GuideTorrent DSA-C02 Exam Questions Real DSA-C02 Practice Dumps [Q22-Q41]

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NEW QUESTION # 22
Which of the following process best covers all of the following characteristics?
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.

• A. Data Virtualization
• B. Data Visualization
• C. Data Profiling
• D. Data Collection

Answer: C

Explanation:
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.

NEW QUESTION # 23
Which one is incorrect understanding about Providers of Direct share?

• A. A data provider is any Snowflake account that creates shares and makes them available to other Snowflake accounts to consume.
• B. You can create as many shares as you want, and add as many accounts to a share as you want.
• C. As a data provider, you share a database with one or more Snowflake accounts.
• D. If you want to provide a share to many accounts, you can do the same via Direct Share.

Answer: D

Explanation:
Explanation
If you want to provide a share to many accounts, you might want to use a listing or a data ex-change.

NEW QUESTION # 24
Which one is not Types of Feature Scaling?

• A. Standard Scaling
• B. Economy Scaling
• C. Robust Scaling
• D. Min-Max Scaling

Answer: D

Explanation:
ExplanationFeature 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.

NEW QUESTION # 25
Which type of 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?

• A. Vectorized Python UDFs
• B. Hybrid Python UDFs
• C. MPP Python UDFs
• D. Scaler Python UDFs

Answer: A

Explanation:
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.

NEW QUESTION # 26
Which one of the following is not the key component while designing External functions within Snowflake?

• A. UDF Service
• B. Proxy Service
• C. API Integration
• D. Remote Service

Answer: A

Explanation:
Explanation
What is an External Function?
An external function calls code that is executed outside Snowflake.
The remotely executed code is known as a remote service.
Information sent to a remote service is usually relayed through a proxy service.
Snowflake stores security-related external function information in an API integration.
External Function:
An external function is a type of UDF. Unlike other UDFs, an external function does not contain its own code; instead, the external function calls code that is stored and executed outside Snowflake.
Inside Snowflake, the external function is stored as a database object that contains information that Snowflake uses to call the remote service. This stored information includes the URL of the proxy service that relays information to and from the remote service.
Remote Service:
The remotely executed code is known as a remote service.
The remote service must act like a function. For example, it must return a value.
Snowflake supports scalar external functions; the remote service must return exactly one row for each row received.
Proxy Service:
Snowflake does not call a remote service directly. Instead, Snowflake calls a proxy service, which relays the data to the remote service.
The proxy service can increase security by authenticating requests to the remote service.
The proxy service can support subscription-based billing for a remote service. For example, the proxy service can verify that a caller to the remote service is a paid subscriber.
The proxy service also relays the response from the remote service back to Snowflake.
Examples of proxy services include:
Amazon API Gateway.
Microsoft Azure API Management service.
API Integration:
An integration is a Snowflake object that provides an interface between Snowflake and third-party services.
An API integration stores information, such as security information, that is needed to work with a proxy service or remote service.
An API integration is created with the CREATE API INTEGRATION command.
Users can write and call their own remote services, or call remote services written by third parties. These remote services can be written using any HTTP server stack,including cloud serverless compute services such as AWS Lambda.

NEW QUESTION # 27
Performance metrics are a part of every machine learning pipeline, Which ones are not the performance metrics used in the Machine learning?

