Top 65 Data Analytics Multiple Choice Questions (MCQs) with Answers

1 .

Data Analysis is a process of?

A)

cleaning data

B)

inspecting data

C)

transforming data

D)

All of the above

Correct Answer : All of the above

Explanation : Data Analysis is a process of inspecting, cleaning, transforming and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making.

2 .

Data Analytics uses ___ to get insights from data.

A)

Statistical figures

B)

Statistical methods

C)

Numerical aspects

D)

None of the above

Correct Answer : Statistical methods

Explanation : To gain insights from data, Data Analytics use statistical approaches. Organizations can use data analytics to uncover trends and develop insights by analyzing all of their data (real-time, historical, unstructured, structured, and qualitative).

3 .

How many main statistical methodologies are used in data analysis?

A)

2

B)

3

C)

4

D)

5

Correct Answer : 2

Explanation : In data analysis, two main statistical methodologies are used Descriptive statistics and Inferential statistics.

4 .

Velocity is the speed at which the data is processed _____.

A)

True

B)

False

C)

Can Not Say

D)

None of the above

Correct Answer : True

Explanation : The rate at which data is generated, distributed, and gathered is referred to as data velocity. High data velocity is created at such a rapid rate that it necessitates the use of specialized processing techniques. The faster data can be captured and processed, the more valuable the data collected will be and the longer it will hold its worth.

5 .

Amongst which of the following is / are the branch of statistics which deals with the development of statistical methods is classified as ______.

A)

Industry statistics

B)

Economic statistics

C)

Applied statistics

D)

None of the above

Correct Answer : Applied statistics

Explaination : The discipline of statistics that works with the development of statistical procedures is known as applied statistics. Planning for data collecting, maintaining data, analyzing, interpreting, and drawing conclusions from data, and finding issues, solutions, and opportunities utilizing analysis are all part of applied statistics. In data analysis and empirical research, these major fosters critical thinking and problem-solving skills.

6 .

Linear Regression is the supervised machine learning model in which the model finds the best fit ______ between the independent and dependent variable.

A)

Curved line

B)

Linear line

C)

Nonlinear line

D)

None of the above

Correct Answer : Linear line

Explaination : Linear Regression is a supervised Machine Learning model that identifies the best fit linear line between the independent and dependent variables, i.e., the linear connection between the dependent and independent variables.

7 .

Which of the following is not a major data analysis approaches?

A)

Data Mining

B)

Text Analytics

C)

Predictive Intelligence

D)

Business Intelligence

Correct Answer : Predictive Intelligence

Explanation : Predictive Analytics is major data analysis approaches not Predictive Intelligence.

8 .

In descriptive statistics, data from the entire population or a sample is summarized with ?

A)

integer descriptors

B)

decimal descriptors

C)

floating descriptors

D)

numerical descriptors

Correct Answer : numerical descriptors

Explanation : In descriptive statistics, data from the entire population or a sample is summarized with numerical descriptors.

9 .

Data Analysis is defined by the which statistician?

A)

John Tukey

B)

William S.

C)

Hans Peter Luhn

D)

Gregory Piatetsky-Shapiro

Correct Answer : John Tukey

Explanation : Data Analysis is defined by the statistician John Tukey in 1961 as "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.

10 .

Amongst which of the following is / are the types of Linear Regression,

A)

Simple Linear Regression

B)

Multiple Linear Regression

C)

Both (A) and (B)

D)

None of the above

Correct Answer : Both (A) and (B)

Explanation : There are two forms of linear regression: simple and multiple. Simple Linear Regression is used when there is only one independent variable and the model must determine the linear connection between it and the dependent variable. Multiple Linear Regression is employed more than one independent variable in the model to determine the link.

11 .

______ refers to the ability to turn your data useful for business.

A)

Variety

B)

Value

C)

Velocity

D)

None of the above

Correct Answer : Value

Explanation : The ability to turn our data into business value is referred to as value. The usefulness of obtained data for our business is referred to as data value. Data, regardless of its magnitude, is rarely useful on its own; to be useful, it must be transformed into insights or knowledge, which is where data processing comes in.

12 .

