MCQ on Data Science | Multiple Choice Questions With Answer
1. Data Analytics uses ___ to get insights from data.
Statistical figures
Numerical aspects
Statistical methods
None of the mentioned above
Answer: C) Statistical methods
Explanation:
To gain insights from data, Data Analytics uses 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).
2. Amongst which of the following is/are the branch of statistics that deals with the development of statistical methods is classified as ___.
Industry statistics
Economic statistics
Applied statistics
None of the mentioned above
Answer: C) Applied statistics
Explanation:
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.
3. Linear Regression is the supervised machine learning model in which the model finds the best fit ___ between the independent and dependent variable.
Linear line
Nonlinear line
Curved line
All of the mentioned above
Answer: A) Linear line
Explanation:
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.
4. Amongst which of the following is / are the types of Linear Regression,
Simple Linear Regression
Multiple Linear Regression
Both A and B
None of the mentioned above
Answer: C) 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.
5. Amongst which of the following is / are the true about regression analysis?
Describes associations within the data
Modeling relationships within the data
Answering yes/no questions about the data
All of the mentioned above
Answer: B) 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.
6. Linear regression analysis is used to predict the value of a variable based on the value of another variable.
True
False
Answer: A) 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.
7. 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.
Optimal values
Linear line
Linear polynomial
None of the mentioned above
Answer: A) Optimal values
Explanation:
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+⋯+βpXip+εi, i=1,⋯,n
8. Error is the difference between the actual value and Predicted value and the goal is to reduce this difference.
True
False
Answer: A) 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.
9. The process of quantifying data is referred to as ___.
Decoding
Structure
Enumeration
Coding
Answer: C) 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. To answer inquiries like how many, quantitative data is used. How often do you do it? How much is it? This information can be confirmed and validated.
10. Text Analytics, also referred to as Text Mining?
True
False
Answer: A) 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.
11. ___ are used when we want to visually examine the relationship between two quantitative variables.
Bar graph
Scatterplot
Line graph
Pie chart
Answer: A) Bar graph
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.
12. A graph that uses vertical bars to represent data is called a ____.
Bar graph
Line graph
Scatterplot
All of the mentioned above
Answer: A) 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.
13. Data Analysis is a process of,
Inspecting data
Data Cleaning
Transforming of data
All of the mentioned above
Answer: D) All of the mentioned above
Explanation:
The process of reviewing, cleansing, and manipulating data with the objective of identifying usable information, informing conclusions, and assisting decision-making is known as data analysis. Data analysis is important in today's business environment since it helps businesses make more scientific decisions and run more efficiently.
14. Least Square Method uses ___.
Linear polynomial
Linear regression
Linear sequence
None of the mentioned above
Answer: B) 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.
15. What is a hypothesis?
A statement that the researcher wants to test through the data collected in a study
A research question the results will answer
A theory that underpins the study
A statistical method for calculating the extent to which the results could have happened by chance
Answer: A) A statement that the researcher wants to test through the data collected in a studyp
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.
16. Linear-regression models are relatively simple and provide an easy-to-interpret mathematical formula that can generate ___.
Predictions
Interpretation
Conclusion
None of the mentioned above
Answer: A) Predictions
Explanation:
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.
17. Amongst which of the following is / are the applications of Linear Regression,
Biological
Behavioral
Social sciences
All of the mentioned above
Answer: D) All of the mentioned 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.
18. With reference to data, dependent and independent variables should be quantitative.
True
False
Answer: A) 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.
19. For each value of the ___, the distribution of the dependent variable must be normal.
Independent variable
Depended variable
Intermediate variable
None of the mentioned above
Answer: A) 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.
20. Residual plot helps in analyzing the model using the values of residues.
True
False
Answer: A) 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.
21. Amongst which of the following is / are not a major data analysis approach?
Predictive Intelligence
Business Intelligence
Text Analytics
Data Mining
Answer: A) 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.
22. By 2025, the volume of data will increase to,
TB
YB
ZB
EB
Answer: C) 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.
23. Alternative Hypothesis is also called as?
Null Hypothesis
Research Hypothesis
Simple Hypothesis
None of the mentioned above
Answer: B) 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.
24. If the null hypothesis is false then which of the following is accepted?
Alternative Hypothesis.
Null Hypothesis
Both A and B
None of the mentioned above
Answer: C) 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.
25. Amongst which of the following is / are not an example of social media?
Both A and B
None of the mentioned above
Answer: C) Both A and B
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.
