IIBA CBDA Practice Test - Questions Answers, Page 8

Explanation: A bar chart is a graphical representation of data that uses rectangular bars of different heights or lengths to show the values of one or more variables1. A bar chart is suitable for comparing the number of online orders received requesting different types of delivery options for the last 3 years, as it can show the frequency or proportion of each category across time.A bar chart can also help identify trends, patterns, or outliers in the data2. A pie chart is a circular chart that shows the relative sizes of data points in a whole by using different-sized and colored slices3. A pie chart is not suitable for comparing the number of online orders received requesting different types of delivery options for the last 3 years, as it can only show the distribution of one variable at a time, and it does not show the changes over time.A pie chart can also be misleading or confusing if there are too many categories or if the slices are too similar in size4. A funnel chart is a type of chart that shows the stages of a process and the amount of data that passes through each stage5. A funnel chart is not suitable for comparing the number of online orders received requesting different types of delivery options for the last 3 years, as it does not show the categories of delivery options, but rather the progression of customers through a sales or marketing funnel.A funnel chart can help visualize the conversion rates, drop-off rates, or bottlenecks in a process6. A scatter plot is a type of chart that shows the relationship between two numerical variables by using dots to represent the values of each pair of data points. A scatter plot is not suitable for comparing the number of online orders received requesting different types of delivery options for the last 3 years, as it does not show the categories of delivery options, but rather the correlation or association between two continuous variables.A scatter plot can help identify the direction, strength, and shape of the relationship, as well as any outliers or clusters in the data.

Explanation: According to the Guide to Business Data Analytics, PERT (Program Evaluation and Review Technique) is a type of estimation that uses three values: optimistic, pessimistic, and most likely. The PERT estimate is calculated as the weighted average of these three values, with more weight given to the most likely value. PERT can be used to estimate the duration, cost, or other variables of a project or activity, taking into account the uncertainty and variability of the dat a. PERT can help provide a realistic and reliable estimate based on the available information.

Probabilistic linkage is a technique that uses statistical methods to match records from different data sources based on the similarity of key variables, such as name, address, date of birth, etc1.Probabilistic linkage can handle variations, errors, or missing values in the data, and assign a score or probability to each potential match2. Probabilistic linkage would be useful in this situation, as the address column may have different formats, spellings, or abbreviations in the internal and external data sources, and a deterministic linkage (which requires exact matches) might miss some valid matches or create false matches.

Deterministic linkage is a technique that uses predefined rules or criteria to match records from different data sources based on the exact agreement of key variables, such as identifiers, codes, or hashes3. Deterministic linkage would not be useful in this situation, as the address column may not have consistent or unique values in the internal and external data sources, and a probabilistic linkage (which allows for some variation or uncertainty) might find more accurate matches or avoid false matches.

Genetic linkage is a term used in genetics to describe the tendency of genes or DNA sequences that are located close together on a chromosome to be inherited together4. Genetic linkage is not relevant to this situation, as it has nothing to do with matching records from different data sources based on the address column.

Cuff linkage is a term used in sewing to describe the process of attaching a cuff to a sleeve by stitching or fastening.Cuff linkage is not relevant to this situation, as it has nothing to do with matching records from different data sources based on the address column.

Explanation: The mean of a set of numbers is the sum of the numbers divided by the number of numbers. The median of a set of numbers is the middle value when the numbers are arranged in ascending or descending order. To find the mean and median of the given scores, we can use the following steps: To find the mean, we add up all the scores and divide by 7, the number of students. The mean is (64 + 70 + 80 + 80 + 90 + 98 + 100) / 7 = 582 / 7 = 83.14 To find the median, we arrange the scores in ascending order: 64, 70, 80, 80, 90, 98, 100. Since there are an odd number of scores, the median is the middle score, which is 80. Therefore, the mean and median for the outlined scores are 83.14 and 80, respectively.

Explanation: 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 dat a.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.

According to the Guide to Business Data Analytics, a cost-benefit analysis is a technique that compares the costs and benefits of a project or decision over a period of time. The net benefit is the difference between the total benefits and the total costs. A positive net benefit indicates that the benefits outweigh the costs. However, a positive net benefit in one year does not necessarily mean that the project or decision is financially viable. The business analysis professional should also consider the cumulative net benefit, which is the sum of the net benefits over the entire time horizon. The cumulative net benefit reflects the overall value of the project or decision, taking into account the time value of money and the opportunity cost of capital. A project or decision is only financially feasible if the cumulative net benefit is positive at the end of the time horizon. Therefore, the business analysis professional should disagree with the team and suggest that they review the cumulative net benefit before endorsing the recommendation.

Explanation: Unstructured data is data that does not have a predefined format, structure, or schema, and that cannot be easily stored, processed, or analyzed by traditional databases or tools1.Unstructured data may include text, images, audio, video, or other types of data that are rich in information but complex and diverse in nature2. In the Customerissues entity, the ComplaintNotes attribute would be categorized as unstructured data, as it may contain free-form text that captures the details, sentiments, or emotions of the customers' complaints, and that may vary in length, language, tone, or style.The ComplaintNotes attribute would require special techniques, such as natural language processing, text mining, or sentiment analysis, to extract meaningful insights from the unstructured data3. The other attributes in the Customerissues entity would be categorized as structured data, as they have a predefined format, structure, or schema, and that can be easily stored, processed, or analyzed by traditional databases or tools4.Structured data may include numbers, dates, codes, categories, or other types of data that are simple and consistent in nature5.In the Customerissues entity, the CustomerlD, ConcernCategory, ConcernSubCategory, AgentID, and lssueResolved(Y/N) attributes would be categorized as structured data, as they may contain numeric, alphanumeric, or binary values that represent the identifiers, classifications, or statuses of the customers' issues, and that may have fixed lengths, ranges, or domains.

A narrative is a written or spoken explanation of the data analysis results that tells a story with the data, provides additional context and background information, highlights the key insights and findings, and draws correlations and implications for the decision makers12. The report is missing the narrative, meaning that it does not communicate the meaning and value of the data analysis effectively, and it leaves the interpretation and action to the senior management without any guidance or recommendation34.

Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 672: Storytelling with Data, Cole Nussbaumer Knaflic, 2015, p. 93: Data Storytelling: The Essential Data Science Skill Everyone Needs, Brent Dykes, 2016, 14: The Power of Data Storytelling, Harvard Business Review, 2018, 2.

The analytics team overlooked the fact that communicating information is not a one-way or one-time process, but rather a bi-directional and iterative one. This means that the team should not only present their recommendations, but also solicit feedback, address concerns, clarify doubts, and confirm understanding from the stakeholders. By doing so, the team can ensure that the stakeholders are fully engaged, informed, and aligned with the recommendations, and that any potential barriers or risks are identified and mitigated before moving forward.

Reference:

* Understanding the Guide to Business Data Analytics, page 9

* Business Analysis Certification in Data Analytics, CBDA | IIBA, CBDA Competencies, Domain 4: Interpret and Report Results

* CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA, page 5, Step 3 -- Schedule and Take The Exam