Correlation is the relationship between_____variables

A)

One

B)

Two

C)

Three

D)

Four

Correct Answer : Two

Explanation : Correlation is the strength of a relationship between two variables, and the Pearson's correlation coefficient measures how strong that relationship is. The correlation of two variables is the statistical link between them :

A positive correlation means that both variables move in the same direction, while a negative correlation means that when one variable's value rises, the other variable's value falls.

13 .

Amongst which of the following is / are the true about regression analysis?

A)

Modeling relationships within the data

B)

Describes associations within the data

C)

Answering yes/no questions about the data

D)

All of the above

Correct Answer : Modeling relationships within the data

Explanation : Regression analysis is used to describe relationships within data, and so it is a collection of statistical methods for estimating relationships between a dependent variable and one or more independent variables. There are various types of regression analysis, including linear, multiple linear, and nonlinear. Simple linear and multiple linear models are the most frequent. Nonlinear regression analysis is typically employed for more difficult data sets with a nonlinear connection between the dependent and independent variables.

14 .

Linear regression analysis is used to predict the value of a variable based on the value of another variable.

A)

True

B)

False

C)

Can Not Say

D)

None of the above

Correct Answer : True

Explanation : Linear regression analysis predicts the value of one variable depending on the value of another. The variable we wish to forecast is referred to as the dependent variable. The variable we are utilizing to predict the value of the other variable is referred to as the independent variable.

15 .

A Linear Regression model's main aim is to find the best fit linear line and the ___ of intercept and coefficients such that the error is minimized.

A)

Linear line

B)

Linear polynomial

C)

Optimal values

D)

None of the above

Correct Answer : Optimal values

Explaination : The basic goal of a Linear Regression model is to determine the best fit linear line and the ideal intercept and coefficient values such that the error is minimized.

A linear regression model describes the relationship between one or more independent variables, X, and a dependent variable, y.

A multiple linear regression model is a type of regression model that has numerous lines of regression.

A multiple linear regression model is yi=β0+β1Xi1+β2Xi2+â‹¯+βpXip+εi, i=1,â‹¯,n

16 .

The Mean Squared Error is a measure of the average of the squares of the residuals.

A)

True

B)

False

C)

Can Not Say

D)

None of the above

Correct Answer : True

Explanation : The degree of inaccuracy in statistical models is measured by the mean squared error (MSE).

The average squared difference between observed and expected values is calculated.

The MSE equals zero when a model has no errors. Its value rises as the model inaccuracy rises.

The mean squared deviation is another name for the mean squared deviation (MSD).

The average squared residual is represented by the mean squared error in regression.

17 .

Logistic regression is used to find the probability of event = Success and event = ____.

A)

Success

B)

Failure

C)

Both (A) and (B)

D)

None of the above

Correct Answer : Failure

Explanation : The likelihood of event=Success and event=Failure is calculated using logistic regression. When the dependent variable is in nature, we should utilize logistic regression. For classification difficulties, logistic regression is commonly employed.

There is no requirement for a linear relationship between the dependent and independent variables in logistic regression. Because it uses a non-linear log transformation on the anticipated odds ratio, it can handle a wide range of relationships.

18 .

A good data analytics solution includes a viable self-service _______.

A)

Data mining

B)

Data warehouse

C)

Data wrangling

D)

None of the above

Correct Answer : Data wrangling

Explanation : A smart data analytics solution incorporates self-service data wrangling and data preparation features so that data may be simply and quickly gathered from a range of incomplete, difficult, or messy data sources and cleansed for mashup and analysis.

19 .

Error is the difference between the actual value and Predicted value and the goal is to reduce this difference.

A)

True

B)

False

C)

Can Not Say

D)

None of the above

Correct Answer : True

Explanation : In statistics, the actual value is the value derived from observation or measurement of the available data. It is also known as the observed value. The expected value is the predicted value of the variable based on the regression analysis.

Linear regression is most commonly used to calculate model error using mean-square error (MSE). MSE is derived by measuring the distance between the observed and anticipated y-values at each value of x and then computing the mean of the squared distances.

20 .

Which of the following is true about hypothesis testing?

