This page was exported from IT Certification Exam Braindumps [ http://blog.braindumpsit.com ] Export date:Sat Apr 12 15:32:09 2025 / +0000 GMT ___________________________________________________ Title: [2024] Practice with these CBDA dumps Certification Sample Questions [Q20-Q44] --------------------------------------------------- [2024] Practice with these CBDA dumps Certification Sample Questions Get Instant Access of 100% REAL CBDA DUMP Pass Your Exam Easily NO.20 A company wants to gauge the thoughts of their employees towards a new company product. On the 25th of March the interviewer makes a list of all employees who were at work on that day and then chooses a subset of those employees to interview. Which term describes the list of all employees present on March 25th?  Population of interest  Survey sample  Sampling frame  Sample weights ExplanationThe sampling frame is the term that describes the list of all employees present on March 25th, because it is a technique that defines the set of elements from which a sample is drawn. The sampling frame should ideally match the population of interest, which is the group of elements that the researcher wants to study or make inferences about. In this case, the population of interest is the employees of the company, and the sampling frame is the subset of employees who were at work on a specific day. The survey sample is the technique that selects a portion of the sampling frame to participate in the survey. The sample weights are the technique that assigns different values or importance to each element in the sample, based on their representation in the population. References:*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 2: Source Data*Understanding the Guide to Business Data Analytics, page 14*CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 14NO.21 The definition of data elements is different across various data sources. The organization is looking to improve the usability of data across the organization. Which practice would help address this problem?  Data governance  Data quality  Data architecture  Data ethics ExplanationData governance is the practice of establishing and enforcing policies, standards, roles, and responsibilities for the management and use of data across the organization. Data governance helps to address the problem of inconsistent data definitions across various data sources by ensuring that data is properly defined, documented, classified, and aligned with the business objectives and requirements12. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 292: Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program, John Ladley, 2012, p. 3.NO.22 The team has completed their analysis on a vast amount of collected data and agree on their recommendations for action.However, they are having difficulty in developing the appropriate messages to support their recommendations.The business analysis professional suggests which technique to assist the team?  T-Testing  Simulation  Visioning  Storyboarding ExplanationStoryboarding is a technique that helps the team to develop the appropriate messages to support their recommendations by creating a visual sequence of the main points, evidence, and actions. Storyboarding helps the team to organize their thoughts, identify gaps, and communicate their findings in a clear and compelling way12 References: 1: Developing Key Messages for Effective Communication – MSKTC 2: 11 Ways Highly Successful Leaders Support Their Team – RedboothNO.23 The results for a certification exam were revealed in percentage and percentile. The results for one of the attendees was: 75%, 90th percentile. What is the value in sharing the percentile score?  The percentile score provides value by assessing the attendee’s score against the average score for that exam  While the exam score is an objective score, the percentile is a relative score that assesses the attendee’s score against the highest possible score  By ranking, it provided additional insight on how the attendee performed in comparison to other attendees  The percentile score does not add any additional value in assessing the attendee’s performance ExplanationThe percentile score provides value by ranking the attendee’s score among all the scores of the exam takers. A percentile score of 90 means that the attendee scored higher than 90% of the exam takers, and only 10% scored higher than the attendee. This gives a relative measure of how the attendee performed in comparison to other attendees, and how competitive or exceptional the score is. The percentile score does not depend on the average or the highest possible score of the exam, but only on the distribution of the scores of the exam takers.References:*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 4:Interpret and Report Results*Understanding the Guide to Business Data Analytics, page 9*What is a Percentile? – Statistics By JimNO.24 A software company launched a new product in late 2016. The product manager is reviewing a Box and Whisker plot used to compare year-over-year sales, from 2017 to 2018. What is the conclusion he can make from this chart?  2017 minimum and maximum sales are higher than 2018, and the 2017 median result is higher than the 2018 median result  2017 minimum and maximum sales are higher than 2018, but the 2017 median result is lower than 2018 1st quartile result  2018 minimum and maximum sales are higher than 2017, and the 2018 quartile results are higher than 2017 quartile results  2018 minimum and maximum sales are higher than 2017, and the 2018 1st quartile is higher than 2017 median result NO.25 A government agency is conducting a study on the performance of 12th grade students’ in mathematics across the country. In particular, they want to understand if there is a relationship between intelligence and scores, as well as the difference in performance between various locations. Which combination of inferential statistics procedures should be used?  