26. Velocity is the speed at which the data is processed -
True
False
Answer: A) 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.
27. ___ refers to the ability to turn your data useful for business.
Value
Variety
Velocity
None of the mentioned above
Answer: A) 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.
28. Correlation is the relationship between two variables -
One
Two
Zero
All of the mentioned above
Answer: B) 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.
29. The Mean Squared Error is a measure of the average of the squares of the residuals.
True
False
Answer: A) 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.
30. Logistic regression is used to find the probability of event = Success and event = ____.
Failure
Success
Both A and B
None of the mentioned above
Answer: A) 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.
31. A good data analytics solution includes a viable self-service ___.
Data mining
Data wrangling
Data warehouse
None of the mentioned above
Answer: B) 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.
32. To glean insights from the data, many analysts and data scientists rely on ___.
Data mining
Data visualization
Data warehouse
All of the mentioned above
Answer: B) 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.
33. Predictive analytics involves taking historical data -
True
False
Answer: A) 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.
34. With reference to Predictive analytics, it allows organizations to predict customer behavior -
True
False
Answer: A) 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.
35. Customer analytics refers -
Customer Relationship Management: churn analysis and prevention
Marketing: cross-sell, up-sell
Pricing: leakage monitoring, promotional effects tracking, competitive price responses
All of the mentioned above
Answer: D) All of the mentioned 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.
36. ___ is the cyclical process of collecting and analyzing data during a research study.
Extremis Analysis
Constant analysis
Interim Analysis
All of the mentioned above
Answer: C) Interim Analysis
Explanation:
The cyclical process of gathering and assessing data throughout a research Endeavour is known as interim analysis.
37. An advantage of using computer programs for qualitative data is that they ___.
Can reduce time required to analyze data
Help in storing and organizing data
Make many procedures available that are rarely done by hand due to time constraints
All of the mentioned above
Answer: D) All of the mentioned 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.
38. Data Modeling is the process of analyzing the data objects -
True
False
Answer: A) 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.
39. ___ are the basic building blocks of qualitative data.
Categories
Data chunk
Numeric figures
None of the mentioned above
Answer: A) 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.
40. Metadata and data modeling tools support the creation and documentation of models -
True
False
Answer: A) 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.
41. The Process of describing the data that is huge and complex to store and process is known as ___.
Analytics mining
Data cleaning
Big data
None of the mentioned above
Answer: C) 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.
42. In descriptive statistics, data from the entire population or a sample is summarized with ___.
Numerical descriptor
Decimal descriptor
Integer descriptor
All of the mentioned above
Answer: A) Numerical descriptor
Explanation:
Data from the full population or a sample is summarized using numerical descriptors in descriptive statistics.
43. Customer behavior analytics is about understanding how your customers act -
True
False
Answer: A) True
Explanation:
Understanding how your customers behave across each channel and interaction point is the goal of customer behavior analytics. Understanding consumer behavior may aid in customer acquisition, engagement, and retention for your company.
44. Data Analysis is defined by the statistician?
John Tukey
Hans Peter Luhn
Gregory Lon
None of the mentioned above
Answer: A) John Tukey
Explanation:
John Tukey, a statistician, defined data analysis. Tukey began his career in statistics, and he was fascinated with data analysis challenges and methodologies. Some people remember him for pioneering exploratory data analysis, but he also made significant contributions to analysis of variance, regression, and a wide range of applications. This study examines some of the most notable contributions in these fields.
45. Amongst which of the following is / are the challenges overcome by the data strategy to make a business in a strong position -
Data privacy, data integrity, and data quality issues that undercut your ability to analyze data
Inefficient movement of data between different parts of the business
Lack of deep understanding of critical parts of the business
All of the mentioned above
Answer: D) All of the mentioned above
Explanation:
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.
46. Tableau is a ___ tool.
Visualization
Analytical
Data Exploration
All of the mentioned above
Answer: D) All of the mentioned 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.
47. Big data analytics refers to collecting, processing, cleaning, and analyzing large datasets -
True
False
Answer: A) True
Explanation:
Big data analytics is the process of gathering, processing, cleaning, and analyzing enormous datasets in order to assist businesses operationalize their data.
48. Amongst which of the following is / are the features of Tableau for data analytics -
Data Blending
Real time analysis
Collaboration of data
All of the mentioned above
Answer: D) All of the mentioned 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.
49. ___ is a category, also called supervised machine learning methods in which the data is split on two parts.
Classification
Clustering
Data mining
None of the mentioned above
Answer: A) Classification
Explanation:
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.