• A. R2 (R-Squared)
• B. AUM
• C. Root Mean Squared Error (RMSE)
• D. AU-ROC

Answer: B

Explanation:
Explanation
Every machine learning task can be broken down to either Regression or Classification, just like the performance metrics.
Metrics are used to monitor and measure the performance of a model (during training and testing), and do not need to be differentiable.
Regression metrics
Regression models have continuous output. So, we need a metric based on calculating some sort of distance between predicted and ground truth.
In order to evaluate Regression models, we'll discuss these metrics in detail:
Mean Absolute Error (MAE),
Mean Squared Error (MSE),
Root Mean Squared Error (RMSE),
R2 (R-Squared).
Mean Squared Error (MSE)
Mean squared error is perhaps the most popular metric used for regression problems. It essentially finds the average of the squared difference between the target value and the value predicted by the regression model.
Few key points related to MSE:
It's differentiable, so it can be optimized better.
It penalizes even small errors by squaring them, which essentially leads to an overestimation of how bad the model is.
Error interpretation has to be done with squaring factor(scale) in mind. For example in our Boston Housing regression problem, we got MSE=21.89 which primarily corresponds to (Prices)2.
Due to the squaring factor, it's fundamentally more prone to outliers than other metrics.
Mean Absolute Error (MAE)
Mean Absolute Error is the average of the difference between the ground truth and the predicted values.
Few key points for MAE
It's more robust towards outliers than MAE, since it doesn't exaggerate errors.
It gives us a measure of how far the predictions were from the actual output. However, since MAE uses absolute value of the residual, it doesn't give us an idea of the direction of the error, i.e. whether we're under-predicting or over-predicting the data.
Error interpretation needs no second thoughts, as it perfectly aligns with the original degree of the variable.
MAE is non-differentiable as opposed to MSE, which is differentiable.
Root Mean Squared Error (RMSE)
Root Mean Squared Error corresponds to the square root of the average of the squared difference between the target value and the value predicted by the regression model.
Few key points related to RMSE:
It retains the differentiable property of MSE.
It handles the penalization of smaller errors done by MSE by square rooting it.
Error interpretation can be done smoothly, since the scale is now the same as the random variable.
Since scale factors are essentially normalized, it's less prone to struggle in the case of outliers.
R2 Coefficient of determination
R2 Coefficient of determination actually works as a post metric, meaning it's a metric that's calcu-lated using other metrics.
The point of even calculating this coefficient is to answer the question "How much (what %) of the total variation in Y(target) is explained by the variation in X(regression line)" Few intuitions related to R2 results:
If the sum of Squared Error of the regression line is small => R2 will be close to 1 (Ideal), meaning the regression was able to capture 100% of the variance in the target variable.
Conversely, if the sum of squared error of the regression line is high=> R2 will be close to 0, meaning the regression wasn't able to capture any variance in the target variable.
You might think that the range of R2 is (0,1) but it's actually (-,1)because the ratio of squared errors of the regression line and mean can surpass the value 1 if the squared error of regression line is too high (>squared error of the mean).
Classification metrics
Classification problems are one of the world's most widely researched areas. Use cases are present in almost all production and industrial environments. Speech recognition, face recognition, textclassification - the list is endless.
Classification models have discrete output, so we need a metric that compares discrete classes in some form.
Classification Metrics evaluate a model's performance and tell you how good or bad the classification is, but each of them evaluates it in a different way.
So in order to evaluate Classification models, we'll discuss these metrics in detail:
Accuracy
Confusion Matrix (not a metric but fundamental to others)
Precision and Recall
F1-score
AU-ROC
Accuracy
Classification accuracy is perhaps the simplest metric to use and implement and is defined as the number of correct predictions divided by the total number of predictions, multiplied by 100.
We can implement this by comparing ground truth and predicted values in a loop or simply utilizing the scikit-learn module to do the heavy lifting for us (not so heavy in this case).
Confusion Matrix
Confusion Matrix is a tabular visualization of the ground-truth labels versus model predictions. Each row of the confusion matrix represents the instances in a predicted class and each column represents the instances in an actual class. Confusion Matrix is not exactly a performance metric but sort of a basis on which other metrics evaluate the results.
Each cell in the confusion matrix represents an evaluation factor. Let's understand these factors one by one:
True Positive(TP) signifies how many positive class samples your model predicted correctly.
True Negative(TN) signifies how many negative class samples your model predicted correctly.
False Positive(FP) signifies how many negative class samples your model predicted incorrectly. This factor represents Type-I error in statistical nomenclature. This error positioning in the confusion matrix depends on the choice of the null hypothesis.
False Negative(FN) signifies how many positive class samples your model predicted incorrectly. This factor represents Type-II error in statistical nomenclature. This error positioning in the confu-sion matrix also depends on the choice of the null hypothesis.
Precision
Precision is the ratio of true positives and total positives predicted
Recall/Sensitivity/Hit-Rate
A Recall is essentially the ratio of true positives to all the positives in ground truth.
Precision-Recall tradeoff
To improve your model, you can either improve precision or recall - but not both! If you try to re-duce cases of non-cancerous patients being labeled as cancerous (FN/type-II), no direct effect will take place on cancerous patients being labeled as non-cancerous.
F1-score
The F1-score metric uses a combination of precision and recall. In fact, the F1 score is the harmonic mean of the two.
AUROC (Area under Receiver operating characteristics curve)
Better known as AUC-ROC score/curves. It makes use of true positive rates(TPR) and false posi-tive rates(FPR).

