DSA-C02 Dumps with Practice Exam Questions Answers

Explanation

Query and process data with a DataFrame object. Refer to Working with DataFrames in Snowpark Python.

Convert custom lambdas and functions to user-defined functions(UDFs) that you can call to process data.

Write a user-defined tabular function (UDTF) that processes data and returns data in a set of rows with one or more columns.

Write a stored procedure that you can call to process data, or automate with a task to build a data pipeline.

Explanation

The drop_duplicates() method removes duplicate rows.

dataframe.drop_duplicates(subset, keep, inplace, ignore_index)

Remove duplicate rows from the DataFrame:

1.import pandas as pd

2.data = {

3."name": ["Peter", "Mary", "John", "Mary"],

4."age": [50, 40, 30, 40],

5."qualified": [True, False, False, False]

6.}

7.

8.df = pd.DataFrame(data)

9.newdf = df.drop_duplicates()

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

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

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.