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NEW QUESTION # 12
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:
- A. Correlation between variables does not imply causation
- B. Causation has no relationship with correlation
- C. Correlation between variables implies causation
- D. Causation between variables does not imply correlation
Answer: A
Explanation:
Explanation
The 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 Jim
NEW QUESTION # 13
An analyst is performing regression analysis and reviewing the results. They would like to rescale the variables in the model to more clearly reflect the relationship between the regression coefficients.Which technique could be used to rescale the variables?
- A. Clustering
- B. Mean Centering
- C. Dimension Reduction
- D. Normalization
Answer: D
Explanation:
Normalization is a technique that rescales the values of the variables in a data set to a common range, such as
[0,1] or [-1,1]. Normalization can help reduce the effect of outliers, improve the performance of some algorithms, and make the interpretation of the regression coefficients easier and more consistent.
Normalization can be done using different methods, such as min-max scaling, z-score scaling, or unit vector scaling.
References:Guide to Business Data Analytics, page 41; Introduction to Business Data Analytics: A Practitioner View, page 12.
NEW QUESTION # 14
A business analyst manager is planning budgets for the new year, and training opportunities for his team of business analysts. The manager sends out a survey to the team to obtain their top interests within the seven areas of training opportunities. The team results were compared against the manager's personal rating. What can be deduced from the following chart with regards to the survey results?
- A. The manager's rating did not match with the team's rating for any of the training areas
- B. The team's top interests in training opportunities were aligned with the manager's, which included Negotiation & Conflict Resolution and Facilitation
- C. The team had equal interest across all training areas
- D. The team's top interests in training opportunities were aligned with the manager's, which included Teamwork and Adaptability
Answer: B
Explanation:
Explanation
The chart shows the personal rating of the manager and the average team rating on different areas of training opportunities. Both the manager and the team rated "Negotiation & Conflict Resolution" and "Facilitation" highly, indicating a shared interest in these areas. These areas are also relevant for business analysts, as they involve skills such as communication, collaboration, problem-solving, and stakeholder management12 References: 1: 6 Charts You Can Use to Create Effective Reports | SurveyMonkey 2: Business Analysis Core Concept Model™ (BACCM™) - IIBA BABOK Guide v3
NEW QUESTION # 15
A business analyst constructs a model they would like to review with key business stakeholders but decides to review the model first with the data scientist who has performed the data analysis. The data scientist provides some suggestions on how to reduce the complexity in the model. One suggestion is to use color to group objects needing to be associated. The data scientist is encouraging using which Gestalt Principle of Perception with regards to data visualization?
- A. Similarity
- B. Enclosure
- C. Connection
- D. Proximity
Answer: A
Explanation:
The data scientist is encouraging using the Gestalt Principle of Similarity with regards to data visualization.
This principle states that the brain groups objects together that are similar in appearance, such as color, shape, size, or orientation. By using color to group objects needing to be associated, the data scientist is suggesting a way to reduce the complexity in the model and make it easier for the viewers to perceive the patterns and relationships among the data12 References: 1: Gestalt Principles For Data Visualization - Topcoder 2:
Introduction to Data Visualization: Gestalt Principles
NEW QUESTION # 16
A new dataset describing employee salaries is received by a company. A colleague wonders whether a variable follows a Gaussian distribution. Which of the following plots would demonstrate this?
- A. Boxplot
- B. Scatterplot
- C. Normal probability plot
- D. Lowess curve
Answer: C
Explanation:
A normal probability plot is a graphical technique that can be used to check if a variable follows a Gaussian distribution. It plots the observed values of the variable against the expected values under the normal distribution. If the variable is normally distributed, the points should form a straight line. A scatterplot, a boxplot, and a lowess curve are not suitable for testing normality, as they do not compare the observed values with the theoretical values of the normal distribution.
https://www.graphpad.com/support/faq/testing-data-for-normal-distrbution/
NEW QUESTION # 17
An analyst is using a Data Flow Diagram (DFD) to depict the flow of data across a data security company.
Which of the following is true about DFDs?
- A. Are used to model data attributes
- B. Provide similar information as process flows
- C. Can be categorized as Logical or Physical
- D. Can illustrate a sequence of activities
Answer: C
Explanation:
A Data Flow Diagram (DFD) is a technique that shows the flow of data among processes, data stores, and external entities in a system. DFDs can be categorized as logical or physical, depending on the level of detail and abstraction. A logical DFD focuses on the business functions and data flows, without specifying the implementation details. A physical DFD shows the actual componentsand mechanisms that are involved in the data flow, such as hardware, software, files, and network connections. References:
*10.13 Data Flow Diagrams | IIBA® - International Institute of Business ..., menu, 10.13 Data Flow Diagrams
*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 2: Source Data
*Introduction to Business Data Analytics: Organizational View, page 16, Figure 6: Data Flow Diagram
NEW QUESTION # 18
An online retailer of men's athletic apparel is seeking to become the market leader in the industry. To deliver on this strategy, the analytics team continuously collects data on the prices of competitor products and uses this information to adjust the retailer's prices. What type of analytics is the retailer using to maintain their pricing structure?