A)

modeling relationships within the data

B)

describing associations within the data

C)

estimating numerical characteristics of the data

D)

answering yes/no questions about the data

Correct Answer : answering yes/no questions about the data

Explanation : answering yes/no questions about the data (hypothesis testing)

21 .

The process of quantifying data is referred to as _______.

A)

Coding

B)

Decoding

C)

Structure

D)

Enumeration

Correct Answer : Enumeration

Explanation : Enumeration is the term for the process of quantifying data. Any quantifiable information that can be used for mathematical calculations or statistical analysis is referred to as quantitative data. This type of information aids in the development of real-world decisions based on mathematical derivations.

22 .

A voluminous amount of structured, semi-structured, and unstructured data that has the potential to be mined for information.

A)

Small Data

B)

Big Data

C)

Meta Data

D)

Statistical Data

Correct Answer : Big Data

Explaination : Big Data refers to a large amount of structured, semi-structured, and unstructured data that has the potential to be analyzed and extracted for valuable insights. This term is used to describe datasets that are too large and complex to be processed by traditional data processing applications. Big Data often includes information from various sources such as social media, sensors, and online transactions. By analyzing Big Data, organizations can gain valuable insights, make data-driven decisions, and discover patterns and trends that can lead to innovation and improved business strategies.

23 .

A free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment.

A)

R

B)

Python

C)

Hadoop

D)

Apache Groovy

Correct Answer : Hadoop

Explaination : Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. It is designed to handle big data processing and storage across a cluster of computers, providing scalability and fault tolerance. Hadoop uses a distributed file system (HDFS) and a processing framework (MapReduce) to efficiently process and analyze large volumes of data. It is widely used in the industry for big data analytics and is known for its ability to handle massive amounts of data in parallel.

24 .

The branch of data mining concerned with the prediction of future probabilities and trends.

A)

Predictive Analytics

B)

Behavioral Analytics

C)

Big Data Analytics

D)

In-memory Analytics

Correct Answer : Predictive Analytics

Explanation : Predictive analytics is the correct answer because it specifically deals with the prediction of future probabilities and trends.

It involves the use of statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes.

This branch of data mining focuses on identifying patterns and trends in data to forecast future behavior, allowing businesses to make informed decisions and take proactive actions.

25 .

To glean insights from the data, many analysts and data scientists rely on ______.

A)

Data mining

B)

Data warehouse

C)

Data visualization

D)

All of the above

Correct Answer : Data visualization

Explanation : Many analysts and data scientists use data visualization, or the graphical depiction of data, to assist individuals visually explores and finds patterns and outliers in the data in order to get insights.

Data visualization features are included in a good data analytics system, making data exploration easier and faster.

26 .

Text Analytics, also referred to as Text Mining?

A)

True

B)

False

C)

Can Not Say

D)

None of the above

Correct Answer : True

Explanation : Text analytics uses a combination of machine learning, statistical, and linguistic tools to analyze vast amounts of unstructured material (text that does not have a preset format) in order to draw insights and trends. It enables corporations, governments, researchers, and the media to make critical decisions based on the vast amounts of data available to them.

27 .

______ are used when we want to visually examine the relationship between two quantitative variables.

A)

Pie chart

B)

Scatterplot

C)

Bar graph

D)

Line graph

Correct Answer : Scatterplot

Explanation : Dots are used to indicate values for two different numeric variables in a scatter plot, also known as a scatter chart or a scatter graph.

The values for each data point are indicated by the position of each dot on the horizontal and vertical axes. Scatter plots are used to see how variables relate to one another.

28 .

A graph that uses vertical bars to represent data is called a _______.

A)

Line graph

B)

Bar graph

C)

Scatterplot

D)

All of the above

Correct Answer : Bar graph

Explanation : A bar graph is a graph that employs vertical bars to represent data. Bar graphs are visual representations of data (usually grouped) in the shape of vertical or horizontal rectangular bars, with bar length proportional to data measure. Bar charts are another name for them. In statistics, bar graphs are one of the data management methods.

29 .

Least Square Method uses _______.

A)

Linear circle

B)

Linear sequence

C)

Linear polynomial

D)

Linear regression

Correct Answer : Linear regression

Explanation : Linear regression employs the Least Square Method. The least-squares approach is a type of mathematical regression analysis that determines the best fit line for a collection of data, displaying the relationship between the points visually.

