Snowflake DSA-C02 SnowPro Advanced: Data Scientist Certification Exam Online Training

Question #1

Which type of Machine learning Data Scientist generally used for solving classification and regression problems?

A . Supervised
B . Unsupervised
C . Reinforcement Learning
D . Instructor Learning
E . Regression Learning

Question #2

Which of the learning methodology applies conditional probability of all the variables with respective the dependent variable?

A . Reinforcement learning
B . Unsupervised learning
C . Artificial learning
D . Supervised learning

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Question #3

In a simple linear regression model (One independent variable), If we change the input variable by 1 unit.

How much output variable will change?

A . by 1
B . no change
C . by intercept
D . by its slope

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Question #4

There are a couple of different types of classification tasks in machine learning, Choose the Correct Classification which best categorized the below Application Tasks in Machine learning?

・ To detect whether email is spam or not

・ To determine whether or not a patient has a certain disease in medicine.

・ To determine whether or not quality specifications were met when it comes to QA (Quality Assurance).

A . Multi-Label Classification
B . Multi-Class Classification
C . Binary Classification
D . Logistic Regression

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Correct Answer: C
C

Explanation:

The Supervised Machine Learning algorithm can be broadly classified into Regression and Classification Algorithms. In Regression algorithms, we have predicted the output for continuous values, but to predict the categorical values, we need Classification algorithms.

What is the Classification Algorithm?

The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups. Such as, Yes or No, 0 or 1, Spam or Not Spam, cat or dog, etc. Classes can be called as targets/labels or categories.

Unlike regression, the output variable of Classification is a category, not a value, such as "Green or Blue", "fruit or animal", etc. Since the Classification algorithm is a Supervised learning technique, hence it takes labeled input data, which means it contains input with the corresponding output. In classification algorithm, a discrete output function(y) is mapped to input variable(x).

y=f(x), where y = categorical output

The best example of an ML classification algorithm is Email Spam Detector.

The main goal of the Classification algorithm is to identify the category of a given dataset, and these algorithms are mainly used to predict the output for the categorical data.

The algorithm which implements the classification on a dataset is known as a classifier. There are two types of Classifications:

Binary Classifier: If the classification problem has only two possible outcomes, then it is called as Binary Classifier.

Examples: YES or NO, MALE or FEMALE, SPAM or NOT SPAM, CAT or DOG, etc. Multi-class Classifier: If a classification problem has more than two outcomes, then it is called as Multi-class Classifier.

Example: Classifications of types of crops, Classification of types of music.

Binary classification in deep learning refers to the type of classification where we have two class labels C one normal and one abnormal.

Some examples of binary classification use:

・ To detect whether email is spam or not

・ To determine whether or not a patient has a certain disease in medicine.

・ To determine whether or not quality specifications were met when it comes to QA (Quality Assurance).

For example, the normal class label would be that a patient has the disease, and the abnormal class label would be that they do not, or vice-versa.

As is with every other type of classification, it is only as good as the binary classification dataset that it has C or, in other words, the more training and data it has, the better it is.

Question #5

Which of the following method is used for multiclass classification?

A . one vs rest
B . loocv
C . all vs one
D . one vs another

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Correct Answer: A
A

Explanation:

Binary vs. Multi-Class Classification

Classification problems are common in machine learning. In most cases, developers prefer using a supervised machine-learning approach to predict class tables for a given dataset. Unlike regression, classification involves designing the classifier model and training it to input and categorize the test dataset. For that, you can divide the dataset into either binary or multi-class modules.

As the name suggests, binary classification involves solving a problem with only two class labels. This makes it easy to filter the data, apply classification algorithms, and train the model to predict outcomes. On the other hand, multi-class classification is applicable when there are more than two class labels in the input train data. The technique enables developers to categorize the test data into multiple binary class labels.

That said, while binary classification requires only one classifier model, the one used in the multi-

class approach depends on the classification technique. Below are the two models of the multi-class

classification algorithm.

One-Vs-Rest Classification Model for Multi-Class Classification

Also known as one-vs-all, the one-vs-rest model is a defined heuristic method that leverages a binary classification algorithm for multi-class classifications. The technique involves splitting a multi-class dataset into multiple sets of binary problems. Following this, a binary classifier is trained to handle each binary classification model with the most confident one making predictions.

For instance, with a multi-class classification problem with red, green, and blue datasets, binary

classification can be categorized as follows:

Problem one: red vs. green/blue

Problem two: blue vs. green/red

Problem three: green vs. blue/red

The only challenge of using this model is that you should create a model for every class. The three classes require three models from the above datasets, which can be challenging for large sets of data with million rows, slow models, such as neural networks and datasets with a significant number of classes.

The one-vs-rest approach requires individual models to prognosticate the probability-like score. The class index with the largest score is then used to predict a class. As such, it is commonly used for classification algorithms that can naturally predict scores or numerical class membership such as perceptron and logistic regression.

Question #6

Which ones are the key actions in the data collection phase of Machine learning included?

A . Label
B . Ingest and Aggregate
C . Probability
D . Measure

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Question #7

Which ones are the type of visualization used for Data exploration in Data Science?

A . Heat Maps
B . Newton AI
C . Feature Distribution by Class
D . 2D-Density Plots
E . Sand Visualization

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Question #8

Which one is not the feature engineering techniques used in ML data science world?

A . Imputation
B . Binning
C . One hot encoding
D . Statistical

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Question #8

Which one is not the feature engineering techniques used in ML data science world?

A . Imputation
B . Binning
C . One hot encoding
D . Statistical

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Question #8

Which one is not the feature engineering techniques used in ML data science world?

A . Imputation
B . Binning
C . One hot encoding
D . Statistical

Reveal Solution Hide Solution

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