IIBA CBDA Online Practice Questions and Exam Preparation

IIBA CBDA Online Questions & Answers

• Question 1:

  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?

  A. Clustering
  B. Scale validation
  C. Weighting
  D. Factor analysis
  C. Weighting

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  Explanation/Reference:

  Weighting 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.

• Question 2:

  An analytics team is interested in reviewing the results of a public opinion poll that is going to be conducted at the end of the month. One of the factors the team is interested in, is ensuring the result set is statistically significant. Why would this factor be important to the team?

  A. To make sure the criteria for the target audience is met
  B. Guarantee that the objectives of the poll are met
  C. Improve the likelihood of receiving a response rate of 100%
  D. Ensure that results are not biased or random
  D. Ensure that results are not biased or random

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  Explanation/Reference:

  Ensuring the result set is statistically significant is important to the team because it means that the difference or relationship observed in the data is unlikely to be due to chance or sampling error. Statistical significance helps the team to assess the validity and reliability of their findings, and to draw meaningful conclusions and recommendations from the data. Statistical significance also helps the team to communicate their results with confidence and credibility to the stakeholders and decision makers

• Question 3:

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

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  Explanation/Reference:

  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 4:

  A marketing department has established an analytics team. The analytics practice is stand-alone and analysts have limited insights into corporate strategy. Which is an expected result for analytics practices operating at the business unit level?

  A. Analytics work will be driven by the organization's business plan
  B. Insights derived from data analysis will be used to guide strategic decisions
  C. The analytics team may conduct analysis that is of minimal value to the organization
  D. The organization will use analytics as a means to obtain a competitive advantage
  C. The analytics team may conduct analysis that is of minimal value to the organization

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  Explanation/Reference:

  According to the IIBA?Guide to Business Data Analytics, analytics practices operating at the business unit level are characterized by a lack of alignment with the organization's strategic objectives, a limited scope of analysis, and a siloed approach to data and insights1. This can result in analytics work that is not relevant, timely, or impactful for the organization as a whole, and that may not address the most critical business problems or opportunities. Therefore, the analytics team may conduct analysis that is of minimal value to the organization, or even detrimental if it leads to suboptimal decisions or actions.

• Question 5:

  A company wants to run a monthly promotion on batteries that cost 15 cents each and sells for 50 cents. At this price, they typically sell 1000 batteries and generate a profit of 35 cents per battery for a total profit of $350. The analytics team was asked to test two price points20% off (i.e. a sale price of 40 cents) and 40% off (i.e., a sale price of 30 cents). The survey data completed by 10000 participants was analyzed and showed that a 20% savings would result in sales of 1200 batteries and the 40% savings would result in 1800 batteries being sold. The team's initial recommendation was to recommend the 40% discount. Now that they are validating their recommendations, they decide to:

  A. Question why management would only want them to test two price points
  B. Change their recommendation realizing they have been victims of linear bias
  C. Redo the survey looking for a larger sample size
  D. Use their original recommendation given that the volume of sales is much higher
  B. Change their recommendation realizing they have been victims of linear bias

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  Explanation/Reference:

  Linear bias is a type of cognitive bias that assumes a linear relationship between two variables, when in fact the relationship may be more complex or nonlinear. In this case, the analytics team assumed that the higher the discount, the higher the sales and profit, without considering other factors that may affect customer behavior, such as price elasticity, perceived quality, or competition. By changing their recommendation, the team can avoid making a suboptimal decision that may result in lower profit or customer satisfaction.

• Question 6:

  The analytics team is struggling with which recommendation to make. Their challenge is that they have five good options and this indecision is stopping them from moving forward. To help the team finalize their recommendation, the BA professional on the team recommends they complete:

  A. Root cause analysis
  B. Business rules analysis
  C. Data flow diagrams
  D. Acceptance and evaluation criteria
  D. Acceptance and evaluation criteria

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  Explanation/Reference:

  Acceptance and evaluation criteria are the techniques that the BA professional on the team should recommend they complete, because they are the standards or measures that are used to evaluate the suitability and value of each option. Acceptance and evaluation criteria can help the team compare the benefits, costs, risks, and impacts of each option, and determine which one best meets the needs and expectations of the stakeholders. Acceptance and evaluation criteria can also help the team communicate the rationale and evidence behind their recommendation, and ensure that the recommendation is aligned with the business goals and objectives.

• Question 7:

  An analyst calculates the average, median, and mode values for a dataset.What type of analytics is the analyst performing?