The relationship between a known independent variable and an unknown dependent variable is represented by each piece of data.

30 .

Predictive analytics involves taking historical data ______.

A)

True

B)

False

C)

Can Not Say

D)

None of the above

Correct Answer : True

Explanation : The approach or practice of utilizing data to generate projections about the possibility of certain future events in your organization is known as predictive analytics, which is a form of advanced analytics.

Predictive analytics models unknown future occurrences by combining historical and current data with advanced statistics and machine learning approaches.

It is commonly characterized as utilizing data science and machine learning to learn from an organization's previous collective experience in order to make better decisions in the future.

31 .

With reference to Predictive analytics, it allows organizations to predict customer behavior ______.

A)

True

B)

False

C)

Can Not Say

D)

None of the above

Correct Answer : True

Explanation : Predictive analytics enables businesses to forecast consumer behavior and business results by combining historical and real-time data.

Furthermore, predictive modeling is a subset of this activity that entails constructing and maintaining models, testing and iterating with existing data, and embedding models into applications.

32 .

Customer analytics refers_________.

A)

Marketing: cross-sell, up-sell

B)

Customer Relationship Management: churn analysis and prevention

C)

Pricing: leakage monitoring, promotional effects tracking, competitive price responses

D)

All of the above

Correct Answer : All of the above

Explanation : Customer analytics includes churn analysis and prevention, marketing: cross-sell and up-sell, and pricing: leakage monitoring, promotional effects tracking, and competitive price reactions.

33 .

What is a hypothesis?

A)

A theory that underpins the study

B)

A statement that the researcher wants to test through the data collected in a study

C)

A research question the results will answer

D)

A statistical method for calculating the extent to which the results could have happened by chance

Correct Answer : A statement that the researcher wants to test through the data collected in a study

Explanation : A hypothesis is a proposition that a researcher wishes to evaluate using data from a study. A hypothesis is a conclusion reached after considering evidence.

This is the first step in any investigation, where the research questions are translated into a prediction. Variables, population, and the relationship between the variables are all included.

A research hypothesis is a hypothesis that is tested to see if two or more variables have a relationship.

34 .

The science of examining raw data with the purpose of drawing conclusions about that information.

A)

Predictive Analytics

B)

Descriptive Analytics

C)

Data Analytics

D)

In-memory Analytics

Correct Answer : Data Analytics

Explanation : Data analytics is the science of examining raw data with the purpose of drawing conclusions about that information. It involves the use of various techniques and tools to analyze and interpret data in order to uncover patterns, trends, and insights.

By analyzing data, organizations can make informed decisions, identify areas for improvement, and gain a competitive advantage.

Data analytics encompasses different types of analytics, such as descriptive analytics, predictive analytics, and prescriptive analytics, each serving a specific purpose in extracting meaningful information from data.

35 .

An approach to querying data when it resides in a computer’s random access memory (RAM), as opposed to querying data that is stored on physical disks.

A)

Data Analytics

B)

Deep Analytics

C)

Data Visualisation

D)

In-memory Analytics

Correct Answer : In-memory Analytics

Explaination : In-memory analytics refers to the approach of querying data that is stored in a computer's random access memory (RAM) instead of physical disks.

This approach allows for faster and more efficient data retrieval and analysis as accessing data from RAM is much quicker than accessing it from disks.

By keeping the data in memory, in-memory analytics enables real-time analysis and faster decision-making, making it suitable for applications that require quick and interactive data processing.

36 .

What is the name of the programming framework originally developed by Google that supports the development of applications for processing large data sets in a distributed computing environment?

A)

Hive

B)

MapReduce

C)

Hadoop

D)

Zookeeper

Correct Answer : MapReduce

Explaination : MapReduce is a programming framework originally developed by Google that supports the development of applications for processing large data sets in a distributed computing environment.

It allows for parallel and distributed processing of data across a cluster of computers, making it efficient for handling big data. Hive is a data warehouse infrastructure, Zookeeper is a coordination service, and Hadoop is an open-source framework that includes MapReduce as one of its components.

37 .

