IIBA IIBA Certifications CBDA Questions & Answers

• Question 51:

  An analytics team is sourcing data for a new analytics initiative and is deciding between two comparable data sources. One source being considered is a very large dataset and another consists of three smaller sources. What advantage will the larger dataset provide over the three smaller sources?

  A. More significant results

  B. Higher validity

  C. More reproducibility

  D. Higher reliability

  Correct Answer: A

  A larger dataset may provide more significant results than three smaller sources, as it may have more statistical power to detect differences or relationships among variables1. Statistical power is the probability of finding a statistically significant result when there is a true effect in the population2. A larger dataset may have more power because it may have more variability, less sampling error, and higher precision than smaller datasets3. More significant results may lead to more confident and valid conclusions and recommendations for the analytics initiative.

  Higher validity, more reproducibility, and higher reliability are not necessarily advantages of a larger dataset over three smaller sources, as they depend on other factors besides the size of the data. Validity is the degree to which the data and the analysis measure what they are intended to measure4. Reproducibility is the degree to which the data and the analysis can be replicated by another analyst using the same methods and data sources. Reliability is the degree to which the data and the analysis produce consistent results under the same conditions. These qualities may be affected by the quality, accuracy, completeness, and relevance of the data, as well as the appropriateness, transparency, and rigor of the analysis methods. A larger dataset may not be valid, reproducible, or reliable if it has errors, biases, missing values, or irrelevant variables, or if the analysis methods are not suitable, documented, or verified.

• Question 52:

  An analyst at an organization has just learnt about bullet charts. For the latest dashboard, the analyst has decided to display the customer satisfaction rate from the latest 2018 customer survey results through a bullet chart while comparing it to the 2017 customer satisfaction rate.What can be gleaned from this chart?

  

  Customer Satisfaction

  120%

  100%

  80%

  A. The 2018 customer satisfaction rate is at 90%. between good and excellent, and exceeded its target of 70%

  B. The 2018 customer satisfaction rate is at 90%. between good and excellent

  C. The 2018 customer satisfaction rate was fair, at 70%, and did not reach its target of 90%

  D. The 2018 customer satisfaction rate is at 90%. between good and excellent, while the 2017 customer satisfaction rate was at 70%

  Correct Answer: D

  A bullet chart is a type of bar chart that shows progress towards a goal or performance against a reference line1. It consists of a bar representing the featured measure, a reference line denoting a target or threshold, and a background with qualitative ranges (such as poor, fair, good, excellent)2. In this case, the featured measure is the customer satisfaction rate for 2018, the reference line is the target of 70%, and the background ranges are 0-50% (poor), 50-70% (fair), 70-90% (good), and 90-120% (excellent). The chart also shows a thin black bar representing the customer satisfaction rate for 2017, which can be used for comparison. From the chart, we can see that the 2018 customer satisfaction rate is at 90%, which falls in the excellent range and exceeds the target of 70%. We can also see that the 2017 customer satisfaction rate was at 70%, which falls in the good range and meets the target. Therefore, the correct answer is D, as it summarizes both the 2018 and 2017 customer satisfaction rates and their relation to the target and the ranges.

• Question 53:

  An analyst is interested in determining whether their company is charging the right prices for their products. Before creating a research question to frame their data analysis, they review a research study provided by the sales department and review several competitor websites.Which statement is true about document analysis?

  A. Documents that add the most value during document analysis are marketing studies

  B. Data mining is a form of document analysis

  C. Document analysis should be limited to proprietary sources

  D. Document analysis only involves reviewing physical documents

  Correct Answer: B

  Document analysis is a qualitative research technique that evaluates electronic and physical documents to interpret them and gain an understanding of their meaning1. It can be used to study various types of documents, such as informal, external, or contextual documents, and to explore their meanings, patterns, and themes. Data mining is a form of document analysis that involves applying statistical and computational methods to large datasets to discover hidden patterns, trends, or relationships2. Data mining can help analysts answer complex questions, generate hypotheses, or support decision making. Therefore, the correct answer is B, as data mining is a form of document analysis.

• Question 54:

  The analytics team discovers there is an abundance of data available to them from various sources. They are excited about the potential of turning this data into usable information for their organization.They decide to focus the analytics work on:

  A. Using the data that is easiest to collect in order to turn out reports quickly

  B. Harnessing all the data and presenting various results to senior management

  C. Harnessing all the data as long as the analysis meets key cost criteria

  D. Using the data to answer a limited number of key questions

  Correct Answer: D

  According to the IIBA?Guide to Business Data Analytics, analytics work should be driven by well-defined business problems or opportunities that are aligned with the organization's strategic objectives1. Having an abundance of data does not necessarily mean that all of it is relevant, reliable, or useful for the analytics purpose. Therefore, the analytics team should focus on using the data to answer a limited number of key questions that are derived from the business context and that can generate actionable insights and outcomes. This approach can help the analytics team prioritize the most important data sources, methods, and tools, as well as avoid wasting time and resources on analysis that is not impactful or meaningful for the organization.