- A. Predictive
- B. Prescriptive
- C. Diagnostic
- D. Descriptive
Answer: B
Explanation:
Explanation
Prescriptive analytics is the type of analytics that the retailer is using to maintain their pricing structure, because it is a technique that uses data and models to recommend the best course of action for a given situation. Prescriptive analytics can help the retailer optimize their prices based on the data collected from the competitors, the market conditions, and the customer preferences, and thus achieve their strategic goal of becoming the market leader. References:
*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3:
Analyze Data
*Understanding the Guide to Business Data Analytics, page 17
*CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 11
NEW QUESTION # 19
A consumer goods manufacturer has recently completed an analytics study to understand how to improve its operational excellence. From the top highlights, online sales outperformed other channels in sales growth and there was a direct relationship between positive customer reviews and increased internet sales. Which strategic business decision may be logically derived from these results?
- A. Encourage customers to complete online reviews
- B. Create an empowered and collaborative work culture
- C. Improve quality of the products
- D. Improve operational efficiencies
Answer: A
Explanation:
Explanation
The strategic business decision that may be logically derived from the results is to encourage customers to complete online reviews, because the results show that there is a direct relationship between positive customer reviews and increased internet sales. By increasing the number and quality of online reviews, the consumer goods manufacturer can boost its online sales performance, which outperformed other channels in sales growth. Online reviews can also help the manufacturer gain customer feedback, improve customer loyalty, and enhance its brand reputation. 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 6
NEW QUESTION # 20
A merger has been completed between two telecommunication companies and the analytic practices from both organizations are being joined. The newly formed analytics department will create a task force of data experts to combine the data from both companies into a structure usable for future analytics initiatives. Which of the following activities would provide a high level understanding about any potential data issues that might be encountered when merging sources?
- A. Data profiling
- B. Data cleansing
- C. Data conversion
- D. Data migration
Answer: A
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.
References: Guide to Business Data Analytics, page 53; CBDA Exam Blueprint, page 7; Data Profiling vs Data Cleansing - Data Ladder
NEW QUESTION # 21
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?
- A. People polling
- B. Fishbowl
- C. Product Box
- D. Affinity Map
Answer: D
Explanation:
Explanation
An 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/
NEW QUESTION # 22
DIAGRAM TAKEN
A data scientist is analyzing a dataset to determine if there is a strong relationship between twovariables. A measure of covariance is done. Which of the following graphs indicate Zero Covariance between variables?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: C
Explanation:
In the context of Business Data Analytics (IIBA®- CBDA), zero covariance between two variables indicates that there is no linear relationship between those variables. When the covariance is zero, it means the variables are independent of each other. In the provided options, graph 4 shows a random scatter of data points without any apparent trend or pattern, indicating zero covariance.
References: The explanation is in alignment with the concepts and principles outlined in IIBA's resources on Business Data Analytics, particularly focusing on statistical analysis and data interpretation.
NEW QUESTION # 23
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. Scale validation
- B. Clustering
- C. Factor analysis
- D. Weighting
Answer: D
Explanation:
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. 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
NEW QUESTION # 24
An analyst is interested in providing a visual diagram to compare and contrast the characteristics of four different solution options. Each option should be represented by their cost, value, and risk level. What type of chart would accomplish this task?
- A. Bubble
- B. Bullet
- C. Pie
- D. Waterfall
Answer: A
Explanation:
A bubble chart is a type of chart that displays three dimensions of data: the x-axis, the y-axis, and the size of the bubble. A bubble chart can be used to compare and contrast the characteristics of different solution options by plotting their cost, value, and risk level on the three axes. For example, a solution option with a high cost, high value, and low risk would be represented by a large bubble on the upper left corner of the chart, while a solution option with a low cost, low value, and high risk would be represented by a small bubble on the lower right corner of the chart. A bubble chart can help the analyst and the stakeholders to visualize the trade-offs and benefits of each solution option and to select the most optimal one based on the business objectives and constraints. References: Guide to Business Data Analytics, page 77; Introduction to Business Data Analytics:
A Practitioner View, page 16; [Business Data Analytics: A Practical Guide], page 121.
NEW QUESTION # 25
The sales department is interested in using business analytics to better understand their customer's purchasing habits. During the process of sourcing data, the analyst discovers geographic differences in how sales data is being recorded. The analyst would like to influence how the organization strategically plans for business analytics. Which practice, would move the organization closer to meeting this objective?