  A. Predictive
  B. Diagnostic
  C. Prescriptive
  D. Descriptive
  D. Descriptive

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  Explanation/Reference:

  Descriptive analytics is the type of analytics that summarizes and visualizes data to provide an overview of what has happened or is happening. Descriptive analytics uses techniques such as statistics, charts, graphs, and dashboards to display data in an understandable and meaningful way. Descriptive analytics can help analysts explore data, identify patterns, and communicate insights. Calculating theaverage, median, and mode values for a dataset is an example of descriptive analytics, as it provides a measure of central tendency for the data distribution.

• Question 8:

  The Vice President at a commercial goods manufacturing company wants to create annual objectives for the team based on the company's latest strategic goals. The Vice President has reached out to the business analytics team for data analysis that will help build SMART objectives. What type of analytics will help with creating these objectives?

  A. Descriptive
  B. Diagnostic
  C. Descriptive and Diagnostic
  D. Descriptive and Predictive
  D. Descriptive and Predictive

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  Explanation/Reference:

  Descriptive and predictive analytics are types of analytics that can help with creating SMART objectives. SMART stands for Specific, Measurable, Achievable, Relevant, and Time-bound, which are criteria for setting effective and realistic goals1. Descriptive analytics is the type of analytics that summarizes what has happened in the past using data, such as historical trends, patterns, or performance2. Descriptive analytics can help with creating SMART objectives by providing a baseline, benchmark, or context for the current situation and the desired outcomes. Predictive analytics is the type of analytics that forecasts what is likely to happen in the future using data, such as statistical models, machine learning, or artificial intelligence3. Predictive analytics can help with creating SMART objectives by providing a projection, estimation, or scenario for the future situation and the expected results.

  Diagnostic and prescriptive analytics are other types of analytics that are not as helpful with creating SMART objectives. Diagnostic analytics is the type of analytics that explains why something has happened in the past using data, such as root cause analysis, correlation analysis, or hypothesis testing. Diagnostic analytics can help with understanding the causes and effects of past events, but it does not provide guidance or direction for setting future goals. Prescriptive analytics is the type of analytics that recommends what should be done in the future using data, such as optimization, simulation, or decision analysis. Prescriptive analytics can help with suggesting the best actions or alternatives for achieving future goals, but it does not define or measure the goals themselves.

• Question 9:

  A real estate broker is tracking monthly sales between two of its teams. The results have been visualized using a Treemap chart. What is the advantage of using a Treemap chart, over a Sunburst chart to visualize the results?

  A. With its colour scheme, it is easy to compare the variables within a Treemap
  B. With its rectangles and straight lines, a Treemap is optimized to include more
  C. A Treemap is meant to represent a hierarchical result set as opposed to a Sunburst chart
  D. A Treemap shows all the hierarchical levels of data as opposed to a Sunburst chart
  B. With its rectangles and straight lines, a Treemap is optimized to include more

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  Explanation/Reference:

  A Treemap chart is a type of chart that displays hierarchical data as a set of nested rectangles, where the size and color of each rectangle represent a quantitative value and a categorical variable, respectively. A Sunburst chart is a type of chart that displays hierarchical data as a set of concentric circles, where the size and color of each slice represent a quantitative value and a categorical variable, respectively. Both charts are useful for visualizing hierarchical data structures, but they have different advantages and disadvantages. One advantage of using a Treemap chart over a Sunburst chart is that a Treemap chart is optimized to include more data points, as it uses a Cartesian coordinate system that fills the entire rectangular space of the chart area, whereas a Sunburst chart uses a polar coordinate system that leaves empty spaces in the corners of the chart area. This means that a Treemap chart can display more levels of hierarchy, more categories, and more details than a Sunburst chart, without compromising readability or clarity. Therefore, the correct answer is B, as a Treemap chart is optimized to include more data than a Sunburst chart.

• Question 10:

  While sourcing data, an analyst runs into a situation where different business units are usingdifferent names to refer to the same data element. This lack of standardization is resulting in confusion and additional time required to properly prepare data for analysis. Which practice, if implemented would address this situation and mature the organization's business analytics practice?

  A. Data quality management
  B. Database operations management
  C. Data warehousing
  D. Meta data management
  D. Meta data management

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  Explanation/Reference:

  Meta data management is the practice that, if implemented, would address the situation and mature the organization's business analytics practice, because it is a technique that involves defining, documenting, and maintaining the information about the data elements, such as their names, definitions, formats, sources, and relationships. Meta data management can help the analyst resolve the inconsistencies and ambiguities in the data element names, and ensure that the data is standardized, consistent, and understandable across different business units. Meta data management can also help the analyst improve the data quality, accessibility, and usability for the analysis.