IIBA CBDA Practice Test - Questions Answers, Page 10

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.

Reference:

* 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

* Acceptance and Evaluation Criteria | Business Analysis

The ability to rerun the data analysis and the results are the same is the condition that the team should have before presenting the data, because it is a technique that ensures the validity, reliability, and reproducibility of the data analysis. By rerunning the data analysis, the team can verify that the results are consistent and not affected by random errors, biases, or anomalies. The team can also confirm that the data analysis process is well-documented, transparent, and traceable, and that the results can be replicated by other analysts or stakeholders. This can minimize the risk of acting on the data, and increase the confidence and trust in the data analysis.

Reference:

* 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

* Business Data Analytics (IIBA-CBDA Exam preparation) | Udemy, Section 4: Interpret and Report Results, Lecture 20: Data Validation and Verification

The question ''Given that you're a customer, would you work at our company?'' is unclear, because it is a hypothetical and subjective question that does not specify the purpose, scope, or context of the analysis. The question also does not define what constitutes a customer, or how the customer's experience or satisfaction relates to the employee's motivation or performance. 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 customer satisfaction score affect the employee retention rate in our company?''

Reference:

* 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 16

A box plot is the technique that would allow for the display of the statistical five number summary, because it is a technique that shows the distribution of a data set using a rectangular box and whiskers. A box plot can help the data scientist visualize the minimum, maximum, median, first quartile, and third quartile of the data set, as well as any outliers or skewness. A box plot can also help the data scientist compare the variation and symmetry of different groups or categories of data. Options A, B, and C are not suitable for displaying the statistical five number summary, because they are techniques that show the frequency, relationship, or density of the data, but not the quartiles or outliers.

Reference:

* Business Analysis Certification in Data Analytics, CBDA | IIBA, CBDA Competencies, Domain 3: Analyze Data

* Understanding the Guide to Business Data Analytics, page 18

* 16 Best Types of Charts and Graphs for Data Visualization [+ Guide]

The business model canvas is the model that the task force has leveraged to facilitate the discussion, because it is a technique that describes the logic of how an organization creates, delivers, and captures value. The business model canvas consists of nine building blocks that cover the key aspects of a business: customer segments, value proposition, channels, customer relationships, revenue streams, key resources, key activities, key partnerships, and cost structure. The business model canvas can help the task force develop a shared understanding of the current state of the online retailer, and identify the opportunities and challenges for developing their analytics capabilities.

Reference:

* Business Analysis Certification in Data Analytics, CBDA | IIBA, CBDA Competencies, Domain 6: Guide Organization-level Strategy for Business Analytics

* Understanding the Guide to Business Data Analytics, page 9

* 10.8 Business Model Canvas | IIBA

Explanation: Sampling error is the difference between the results obtained from a sample and the results obtained from the population from which the sample is drawn1.Sampling error can affect the validity, reliability, and generalizability of the survey results2. To reduce the degree of sampling error, the data team should use a large sample size and a random selection of the target population.A large sample size means that the sample is more likely to represent the diversity and variability of the population, and that the results are more precise and accurate3.A random selection of the target population means that every member of the population has an equal chance of being included in the sample, and that the results are less biased and more representative4. The other criteria would not help the team meet this objective, as they would increase the degree of sampling error. A large sample size and variation in the target population would not reduce the sampling error, as variation refers to the differences or heterogeneity within the population, not the sample.Variation in the target population can increase the sampling error, as it makes it harder to capture the true characteristics of the population with a sample5. A small sample size and specific subset of the target population would not reduce the sampling error, as they would make the sample less representative and more prone to bias. A small sample size means that the sample is less likely to reflect the diversity and variability of the population, and that the results are less precise and accurate. A specific subset of the target population means that the sample is not randomly selected, but based on some criteria or convenience, and that the results are more biased and less representative. A small sample size and using customers who agreed to take the survey would not reduce the sampling error, as they would also make the sample less representative and more prone to bias. A small sample size has the same drawbacks as mentioned above.Using customers who agreed to take the survey means that the sample is not randomly selected, but based on self-selection or voluntary response, and that the results are more biased and less representative.

Explanation: According to the Guide to Business Data Analytics, a p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true. A p-value is used to make conclusions in hypothesis testing by comparing it to a significance level, which is the maximum probability of making a type I error (rejecting the null hypothesis when it is true). If the p-value is less than or equal to the significance level, then there is strong evidence against the null hypothesis and it is rejected in favor of the alternative hypothesis. If the p-value is greater than the significance level, then there is weak evidence against the null hypothesis and it is not rejected. In this situation, the team realizes that the p-value of 0.02 is lower than the initial target, which means that the probability of observing such a result under the null hypothesis is very low. This clearly indicates that the team can reject the null hypothesis in favor of the alternative hypothesis, as there is sufficient evidence to support their hypothesis.

Explanation: According to the Guide to Business Data Analytics, data profiling is a technique that analyzes the structure, content, and quality of data sources. Data profiling can help identify data issues such as missing values, outliers, inconsistencies, duplicates, and errors. Data profiling can also provide information about the data types, formats, ranges, distributions, and relationships of data elements. Data profiling can help prepare data for data conversion, data cleansing, and data migration by providing a high level understanding of the current state of data and the potential challenges and risks involved in transforming and integrating data from different sources.

Explanation: A data strategy is a long-term plan that defines how the enterprise will identify, store, manage, share, and use its data to achieve its business goals and objectives1.A data strategy aligns the data vision, mission, principles, and policies with the business strategy, and guides the data governance, data quality, data architecture, data security, data integration, data analytics, and data culture of the organization2.A data strategy helps the organization to leverage its data as a strategic asset, to create value, to improve performance, and to gain competitive advantage3. A data roadmap is a document that outlines the specific actions, milestones, deliverables, and timelines for implementing the data strategy. A data roadmap is a tactical tool that helps the organization to prioritize, coordinate, and communicate its data initiatives, and to track its progress and outcomes. A data roadmap is not a long-term plan, but a dynamic and flexible plan that can be updated and revised as the data strategy evolves. A business strategy is a high-level plan that defines how the enterprise will achieve its vision, mission, and goals in a competitive market. A business strategy sets the direction, scope, and value proposition of the organization, and guides its decisions on resource allocation, product development, customer segmentation, pricing, marketing, and differentiation. A business strategy is not a plan that defines how the enterprise will identify, store, manage, share, and use its data, but a plan that defines how the enterprise will create and sustain value for its stakeholders. A data management plan is a document that describes the data that will be collected, generated, or used in a specific project, and how the data will be handled, stored, preserved, shared, and reused during and after the project. A data management plan is a operational tool that helps the project team to comply with the data policies, standards, and best practices of the organization, and to ensure the quality, integrity, security, and accessibility of the dat a.A data management plan is not a long-term plan, but a project-specific plan that can be modified and updated as the project progresses.