Range, standard deviation  Mean, median  Correlation co-efficient, analysis of variance  Frequency distribution, time-series ExplanationA correlation co-efficient is a measure of the strength and direction of the linear relationship between two variables, such as intelligence and scores. A correlation co-efficient can range from -1 to 1, where -1 indicates a perfect negative relationship, 0 indicates no relationship, and 1 indicates a perfect positive relationship12. An analysis of variance (ANOVA) is a procedure that tests whether the means of two or more groups are significantly different from each other, such as the performance of students across various locations. ANOVA can compare the variation within each group and the variation between groups to determine if there is a statistically significant difference among the group means34. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 582: Statistics for Business and Economics, David R. Anderson et al., 2014, p. 7133: Guide to Business Data Analytics, IIBA, 2020, p. 594: Statistics for Business and Economics, David R. Anderson et al.,2014, p. 849.NO.26 There were 7 students enrolled in the Introduction to Artificial Intelligence course. These were the student’s scores from the final exam: 64, 70, 80, 80, 90, 98, 100 What is the mean and mode for the outlined scores?  83.14, 80  79.84, 81.40  80,80  80, 83.14 ExplanationThe mean is the average of all the scores, which is found by adding them up and dividing by the number of scores. The mode is the most frequent score, which is the one that occurs the most times. To find the mean and mode for the outlined scores, we can use the following steps:*Arrange the scores in ascending order: 64, 70, 80, 80, 90, 98, 100*Add up the scores: 64 + 70 + 80 + 80 + 90 + 98 + 100 = 582*Divide the sum by the number of scores: 582 / 7 = 83.14*The mean is 83.14*Count how many times each score occurs: 64 occurs once, 70 occurs once, 80 occurs twice, 90 occurs once,98 occurs once, 100 occurs once*The score that occurs the most times is 80*The mode is 80Therefore, the mean and mode for the outlined scores are 83.14 and 80, respectively12 References: 1: Mean, median, and mode review (article) | Khan Academy 2: Mean, Median, and Mode: Measures of Central Tendency – Statistics By JimNO.27 A Human Resource manager recently learned that their competitor reduced employee attrition rates by 20% after implementing personality tests as part of their screening process. Intrigued by the idea, the manager suggests collecting data on personality tests and attrition rates over the next year. The data from this year is then analyzed to explore possible relationships. What type of analytics has the team been asked to perform?  Predictive  Descriptive  Prescriptive  Diagnostic ExplanationDescriptive analytics is a type of analytics that summarizes and visualizes the data to provide an overview of what has happened or is happening, such as the attrition rates and the personality test scores of the employees12. The team has been asked to perform descriptive analytics to explore possible relationships between the data variables, without making any predictions or prescriptions for the future. References: 1:Guide to Business Data Analytics, IIBA, 2020, p. 182: Business Analytics: Data Analysis & Decision Making,S. Christian Albright and Wayne L. Winston, 2015, p. 5.NO.28 The analytics team has established two equally strong potential recommendations which will deliver the desired outcomes with similar benefits to be derived from each one. On the surface there is no discernable difference in costs or schedule for either option. To help the analytics team reach a recommendation the business analysis professional recommends the team:  Complete market research  Assess risks for each option  Vote to choose the recommendation  Seek management guidance ExplanationAssessing risks for each option is the recommendation that the business analysis professional should make to the analytics team, because it is a technique that involves identifying, analyzing, and evaluating the potential positive or negative impacts of each option on the project, the organization, or the stakeholders. Assessing risks can help the team compare the pros and cons of each option, and determine which one has the highest expected value or the lowest expected loss. Assessing risks can also help the team prepare contingency plans or mitigation strategies for the chosen option, and communicate the rationale and assumptions behind their recommendation. References:*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 5: Use Results to Influence Business Decision Making*Understanding the Guide to Business Data Analytics, page 9*CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 12NO.29 An analytics team has completed some initial data analysis but is considering revising their research question based on their analysis findings. The team was concerned the original question was too broad. What outcome would lead the team to have this concern?  Data once analyzed had significant data quality issues  Data the team had planned to use was not available  Difficult to identify the KPIs to measure  The source data sets could not be merged ExplanationA research question is a clear and focused question that guides the data analytics process and defines the expected outcome or value of the analysis1. A research question that is too broad may lead to the concern of being difficult to identify the key performance indicators (KPIs) to measure, as KPIs are specific, quantifiable, and relevant metrics that indicate the progress and success of the analysis in relation to the research question23. A broad research question may also result in too much or too little data, unclear or conflicting objectives, or irrelevant or ambiguous results4. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 202: Guide to Business Data Analytics, IIBA, 2020, p. 233: Key Performance Indicators: Developing, Implementing, and Using Winning KPIs, David Parmenter, 2015, p. 34: How to Write a Good Research Question, ThoughtCo, 2021, 1.NO.30 Based on the results of a recently completed analytics initiative, the Human Resource department for a major department store implemented a change to its hiring practice to address the attrition rates of its sales associates. The new policy stated that candidates applying for sales positions must possess at least 3 years of relevant sales experience to be considered. After implementing the change, attrition rates are 10% higher and management is frustrated. Which of the following could result in this outcome?  The results of analysis have been incorrectly interpreted  Sales experience is not a relevant skill  Analytics is not helpful given this situation  The change proposed is not aligned to company strategy ExplanationThe change proposed is not aligned to company strategy, because it may not address the root cause of the attrition problem, or it may conflict with other organizational goals or values. For example, the change may reduce the pool of qualified candidates, increase the hiring costs, or lower the diversity or customer satisfaction of the sales team. The change may also ignore other factors that influence the attrition rates, such as compensation, training, feedback, or recognition. Therefore, the change may not achieve the desired outcome of reducing attrition, and may even worsen it. References:*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 5: Use Results to Influence Business Decision Making*Understanding the Guide to Business Data Analytics, page 9*CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 13NO.31 After analyzing sales data, the analytics team finds that the older the customer, the more expensive the neckties purchased. The team felt this was a breakthrough insight but on closer analysis realized that other factors could account for this relationship. This is a clear indication that:  Correlation between variables implies causation  Causation has no relationship with correlation  Causation between variables does not imply correlation  Correlation between variables does not imply causation ExplanationThe analytics team found a correlation between the age of the customer and the price of the neckties purchased, meaning that as one variable changes, the other tends to change in the same direction. However, this correlation does not imply causation, meaning that one variable does not necessarily cause the other to change. There could be other factors, such as income, preference, or quality, that affect both variables and create a spurious relationship. Therefore, the team realized that they need to investigate further to determine if there is a causal link between the variables, or if the correlation is coincidental12 References: 1: Correlation vs. Causation | Difference, Designs & Examples – Scribbr 2: Correlation vs Causation: Understanding the Differences – Statistics By JimNO.32 An analytics team employed at a leading credit card company is utilizing data analytics to identify unusual credit card purchases.They have created the following visual. How many extreme outliers exists in this dataset?  0  5  3  2 ExplanationAccording to the Business Data Analytics (IIBA®- CBDA) principles, extreme outliers in a dataset can be identified visually on a scatter plot as points that are distinctly separate from the bulk of the data. In this visual, there are three points that are significantly higher on the y-axis (credit card expense) relative to their position on the x-axis (household income), indicating unusual credit card purchases. References: The identification and interpretation of outliers is a standard practice in data analytics and is covered under the Business Data Analytics (IIBA®- CBDA) learning resources.NO.33 A lab is conducting a study on protein interactions. They have used the data to create a graph visualization. In graph visualization, what would a layout be?  A single data point  A link between two data points  A dedicated algorithm that calculates the node positions  A collection of data points and links ExplanationA layout is a way of arranging the nodes and links of a graph visualization to convey meaningful information about the data. A layout is determined by a dedicated algorithm that calculates the node positions based on certain criteria, such as minimizing edge crossings, maximizing node spacing, or emphasizing clusters12. A layout can also be influenced by user interaction, such as zooming, panning, or dragging3. References: 1:Guide to Business Data Analytics, IIBA, 2020, p. 642: Graph Drawing: Algorithms for the Visualization of Graphs, Giuseppe Di Battista et al., 1999, p. 33: Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et al., 2015, p. 227.NO.34 A Data Dictionary is being developed for an employee database. When reviewing the data dictionary, the analyst recommends adding another primitive data element. Which element would be suggested?  