50. Clustering belongs to ___ data analysis.
Supervised
Unsupervised
Both A and B
None of the mentioned above
Answer: B) 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.
51. Unprocessed data or processed data are observations or measurements that can be expressed as text, numbers, or other types of media?
True
False
Answer: A) True
Explanation:
Data are observations or measurements (unprocessed or processed) represented as text, numbers, or multimedia. Information that has been transformed into a form that is more efficient for movement or processing is referred to as data in computing.
52. With reference to computing aspects ___ is a symbolic representation of facts or concepts from which information may be obtained with a reasonable degree of confidence.
Program
Knowledge
Data
Flowchart
Answer: A) Program
Explanation:
With reference to computing aspects data is a symbolic representation of facts or concepts from which information may be obtained with a reasonable degree of confidence.
53. Which of the following can be considered to be the primary source of unstructured data among the others?
Internet webs
All of the mentioned above
Answer: D) All of the mentioned above
Explanation:
Facebook, Twitter and Internet webs can be considered to be the primary source of unstructured data among the others.
54. Amongst which of the following is/are the examples of structured data -
Videos
Employee's name, employee's id, employee's age
Audio files
All of the mentioned above
Answer: B) Employee's name, employee's id, employee's age
Explanation:
Structured data is extremely particular and is recorded in a set format, whereas unstructured data is a mashup of many different forms of data that are all stored in their original formats, as opposed to structured data. In above question, Employees name, employee's id, employee's age is an example of structured data.
55. Amongst which of the following step is performed by data scientist after acquiring the data?
Deletion
Data Replication
Data Integration
Data Cleansing
Answer: D) Data Cleansing
Explanation:
after acquiring the data, data scientists perform Data Cleansing. Data cleansing is a critical step in preparing data for use in subsequent operations, whether in operational activities or in downstream analysis and reporting. It is most effectively accomplished with the use of data quality technologies. Depending on their purpose, these tools can perform a number of tasks ranging checking basic typographical errors to validating values against a known true reference set.
56. Quantitative data mainly deals with ______.
Audio data
Images data
Numeric data
Videos
Answer: C) Numeric data
Explanation:
Quantitative data mainly deals with Numeric data Quantitative data is defined as the value of data in the form of counts or numbers, where each data-set has a unique numerical value associated with it, and where each data-set has a unique numerical value associated with it.
57. Big Data is a term that refers to data that is both too massive and impossible to be stored in _____.
Traditional databases
Big Databases
SQL Databases
All of the mentioned above
Answer: A) Traditional databases
Explanation:
Big Data is a term that refers to data that is both too massive and impossible to be stored in Traditional databases. The quantities, letters, or symbols on which computer operations are done, which may be stored and conveyed in the form of electrical impulses and recorded on magnetic, optical, or mechanical storage media.
58. Big Data is a field dedicated to,
Storage of large collections of data
Processing
Analysis
All of the mentioned above
Answer: D) All of the mentioned above
Explanation:
Big Data is a field dedicated to Storage of large collections of data, Processing and Analysis. Big data is defined as data that is so massive, quick, or complicated that it is difficult or impossible to process it using traditional methods, as opposed to little data. Having access to and keeping massive amounts of data for the purpose of analytics has been around for quite some time. The concept of big data, on the other hand, gained traction in the early 2000s.
59. Data that is less than 10 GB in size can be considered to be a little amount of data.
Small
Medium
Big
All of the mentioned above
Answer: A) Small
Explanation:
Data that is less than 10 GB in size can be considered as a small data. Small data is data that is 'small' enough to be comprehended by a human being. It is information in a volume and manner that makes it easily accessible, instructive, and actionable for the intended audience.
60. Which of the following are benefits of Data Processing?
Cost Reduction
Time Reductions
Smarter Business Decisions
All of the mentioned above
Answer: D) All of the mentioned above
Explanation:
When data is collected and transformed into useful information, this is referred to as data processing. Data processing is typically undertaken by a data scientist or team of data scientists, and it is critical that it is done correctly in order to avoid having a negative impact on the final product, or data output.
Rather than starting with unstructured data in its raw form, data processing transforms information into a more understandable format (graphs, documents, etc.), providing it the form and context that are required for it to be processed by computers and used by personnel throughout an organization.
61. Which is the process of examining large and varied data sets?
Machine learning
Cloud computing
Big data analytics
All of the mentioned above
Answer: D) All of the mentioned above
Explanation:
Big data analytics is the process of examining large and varied data sets. In the context of big data analytics, the application of advanced analytic techniques to extremely large and heterogeneous big data sets that contain structured, semi-structured, and unstructured data, from a variety of sources, and in various sizes ranging from terabytes to zettabytes is described.