NEW QUESTION # 28
You previously trained a model using a training dataset. You want to detect any data drift in the new data collected since the model was trained.
What should you do?

• A. Create a new version of the dataset using only the new data and retrain the model.
• B. Create a new dataset using the new data and a timestamp column and create a data drift monitor that uses the training dataset as a baseline and the new dataset as a target.
• C. Retrained your training dataset after correcting data outliers & no need to introduce new data.
• D. Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.

Answer: B

Explanation:
Explanation
To track changing data trends, create a data drift monitor that uses the training data as a baseline and the new data as a target.
Model drift and decay are concepts that describe the process during which the performance of a model deployed to production degrades on new, unseen data or the underlying assumptions about the data change.
These are important metrics to track once models are deployed toproduction. Models must be regularly re-trained on new data. This is referred to as refitting the model. This can be done either on a periodic basis, or, in an ideal scenario, retraining can be triggered when the performance of the model degrades below a certain pre-defined threshold.

NEW QUESTION # 29
Which is the visual depiction of data through the use of graphs, plots, and informational graphics?

• A. Data Virtualization
• B. Data Mining
• C. Data Interpretation
• D. Data visualization

Answer: B

Explanation:
Explanation
Data visualization is the visual depiction of data through the use of graphs, plots, and informational graphics.
Its practitioners use statistics and data science to conveythe meaning behind data in ethical and accurate ways.

NEW QUESTION # 30
Skewness of Normal distribution is ___________

• A. Positive
• B. Negative
• C. Undefined
• D. 0

Answer: D

Explanation:
Explanation
Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical explanation for mathematical proofs, you can refer to books or websites that speak on the same in detail.

NEW QUESTION # 31
Which one is the incorrect option to share data in Snowflake?

• A. a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.
• B. a Direct Marketplace, in which you directly share specific database objects (a share) to another account in your region using Snowflake Marketplace.
• C. a Listing, in which you offer a share and additional metadata as a data product to one or more accounts.
• D. a Direct Share, in which you directly share specific database objects (a share) to anoth-er account in your region.

Answer: B

Explanation:
Explanation
Options for Sharing in Snowflake
You can share data in Snowflake using one of the following options:
a Listing, in which you offer a share and additional metadata as a data product to one or more ac-counts,
a Direct Share, in which you directly share specific database objects (a share) to another account in your region,
a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.

NEW QUESTION # 32
What is the formula for measuring skewness in a dataset?

• A. (3(MEAN - MEDIAN))/ STANDARD DEVIATION
• B. MEAN - MEDIAN
• C. (MEAN - MODE)/ STANDARD DEVIATION
• D. MODE - MEDIAN

Answer: A

Explanation:
Explanation
Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical expla-nation for mathematical proofs, you can refer to books or websites that speak on the same in detail.