Street address  First name  Customer name  Work phone number ExplanationA street address is a primitive data element, because it is a basic unit of data that cannot be further decomposed into smaller components. A primitive data element has a distinct name, definition, format, and value domain. A street address can be used to identify the location of an employee or a customer, and it can be stored as a string or a combination of numbers and characters. Options B, C, and D are not primitive data elements, because they can be further broken down into smaller components. For example, a first name can be divided into a prefix, a given name, and a suffix. A customer name can be composed of a first name and a last name. A work phone number can be split into a country code, an area code, and a local number. References:*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 2: Source Data*Business analysis data dictionary – The Functional BA*CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 15NO.35 A marketing director has asked the question ‘How many product purchases are expected this coming year given the current marketing campaign?”. What type of analytics would be performed to answer this question?  Descriptive  Predictive  Diagnostic  Prescriptive ExplanationPredictive analytics is a type of analytics that uses historical and current data, as well as statistical and machine learning techniques, to forecast future events or outcomes, such as product purchases, customer behavior, or market trends12. To answer the question ‘How many product purchases are expected this coming year given the current marketing campaign?’, predictive analytics would be performed to estimate the demand and sales based on the existing data and the marketing campaign variables. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 182: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Eric Siegel, 2016, p. 3.NO.36 Senior executives in a large organization receive numerous sales reports of every sale through a corporate dashboard on a weekly basis. The executives are considering budget increases for various functions but would like to know if they are obtaining good returns for current budget allocations. They ask the analytics team to research and answer: “How effective is our marketing spend?” This question is:  Already answered in the sales data  Difficult to analyze because its narrowly focused  Sufficient to begin initial analysis  Too broadly scoped to be effectively answered ExplanationThe question “How effective is our marketing spend?” is too broadly scoped to be effectively answered, because it is a vague and ambiguous question that does not specify the criteria, scope, or timeframe for measuring the effectiveness of the marketing spend. The question also does not define what constitutes marketing spend, or how it relates to the sales data or the budget allocations. The question needs to be refined and clarified to make it more focused, relevant, and feasible for the analytics team to answer. For example, the question could be rephrased as “How does the marketing spend per channel affect the sales revenue and customer retention rate in the last quarter?” References:*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 1: Identify the Research Questions*Understanding the Guide to Business Data Analytics, page 10-11*CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 16NO.37 Collaborative games are used by a business analyst to identify the research questions to be explored within an analytics system.Participants are asked to write down a research question on a sticky note, put the notes on the wall, and move them towards related research questions. What type of Collaborative game is being played?  Affinity Map  Fishbowl  People polling  Product Box ExplanationAn affinity map is a collaborative game that helps participants to group similar ideas or features together. It is useful for identifying research questions that are related to each other and finding common themes or patterns.In this game, participants write down their research questions on sticky notes and place them on the wall.Then, they move the notes around to form clusters of related questions. The clusters can be labeled with a descriptive name or a question that summarizes the theme. An affinity map can help participants to prioritize the most important or relevant research questions and generate insights from the data.https://businessanalystmentor.com/collaborative-games-business-analysis/NO.38 Interested in experimenting with analytics, a manufacturing company hires an analyst to see how the capability can be developed within its organization. The analyst is getting started and recognizes the need to show value from the onset of their work to gain upper management’s trust and future funding. What action will accomplish these objectives?  Solve the biggest problem the organization has first to quickly grab the support and attention of senior management  Develop a question that can be answered quickly regardless of alignment to strategy, just to get started  Develop a meaningful question that can be answered with data the company already has in its possession  Perform a market analysis to understand how competitors are using analytics and then launch a similar initiative ExplanationThe best action for the analyst to show value from the onset of their work is to develop a meaningful question that can be answered with data the company already has in its possession. This way, the analyst can demonstrate the potential of analytics to solve relevant business problems, without spending too much time or resources on data collection or market research. The question should also be aligned with the organization’s strategy and goals, and provide actionable insights for decision making12. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 202: Data Science for Business, Foster Provost and Tom Fawcett, 2013, p. 14.NO.39 Based on the financial analysis that’s been completed by the analytics team, the business analysis professional reminds the team that the most financially feasible option is the one with the:  Highest ROI, highest present value, lowest NPV and highest payback period  Highest ROI, highest present value, highest NPV, and lowest payback period  Highest ROI, lowest present value, lowest NPV and highest payback period  Highest ROI, lowest present value, highest NPV and lowest payback period ExplanationThe most financially feasible option is the one that maximizes the return on investment (ROI), the present value (PV), and the net present value (NPV), and minimizes the payback period. ROI measures the annual percentage return of an investment, PV measures the current value of future cash flows, NPV measures the difference between the PV and the initial cost of an investment, and payback period measures the time it takes to recover the initial cost of an investment. A higher ROI, PV, and NPV indicate a more profitable and valuable investment, while a lower payback period indicates a faster recovery and lower risk of an investmentNO.40 Which attributes from the Order entity will need to be normalized to avoid redundancies?. Orderld. OrderDate. Itemld. ItemName. Quantity. ItemPrice  OrderDateItemPrice  ItemNameItemPrice  OrderDateItemName  Item NameQuantity ExplanationThe attributes ItemName and ItemPrice need to be normalized to avoid redundancies because they depend on the attribute ItemId, which is not part of the primary key of the Order entity. This is a case of partial dependency, which violates the second normal form (2NF) of database normalization. To achieve 2NF, the Order entity should be split into two entities: Order and Item, where Item contains the attributes ItemId, ItemName, and ItemPrice, and Order contains the attributes OrderId, OrderDate, ItemId, and Quantity. This way, the ItemName and ItemPrice are stored only once for each ItemId, and the Order entity references them through a foreign key12 References: 1: Balancing Data Integrity and Performance: Normalization vs … 2:Normalization Process in DBMS – GeeksforGeeksNO.41 An analyst has just completed building a data model that shows the table structures including table names, table relationships with primary and foreign keys and column names with respective data types. What type of data model has the analyst just built?  Physical  Hierarchical  Conceptual  Logical ExplanationA physical data model is the most detailed and specific type of data model, which shows how the data is stored, accessed, and manipulated in the database. It includes the table structures, column names, data types, primary and foreign keys, constraints, indexes, and other physical attributes of the data12. References: 1:Guide to Business Data Analytics, IIBA, 2020, p. 542: Data Modeling Essentials, Graeme Simsion and Graham Witt, 2005, p. 15.NO.42 An organization’s customers are categorized based on the amount of purchases completed over the last 12 months. The analytics team would like to ensure the accuracy of their survey results and decide to randomly select 500 customers to participate in a survey from this large pool of customers. This is an example of:  Stratified sampling  Quota sampling  Purposive sampling  Snowball sampling ExplanationStratified sampling is a technique that divides the population into homogeneous subgroups (strata) based on a relevant characteristic, such as the amount of purchases, and then randomly selects a proportional number of elements from each subgroup to form the sample. Stratified sampling ensures that the sample is representative of the population and reduces the sampling error and bias12. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 312: Statistics for Business and Economics, David R. Anderson et al., 2014, p. 262.NO.43 A research marketer is interested in collecting information about the spending habits of families in North America. Concerned about the volume of data required to conduct the research, they choose to use sampling.The dataset is sourced using all credit card transactions from a leading North American credit card company for Quarter 1 of the prior year. The sample used is:  Statistically representative  Not relevant  Too large to be helpful  Biased ExplanationThe sample used in this case is biased, meaning that it is not representative of the population of interest. The population of interest is the families in North America, but the sample is drawn from only one source of data:the credit card transactions from a leading North American credit card company. This sample excludes the families who do not use credit cards, or who use other credit card companies, or who use other payment methods. Therefore, the sample is not random or fair, and it may introduce sampling bias into the research results12 References: 1: Sampling Methods | Types, Techniques & Examples 2: Sampling Bias – an overview | ScienceDirect TopicsNO.44 While creating a dataset for analysis, the analyst reviews the data collected and finds a large percentage of records are missing values. Which activity would the analyst perform in order to use this dataset?  Clustering  Scale validation  Weighting  Factor analysis ExplanationWeighting is a technique that assigns different values or weights to different records or variables in a dataset, based on their importance or relevance. Weighting can be used to handle missing values by giving them a lower weight or imputing them with a weighted average of other values. Weighting can also help to adjust for sampling bias or non-response bias in the data collection process. References:*Understanding the Guide to Business Data Analytics, page 16*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3:Analyze Data*CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 4 Loading … Free Exam Files Downloaded Instantly: https://www.braindumpsit.com/CBDA_real-exam.html --------------------------------------------------- Images: https://blog.braindumpsit.com/wp-content/plugins/watu/loading.gif https://blog.braindumpsit.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2024-03-27 12:28:36 Post date GMT: 2024-03-27 12:28:36 Post modified date: 2024-03-27 12:28:36 Post modified date GMT: 2024-03-27 12:28:36