A method of storing data within a system that facilitates the collocation of data in various schemata and structural forms.

A)

Data Lake

B)

Deep Analytics

C)

Data Visualisation

D)

Big Data Management

Correct Answer : Data Lake

Explaination : A data lake is a method of storing data within a system that allows for the collocation of data in various schemata and structural forms. It is a centralized repository that stores raw, unprocessed data from different sources, such as databases, applications, and IoT devices.

Data lakes enable organizations to store large volumes of data in its native format, without the need for upfront data modeling or transformation. This flexibility allows for more efficient data analysis and enables data scientists to explore and extract insights from diverse data sets.

38 .

Linear-regression models are relatively simple and provide an easy-to-interpret mathematical formula that can generate ______.

A)

Conclusion

B)

Interpretation

C)

Predictions

D)

None of the above

Correct Answer : Predictions

Explaination : Linear-regression models are straightforward and provide a basic mathematical method for generating predictions. Linear regression can be used in a variety of corporate and academic study.

39 .

Amongst which of the following is / are the applications of Linear Regression,

A)

Behavioral

B)

Biological

C)

Social sciences

D)

All of the above

Correct Answer : All of the above

Explanation : Linear regression is utilized in a variety of fields, including biology, behavioral science, environmental research, and business. Linear regression models have proven to be a reliable and scientific means of forecasting the future.

Because linear regression is a well-known statistical process, its properties are well understood and linear regression models may be trained quickly.

40 .

With reference to data, dependent and independent variables should be quantitative.

A)

True

B)

False

C)

Can Not Say

D)

None of the above

Correct Answer : True

Explanation : Dependent and independent variables should be quantitative when it comes to data. Both the dependent and independent variables should have a numerical value. Religious, major field of study and residential region categorical factors must be represented as binary variables or other sorts of contrast variables.

41 .

______ is the cyclical process of collecting and analyzing data during a research study.

A)

Constant analysis

B)

Interim Analysis

C)

Extremis Analysis

D)

All of the above

Correct Answer : Interim Analysis

Explanation : The cyclical process of gathering and assessing data throughout a research Endeavour is known as interim analysis.

42 .

An advantage of using computer programs for qualitative data is that they _______.

A)

Help in storing and organizing data

B)

Can reduce time required to analyze data

C)

Make many procedures available that are rarely done by hand due to time constraints

D)

All of the above

Correct Answer : All of the above

Explanation : Qualitative data is that they can reduce time required to analyze data, help in storing and organizing data and make many procedures available that are rarely done by hand due to time constraints.

43 .

Data Modeling is the process of analyzing the data objects _____.

A)

True

B)

False

C)

Can Not Say

D)

None of the above

Correct Answer : True

Explanation : The practice of evaluating data items and their relationships with other things is known as data modeling. It's utilized to look into the data requirements for various business activities. The data models are constructed in order to store the information in a database.

44 .

______ are the basic building blocks of qualitative data.

A)

Data chunk

B)

Numeric figures

C)

Categories

D)

None of the above

Correct Answer : Categories

Explanation : The fundamental building elements of qualitative data are categories.

The descriptive and conceptual results gathered through surveys, interviews, or observation is referred to as qualitative data.

We can explore concepts and further explain quantitative outcomes by analyzing qualitative data.

45 .

Metadata and data modeling tools support the creation and documentation of models ____.

A)

True

B)

False

C)

Can Not Say

D)

None of the above

Correct Answer : True

Explanation : Models representing the structures, flows, mappings and transformations, connections, and quality of data may be created and documented using metadata and data modeling tools.

46 .

For each value of the _____, the distribution of the dependent variable must be normal.

A)

Depended variable

B)

Independent variable

C)

Intermediate variable

D)

None of the above

Correct Answer : Independent variable

Explanation : The dependent variable's distribution must be normal for each value of the independent variable.

For all values of the independent variable, the variance of the dependent variable's distribution should be constant.

The dependent variable should have a linear relationship with each independent variable, and all observations should be independent.

47 .

Residual plot helps in analyzing the model using the values of residues.