• Question 55:

  Results of the data analysis have been analyzed and the team was confident with the results but also quite surprised the outcome was not what was expected. In pondering the value of what can be gleaned from the data, the team has no feasible solution to put forth to address the business need.A logical next step would be to:

  A. Repeat the business analytics cycle with the formation of a new research question

  B. Provide the results to a 2nd analytics team to see if similar conclusions are drawn

  C. Analyze the data again, to determine if any insights were overlooked

  D. Check the quality of the data that was used for the analysis

  Correct Answer: A

  According to the Guide to Business Data Analytics, the business analytics cycle is an iterative process that consists of four phases: identify the research questions, source data, analyze data, and interpretand report results. The cycle can be repeated as many times as needed until the business problem or opportunity is addressed or resolved. In this situation, the team was confident with the results but also surprised that the outcome was not what was expected. This means that the initial research question may not have been relevant, specific, or testable enough to provide a feasible solution for the business need. Therefore, a logical next step would be to repeat the business analytics cycle with the formation of a new research question that is more aligned with the business goal, scope, and context.

• Question 56:

  An analyst supporting the Marketing department for a specialty retailer has been asked to look through past sales data to help guide product decisions. The business sponsor for this initiative would first like to know 'What is the most profitable product line?'. What type of analytics is the analyst going to perform to address this question?

  A. Predictive

  B. Diagnostic

  C. Descriptive D. Prescriptive

  Correct Answer: C

  According to the Guide to Business Data Analytics, descriptive analytics is a type of analytics that summarizes and presents data in a meaningful way. Descriptive analytics uses techniques such as statistics, charts, tables, and dashboards to provide an overview of what has happened or is happening in the data. Descriptive analytics can help answer questions such as who, what, when, where, and how. In this situation, the analyst has been asked to look through past sales data to help guide product decisions. The business sponsor for this initiative would first like to know `What is the most profitable product line?'. This is a descriptive analytics question, as it involves summarizing and presenting the past sales data by product line and calculating the profit margin for each product line.

• Question 57:

  The research study is complete, the data has been analyzed and the team has created the necessary high impact visuals.The business analysis professional urges the team to:

  A. Present the results to stakeholders

  B. Validate regression analysis

  C. Curate the data

  D. Develop the narrative

  Correct Answer: D

  Developing the narrative is the process of creating a clear, concise, and compelling story that communicates the key insights, findings, and recommendations from the data analysis to the stakeholders1. Developing the narrative is an important step after completing the research study, the data analysis, and the high impact visuals, as it helps to bridge the gap between the data and the decision-making, to engage and persuade the audience, and to drive action and change2. Developing the narrative involves defining the purpose, audience, and message of the story, choosing the best format and medium to deliver the story, and using effective storytelling techniques, such as structure, context, emotion, and call to action3.

  Presenting the results to stakeholders is the process of delivering the data story to the intended audience, using the appropriate communication channels, methods, and tools4. Presenting the results to stakeholders is a subsequent step after developing the narrative, as it requires a well-crafted and well-prepared data story to be effective and impactful. Presenting the results to stakeholders involves planning and rehearsing the presentation, adapting to the feedback and questions, and evaluating the outcomes and impacts of the presentation5.

  Validating regression analysis is the process of checking the assumptions, accuracy, and suitability of a statistical model that estimates the relationship between one or more independent variables and a dependent variable. Validating regression analysis is a part of the data analysis step, not a step after completing the data analysis. Validating regression analysis involves testing the significance, fit, and residuals of the model, and comparing the model with alternative models or methods.

  Curating the data is the process of organizing, annotating, and preserving the data for future use, reuse, or sharing. Curating the data is a part of the data management step, not a step after completing the data analysis. Curating the data involves applying the data policies, standards, and best practices of the organization, and ensuring the quality, integrity, security, and accessibility of the data.

• Question 58:

  A large retail chain has asked their analytics team to complete a study on their customers' purchasing patterns. The analyst assigned to the study has decided to draw further insight by grouping customers based on their purchasing habits.This clustering approach is an example of:

  A. Untrained learning

  B. Trained learning

  C. Unsupervised learning

  D. Supervised learning

  Correct Answer: C

  Unsupervised learning is a category of data analysis techniques that does not require labeled data or predefined outcomes. Unsupervised learning aims to discover patterns, structures, or relationships in the data without any guidance or supervision. Clustering is a common example of unsupervised learning, where the data is grouped into clusters based on some similarity or distance measure. Clustering can help reveal customer segments, market trends, or product preferences, among other insights.

• Question 59:

  An HR manager attended a conference where the topic of HR analytics was presented. The manager returned to the office feeling strongly that analytics could be used to guide hiring decisions in the future. Which of the following results would assist the HR team in making such decisions?

  A. Employee skill gaps

  B. Employee engagement scores

  C. Workforce performance

  D. Absentee rates

  Correct Answer: A

  According to the Introduction to Business Data Analytics: A Practitioner View, employee skill gaps are the differences between the skills that employees have and the skills that they need to perform their jobs effectively. Employee skill gaps can affect the productivity, quality, and innovation of an organization. HR analytics can help identify and measure employee skill gaps and provide insights on how to close them. HR analytics can also help guide hiring decisions by finding the best candidates who have the required skills or the potential to acquire them. By using HR analytics to address employee skill gaps, the HR team can improve the alignment of human capital with organizational goals and strategies.

• Question 60:

  A large number of text messages are received by Twitter each year making Twitter one example of Big Data. What data characteristic represents this large number of text messages?

  A. Veracity

  B. Velocity

  C. Value

  D. Variety

  Correct Answer: B

  Velocity is one of the four V's of Big Data, along with Volume, Variety, and Veracity. Velocity refers to the speed at which data is generated, collected, and processed. A large number of text messages received by Twitter each year is an example of high-velocity data, as it requires real-time or near-real-time processing and analysis to extract insights and value from it. High-velocity data poses challenges and opportunities for business data analytics, as it requires efficient and scalable data infrastructure, streaming analytics, and timely decision-making.