DSA-C02 Online Practice Questions and Answers

Correct Answer: B

Explanation:

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

Correct Answer: D

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()

Correct Answer: AB

Explanation:

Model versioning in a way involves tracking the changes made toan ML model that has been previously built. Put differently, it is the process of making changes to the configurations of an ML Model. From another perspective, we can see

model versioning as a feature that helps Machine Learning Engineers, Data Scientists, and related personnel create and keep multiple versions of the same model. Think of it as a way of taking notes of the changes you make to the model

through tweaking hyperparameters, retraining the model with more data, and so on. In model versioning, a number of things need to be versioned, to help us keep track of important changes. I'll list and explain them below:

Implementation code: From the early days of model building to optimization stages, code or in this case source code of the model plays an important role. This code experiences significant changes during optimization stages which can easily

be lost if not tracked properly. Because of this, code is one of the things that are taken into consideration during the model versioning process.

Data: In some cases, training data does improve significantly from its initial state during model op-timization phases. This can be as a result of engineering new features from existing ones to train our model on. Also there is metadata (data

about your training data and model) to consider versioning. Metadata can change different times over without the training data actually changing. We need to be able to track these changes through versioning

Model: The model is a product of the two previous entities and as stated in their explanations, an ML model changes at different points of the optimization phases through hyperparameter setting, model artifacts and learning coefficients.

Versioning helps take record of the different versions of a Machine Learning model. MLFlow and Pachyderm are the tools used to manage ML lifecycle and Model versioning.

Correct Answer: AC

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.

Correct Answer: C

Explanation:

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.

Correct Answer: D

Explanation:

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()

Correct Answer: D

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.

Correct Answer: C

Explanation:

Partial dependence plots (PDP) is a useful tool for gaining insights into the relationship between features and predictions. It helps us understand how different values of a particular feature impact model's predictions.

Correct Answer: CD

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.

Correct Answer: D

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.