NEW QUESTION # 33
A Data Scientist as data providers require to allow consumers to access all databases and database objects in a share by granting a single privilege on shared databases. Which one is incorrect SnowSQL command used by her while doing this task?
Assuming:
A database named product_db exists with a schema named product_agg and a table named Item_agg.
The database, schema, and table will be shared with two accounts named xy12345 and yz23456.
1.USE ROLE accountadmin;
2.CREATE DIRECT SHARE product_s;
3.GRANT USAGE ON DATABASE product_db TO SHARE product_s;
4.GRANT USAGE ON SCHEMA product_db. product_agg TO SHARE product_s;
5.GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;
6.SHOW GRANTS TO SHARE product_s;
7.ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;
8.SHOW GRANTS OF SHARE product_s;

• A. GRANT USAGE ON DATABASE product_db TO SHARE product_s;
• B. GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;
• C. ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;
• D. CREATE DIRECT SHARE product_s;

Answer: B

Explanation:
Explanation
CREATE SHARE product_s is the correct Snowsql command to create Share object.
Rest are correct ones.
https://docs.snowflake.com/en/user-guide/data-sharing-provider#creating-a-share-using-sql

NEW QUESTION # 34
Which one is not the types of Feature Engineering Transformation?

• A. Normalization
• B. Aggregation
• C. Scaling
• D. Encoding

Answer: B

Explanation:
Explanation
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 featuresinto 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.

NEW QUESTION # 35
How do you handle missing or corrupted data in a dataset?

• A. All of the above
• B. Drop missing rows or columns
• C. Replace missing values with mean/median/mode
• D. Assign a unique category to missing values

Answer: A

NEW QUESTION # 36
Select the Correct Statements regarding Normalization?

• A. Normalization technique uses minimum and max values for scaling of model.
• B. Normalization got affected by outliers.
• C. Normalization technique uses mean and standard deviation for scaling of model.
• D. Scikit-Learn provides a transformer RecommendedScaler for Normalization.

Answer: A,B

Explanation:
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.

NEW QUESTION # 37
Which of the following method is used for multiclass classification?

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

Answer: A

Explanation:
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 forclassification algorithms that can naturally predict scores or numerical class membership such as perceptron and logistic regression.

NEW QUESTION # 38
Which ones are the correct rules while using a data science model created via External function in Snowflake?

• A. An external function can appear in any clause of a SQL statement in which other types of UDF can appear.
• B. External functions can be overloaded.
• C. External functions return a value. The returned value can be a compound value, such as a VARIANT that contains JSON.
• D. External functions can accept Model parameters.

Answer: A,B,C,D

Explanation:
Explanation
From the perspective of a user running a SQL statement, an external function behaves like any other UDF .
External functions follow these rules:
External functions return a value.
External functions can accept parameters.
An external function can appear in any clause of a SQL statement in which other types of UDF can appear. For example:
1.select my_external_function_2(column_1, column_2)
2.from table_1;
1.select col1
2.from table_1
3.where my_external_function_3(col2) < 0;
1.create view view1 (col1) as
2.select my_external_function_5(col1)
3.from table9;
An external function can be part of a more complex expression:
1.select upper(zipcode_to_city_external_function(zipcode))
2.from address_table;
The returned value can be a compound value, such as a VARIANT that contains JSON.
External functions can be overloaded; two different functions can have the same name but different signatures (different numbers or data types of input parameters).

NEW QUESTION # 39
Which of the following is a common evaluation metric for binary classification?

• A. F1 score
• B. Mean squared error (MSE)
• C. Area under the ROC curve (AUC)
• D. Accuracy

Answer: C

Explanation:
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.

NEW QUESTION # 40
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. Binary Classification
• C. Multi-Class Classification
• D. Logistic Regression

Answer: B

Explanation:
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 - 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 - or, in other words, the more training and data it has, the better it is.

NEW QUESTION # 41
......

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