A)

True

B)

False

C)

Can Not Say

D)

None of the above

Correct Answer : True

Explanation : The residue plot aids in the analysis of the model by displaying the values of the residues. It's shown as a line between the projected values and the residual. Their values are all the same.

The point's distance from 0 indicates how inaccurate the prediction was for that number.

If the value is positive, the probability of success is minimal. If the value is negative, the probability of success is high. A number of 0 implies that the forecast is perfect. The model can be improved by detecting residual patterns.

48 .

The Process of describing the data that is huge and complex to store and process is known as _______.

A)

Data cleaning

B)

Analytics mining

C)

Big data

D)

None of the above

Correct Answer : Big data

Explanation : Big data is a term used to describe the process of describing data that is large and difficult to store and interpret. Big data analytics is the use of advanced analytic techniques to very large, heterogeneous big data sets, which can contain structured, semi-structured, and unstructured data, as well as data from many sources and sizes ranging from terabytes to zettabytes.

49 .

In descriptive statistics, data from the entire population or a sample is summarized with _______.

A)

Integer descriptor

B)

Decimal descriptor

C)

Both (A) and (B)

D)

Numerical descriptor

Correct Answer : Numerical descriptor

Explanation : Data from the full population or a sample is summarized using numerical descriptors in descriptive statistics.

50 .

Amongst which of the following is / are the challenges overcome by the data strategy to make a business in a strong position_______.

A)

Lack of deep understanding of critical parts of the business

B)

Inefficient movement of data between different parts of the business

C)

Data privacy, data integrity, and data quality issues that undercut your ability to analyze data

D)

All of the above

Correct Answer : All of the above

Explaination : Data strategy aids in the development of a strong firm. It also puts a company in a good position to overcome obstacles.

Issues with data privacy, integrity, and quality that limit your capacity to evaluate data Lack of understanding of important business components and the processes that keep them run Inefficient data transportation between different portions of the organization, or data duplication by several business units, as well as a lack of clarity about current business needs and goals.

51 .

Tableau is a ______ tool.

A)

Analytical

B)

Visualization

C)

Data Exploration

D)

All of the above

Correct Answer : All of the above

Explanation : Tableau is a visualization software program. Tableau gives data scientists a versatile front-end for data exploration with the analytical depth they need.

Data scientists may execute complicated quantitative studies in Tableau and communicate visual findings to encourage improved understanding and collaboration with data by utilizing advanced computations, R and Python integration, quick cohort analysis, and predictive capabilities.

52 .

Big data analytics refers to collecting, processing, cleaning, and analyzing large datasets______.

A)

True

B)

False

C)

Can Not Say

D)

None of the above

Correct Answer : True

Explanation : Big data analytics is the process of gathering, processing, cleaning, and analyzing enormous datasets in order to assist businesses operationalize their data.

53 .

Clustering belongs to ______ data analysis.

A)

Supervised

B)

Unsupervised

C)

Both (A) and (B)

D)

None of the above

Correct Answer : Unsupervised

Explanation : Unsupervised data analysis includes clustering. Without any prior knowledge, the data's hidden structure is discovered and emphasized. Popular clustering techniques include K-means, K-nearest neighbors, and hierarchical clustering.

54 .

Amongst which of the following is / are not a major data analysis approach?

A)

Data Mining

B)

Text Analytics

C)

Predictive Intelligence

D)

Business Intelligence

Correct Answer : Predictive Intelligence

Explanation : The practice of collecting data about consumers' and potential consumers' behaviors/actions from a number of sources and perhaps integrating it with profile data about their qualities is known as predictive intelligence.

55 .

Amongst which of the following is / are not an example of social media?

A)

Twitter

B)

Instagram

C)

Both (A) and (B)

D)

None of the above

Correct Answer : None of the above

Explanation : Social media is a type of computer-based technology that allows people to share their ideas, thoughts, and information with others via virtual networks and communities. Social media is an internet-based platform that allows people to share content such as personal information, documents, films, and images quickly and electronically.

56 .

Amongst which of the following is / are the features of Tableau for data analytics________.

A)

Data Blending

B)

Real time analysis

C)

Collaboration of data

D)

All of the above

Correct Answer : All of the above

Explanation : Tableau software's finest features are data blending, real-time analysis, and data collaboration. The beautiful thing about Tableau software is that it can be used without any technical or programming knowledge.