62. Data Identification → Data Acquisition & Filtering → Data Extraction → Data Validation & Cleansing, are the phases of?
Data Analytics Lifecycle
System Analysis and Design
Software Development and Life Cycle
None of the mentioned above
Answer: A) Data Analytics Lifecycle
Explanation:
Data Identification, Data Acquisition & Filtering, Data Extraction, Data Validation & Cleansing are the phases of Data Analytics Lifecycle. The Data Analytics Lifecycle is a diagram that depicts these steps for professionals that are involved in data analytics projects. The phases of the Data Analytics Lifecycle are organized in a systematic manner to build a Data Analytics Lifecycle. Each phase has its own significance as well as its own set of traits.
63. Hadoop is a framework that is free and open source.
True
False
Answer: A) True
Explanation:
Hadoop is an open-source platform. The Hadoop software library is a framework that enables for the distributed processing of massive data sets across clusters of computers using simple programming models. It is a component of the Apache Hadoop software library. It is intended to grow from a small number of servers to thousands of devices, each of which can do computing and storage on its own.
64. Hadoop File System is constantly required to deal with enormous amounts of data ____.
Network
Clusters
Data sets
None of the mentioned above
Answer: C) Data sets
Explanation:
Hadoop File System is constantly required to deal with enormous amounts of data sets. HDFS is a distributed file system that can handle big data volumes and is designed to run on low-cost commodity computing gear. It is used to grow a single Apache Hadoop cluster to hundreds (or even thousands) of nodes by using a distributed computing model. HDFS is one of the three key components of Apache Hadoop, the other two being MapReduce and YARN. HDFS is used to store and organize data.
65. Hadoop is a framework that is used to work with _____.
MapReduce, Hive and HBase
MapReduce, MySQL and Google Apps
MapReduce, Hummer and Iguana
MapReduce, Heron and Trumpet
Answer: A) MapReduce, Hive and HBase
Explanation:
Hadoop is a framework that is used to work with MapReduce, Hive and HBase. Hadoop is an open-source framework that can be used to store and process enormous datasets ranging in size from gigabytes to petabytes of data in a scalable and efficient manner. As opposed to employing a single huge computer to store and analyze all of the data, Hadoop enables for the clustering of numerous computers to analyze enormous datasets in parallel, allowing for faster analysis.
66. Amongst which of the following accurately describe Hadoop?
Open-source
Real-time
Java-based
Distributed computing approach
Answer: B) Real-time
Explanation:
Hadoop is a Real-time data processing framework. Hadoop was originally intended to be used for batch processing. That is, take a large dataset as input and analyze it all at the same time, then create a large output dataset. The very concept of MapReduce is geared toward batch processing rather than real-time processing. This was true from the beginning of Hadoop's existence; today, however, there are numerous options to use Hadoop in an even more real-time manner.
67. ___ has the world's largest Hadoop cluster.
Apple
Datamatics
None of the mentioned above
Answer: C) Facebook
Explanation:
Facebook has the world’s largest Hadoop cluster.
68. Amongst which of the following is a correct statement?
Machine learning emphasizes on prediction, based on well-known properties learned from the training data
Data Cleaning emphasizes on prediction, based on well-known properties learned from the training data
Both a and b
None of the mentioned above
Answer: A) Machine learning emphasizes on prediction, based on well-known properties learned from the training data
Explanation:
Machine learning emphasizes on prediction, based on well-known properties learned from the training data. Machine learning is the study of computer algorithms that can improve themselves automatically as a result of their experience and the usage of data collected from various sources. It is considered to be a component of AI. Machine learning algorithms create a model based on sample data, known as training data, in order to make predictions or choices without being explicitly taught to do so. They can accomplish this without being explicitly coded.
69. Which of the characteristics of big data is, in terms of importance, more concerned with data science?
Variety
Velocity
Volume
None of the mentioned above
Answer: A) Variety
Explanation:
Variety in data is a main characteristic of big data which is more concerned with data science.
70. In which of the following areas do information management firms specialize in analytical capabilities?
Stream Computing
Content Management
Information Integration
All of the mentioned above
Answer: D) All of the mentioned above
Explanation:
Stream Computing, Content Management and Information Integration are the areas in which information management firms specialize in analytical capabilities.
