for whole class of model evaluation methods. The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it. It works by the idea of splitting dataset into number of subsets, keep a subset aside, train the model, and test the model on the holdout subset.

Leave-one-out cross validation

Leave-one-out cross validation is K-fold cross validation taken to its logical extreme, with K equal to N, the number of data points in the set. That means that N separate times, the function approximator is trained on all the data except for one point and a prediction is made for that point. As be-fore the average error is computed and used to evaluate the model. The evaluation given by leave-one-out cross validation is very expensive to compute at first pass.

Explanation

All Snowpark ML modeling and preprocessing classes are in the snowflake.ml.modeling namespace. The Snowpark ML modules have the same name as the corresponding module from the sklearn namespace. For example, the Snowpark ML module corresponding to sklearn.calibration is snow-flake.ml.modeling.calibration.

The xgboost and lightgbm modules correspond to snowflake.ml.modeling.xgboost and snow-flake.ml.modeling.lightgbm, respectively.

Not all of the classes from scikit-learn are supported in Snowpark ML.

Explanation

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.

Explanation

Supervised learning methodology applies conditional probability of all the variables with respective the dependent variable and generally conditional probability of variables is nothing but a basic method of estimating the statistics for few random experiments.

Conditional probability is thus the likelihood of an event or outcome occurring based on the occurrence of some other event or prior outcome. Two events are said tobe independent if one event occurring does not affect the probability that the other event will occur.

Explanation

Vectorized Python UDFs let you define Python functions that receive batches of input rows as Pandas DataFrames and return batches of results as Pandas arrays or Series. You call vectorized Py-thon UDFs the same way you call other Python UDFs.

Advantages of using vectorized Python UDFs compared to the default row-by-row processing pat-tern include:

The potential for better performance if your Python code operates efficiently on batches of rows.

Less transformation logic required if you are calling into libraries that operate on Pandas Data-Frames or Pandas arrays.

When you use vectorized Python UDFs:

You do not need to change how you write queries using Python UDFs. All batching is handled by the UDF framework rather than your own code.

As with non-vectorized UDFs, there is no guarantee of which instances of your handler code will see which batches of input.

Explanation

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—dataprofiling 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.

Explanation

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

Explanation

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.Itis useful when feature distribution is unknown.It got affected by outliers.