The tool has piqued the curiosity of people from many walks of life, including business, researchers, and other industries.

57 .

______ is a category, also called supervised machine learning methods in which the data is split on two parts.

A)

Clustering

B)

Classification

C)

Data mining

D)

None of the above

Correct Answer : Classification

Explaination : Classification is a type of supervised machine learning approach in which the data is divided into two parts: a training set and a validation set.

A model is trained from the training set by extracting the most discriminative characteristics that are previously connected with known outputs.

This model is then tested on a test set, in which we evaluate the learnt model's efficiency by creating appropriate outputs for a particular set of input values.

58 .

Last summer, Splunk announced a new product to search, access and report on Hadoop data sets. What is this product called?

A)

Hunk

B)

MongoDB

C)

Splunk Cloud

D)

Splunk Storm

Correct Answer : Hunk

Explaination : Hunk is also known as Splunk Analytics for Hadoop.

59 .

According to a study conducted by IBM, what is the largest single source where data is gathered?

A)

Email

B)

Log Data

C)

Social Media

D)

Business Transactions

Correct Answer : Business Transactions

Explanation : According to IBM, 90% of organizations gather data on this subject – more than any other. Social Media is the lowest on this list, coming in at 39%

60 .

Which of the following is/are correct types of data?

A)

Unstructured Data

B)

Semi-structured Data

C)

Both (A) and (B)

D)

Semi Data

Correct Answer : Both (A) and (B)

Explanation : Semi-structured data and unstructured data are both correct types of data.

Semi-structured data refers to data that does not have a fixed structure, but still has some organizational elements, such as tags or labels.

Unstructured data, on the other hand, does not have any predefined structure or organization. Both types of data are important in different contexts and require different approaches for analysis and storage.

61 .

If the null hypothesis is false then which of the following is accepted?

A)

Null Hypothesis

B)

Alternative Hypothesis.

C)

Both (A) and (B)

D)

None of the above

Correct Answer : Both (A) and (B)

Explanation : The alternative hypothesis is accepted if the null hypothesis is untrue. An alternative theory is a proposition that a researcher is testing in hypothesis testing.

From the researcher's perspective, this assertion is correct, and it finally proves to reject the null hypothesis and replace it with a different one. The difference between two or more variables is anticipated in this hypothesis.

62 .

Alternative Hypothesis is also called as?

A)

Null Hypothesis

B)

Research Hypothesis

C)

Simple Hypothesis

D)

None of the above

Correct Answer : Research Hypothesis

Explanation : The alternative hypothesis is the assertion that is being tested against the null hypothesis. Ha or H1 are common abbreviations for alternative hypotheses.

The alternative hypothesis is the hypothesis that is inferred from a null hypothesis that has been rejected.

It is best stated as an explanation for why the null hypothesis was rejected. It is also known as the research hypothesis. Unlike the null hypothesis, the researcher is usually most interested in the alternative hypothesis.

63 .

By 2025, the volume of data will increase to_____.

A)

ZB

B)

YB

C)

EB

D)

TB

Correct Answer : ZB

Explanation : It is projected that 2.5 quintillion bytes of data are created every day, with the volume of digital data expected to reach Zeta Byte by 2025.

64 .

According to a very recent Jaspersoft Survey, what is the most popular big data store?

A)

MongoDB

B)

Hadoop HDFS

C)

Both (A) and (B)

D)

Relational Databases

Correct Answer : Relational Databases

Explanation : The most popular big data store according to the recent Jaspersoft Survey is Relational Databases.

65 .

________ Analysis is used to analyze a system in terms of its requirements to identify its impact on customers’ satisfaction. Fill in the blank.

A)

Impact

B)

Kano

C)

Paretto

D)

RootCause

Correct Answer : Kano

Explaination : The Kano analysis is used to analyze a system in terms of its requirements and identify its impact on customers' satisfaction.

This analysis helps to categorize customer preferences into different types: basic requirements, performance requirements, and excitement requirements.

By understanding these different types of requirements, businesses can prioritize their efforts and focus on delivering features and functionalities that will truly satisfy their customers.