71. The use of reporting and visualization features in Data Analytics refers to,
Processing of data
User-friendly representation
Both A and B
None of the mentioned above
Answer: C) Both A and B
Explanation:
The use of reporting and visualization features in Data Analytics refers to the processing of data and User-friendly representation. The graphical display of information and data is referred to as data visualization. Data visualization tools, which make use of visual components like as charts, graphs, and maps, make it easier to detect and analyze trends, outliers, and patterns in large amounts of information.
72. BI stands for ____.
Business Information
Business Initiation
Business Intelligence
Business Insider
Answer: C) Business Intelligence
Explanation:
BI stands for Business Intelligence. Business Intelligence (BI) is concerned with complicated techniques and technology that assist end-users in analyzing data and performing decision-making activities in order to expand their businesses. Business intelligence is essential in the management of business data and the management of performance.
73. The primary introduction of Power BI was dependent on,
Microsoft Word
Microsoft Excel
Microsoft Outlook
Microsoft PowerPoint
Answer: B) Microsoft Excel
Explanation:
The primary introduction of Power BI was dependent on Microsoft Excel. It is possible to consolidate self-service and enterprise data into a single view with Power BI, even when the data comes from multiple sources.
74. To consolidate inquiries in Power BI, what method do you employ?
Join Queries
Union Queries
Both A & B
None of the above
Answer: A) Join Queries
Explanation:
To consolidate inquiries in Power BI, Join Queries method employ. When we combine data, we connect to two or more data sources, shape them as needed, and then consolidate them into a relevant query for the end user. The Power Query Editor in Power BI Desktop makes extensive use of the right-click menus as well as the Transform ribbon to perform complex transformations. The majority of the options available through the ribbon can also be accessed by right-clicking an object on the ribbon, such as a column, and selecting from the menu that appears.
75. What is the most effective method of preparing your data for Power BI?
The user of a star schema
Load all tables
Include multiple objects
None of the above
Answer: A) User of a star schema
Explanation:
The most effective method of preparing data for Power BI is a User of a star schema. Among relational data warehouses, the star schema is a mature modeling method that has been widely implemented. In order to comply with this requirement, modelers must categorize their model tables as either dimensions or facts.
76. Access to Streaming Data is associated with _____.
System administrator
HDFS
Network System
None of the mentioned above
Answer: B) HDFS
Explanation:
Access to Streaming Data is associated with HDFS. In the Hadoop distribution, there is an application called Hadoop streaming that may be used to stream data. Using the tool, you can construct and run Map/Reduce tasks that can use any executable or script as the mapper and/or the reducer, depending on your preferences.
77. Power BI is used by a variety of companies, including Facebook, Twilio, GitHub, and MailChimp as,
Online services
Database data sources
File data sources
None of the mentioned above
Answer: A) Online services
Explanation:
Power BI is used by a variety of companies, including Facebook, Twilio, GitHub, and MailChimp as Online services. In any organization, systems generate a large amount of data, which can be measured in terabytes, petabytes, or even exabytes in some instances.
Businesses use Business Intelligence to evaluate this data and turn it into actionable information (decisions), and the entire process is referred to as business intelligence. It is undeniable that the success of the firm is dependent on the decisions that are made as a result of business intelligence.
78. When it comes to Power BI Desktop, which of the following might be regarded as the most important feature?
Data
Report
Dashboard
All of the mentioned above
Answer: D) All of the mentioned above
Explanation:
Data, reports, and dashboards are the most important features. Power BI Desktop is used to gather, organize, transform, and visualize data in various ways. With Power BI Desktop, we can connect to a variety of different data sources and merge them (a process known as modeling) into a single data model for analysis.
79. Amongst which of the following is a must before using any technology to evaluate your data,
Study the dataset
Organize dataset
Remove impurities from the data set
All of the mentioned above
Answer: D) All of the mentioned above
Explanation:
Before using any technology to evaluate your data we must study the dataset, organize dataset and remove impurities from the data set. Before we begin collecting data, we must develop a detailed analysis strategy that will guide us through the various steps of the research process, from summarizing and characterizing the data to testing our hypotheses.
80. Power BI modeling refers to the relationships that exist between your data sources.
True
False
Answer: A) True
Explanation:
Data Modeling is one of the aspects in a business intelligence tool that is used to connect multiple data sources through the usage of a relationship. A relationship explains how data sources are connected to one another, and we can use relationships to generate fascinating data visualizations across a variety of data sets. In Power BI, we can also see the "Relationship" between two variables in a data model.
0 Comments