- A. Data governance
- B. Data warehousing
- C. Data management
- D. Data integration
Answer: A
Explanation:
Data governance is the practice of establishing and enforcing policies, standards, roles, and responsibilities for the quality, security, and usage of data across an organization1. Data governance helps ensure that data is consistent, reliable, and trustworthy, and that it aligns with the organization's strategic goals and objectives. Data governance also facilitates collaboration and communication among different stakeholders, such as business analysts, data owners, data stewards, and data consumers2. By implementing data governance, the analyst can influence how the organization strategically plans for business analytics, as data governance can help address the issues of data quality, data integration, data access, data ethics, and data value3.
Data integration, data management, and data warehousing are related but distinct concepts from data governance. Data integration is the process of combining data from different sources into a unified view4. Data management is the process of collecting, storing, organizing, and maintaining data throughout its lifecycle5. Data warehousing is the process of creating and maintaining a centralized repository of data for analytical purposes. While these practices can support business analytics, they do not necessarily influence how the organization strategically plans for business analytics, as they are more focused on the technical aspects of data rather than the organizational aspects of data.
References:1: Guide to Business Data Analytics, IIBA, 2020, p. 392: Introduction to Business Data Analytics:
An Organizational View, IIBA, 2019, p. 143: Data Governance: The Definitive Guide, Tableau, 4: Data Integration: The Definitive Guide, Tableau, 5: Data Management: The Definitive Guide, Tableau, . : Data Warehousing: The Definitive Guide, Tableau, .
NEW QUESTION # 26
An organization has a customer database of 3000 customers and has accumulated 5 years of sales data. They want to make decisions about which products to retire and which to continue to offer. Management has turned to the analytics team to analyze the data and provide recommendations. The analytics team develops a survey to send to randomly selected customers.This is an example of:
- A. Data Sampling
- B. Data Manipulation
- C. Data Wrangling
- D. Data Grouping
Answer: A
Explanation:
Data sampling is the process of selecting a subset of data from a larger population to represent the characteristics of the whole population. Data sampling is often used when the population is too large or costly to collect data from every individual. Data sampling can help reduce the time, cost, and complexity of data analysis, while maintaining the validity and reliability of the results. Data sampling can also help avoid biases and errors that may arise from collecting data from the entire population. Data sampling can be done using various methods, such as random sampling, stratified sampling,cluster sampling, or convenience sampling, depending on the research objectives and the availability of data. In this example, the analytics team develops a survey to send to randomly selected customers, which is a form of data sampling. The survey aims to collect data from a representative sample of customers that can reflect the preferences and opinions of the entire customer population. The survey data can then be used to analyze the performance and demand of different products, and provide recommendations to management. References:
* [Business Data Analytics: A Practitioner's Guide], Chapter 4: Data Analysis, Section 4.2: Data Sampling, pp. 69-72.
* [A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide)], Version 3, Chapter 6:
Solution Evaluation, Section 6.2: Analyze Performance Measures, pp. 152-153.
NEW QUESTION # 27
An analyst is looking at a particular dataset that includes the scores across all 8th grade students, across three schools. The analyst is trying to determine which type of statistics average to use to best represent the results.
On looking through the dataset, the analyst has identified a few extreme outliers. As a result, the analyst was led to use the following type of average:
- A. Median
- B. Range
- C. Mean
- D. Mode
Answer: A
Explanation:
Explanation
The median is the type of statistics average that the analyst should use to best represent the results, because it is a measure of central tendency that divides the data set into two equal halves. The median is the middle value of the data set when it is arranged in ascending or descending order. The median is not affected by extreme outliers, unlike the mean, which is the arithmetic average of the data set. The median can give a more accurate representation of the typical score of the 8th grade students across the three schools. Options B, C, and D are not types of statistics average, but types of statistics measures that describe other aspects of the data set. The range is a measure of dispersion that shows the difference between the highest and the lowest values of the data set. The mean is a measure of central tendency that shows the sum of the values of the data set divided by the number of values. The mode is a measure of central tendency that shows the most frequent value of the data set. References:
*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3:
Analyze Data
*Understanding the Guide to Business Data Analytics, page 17
*Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 3: Analyze Data, Lecture 13:
Descriptive Statistics
NEW QUESTION # 28
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?
- A. Survey sample
- B. Sampling frame
- C. Sample weights
- D. Population of interest
Answer: B
Explanation:
The 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 14
NEW QUESTION # 29
A lab is conducting a study on protein interactions. They have used the data to create a graph visualization.In graph visualization, what would an edge represent?
- A. A dedicated algorithm that calculates the node positions
- B. A single datapoint
- C. A link between two datapoints
- D. A collection of datapoints and links
Answer: C
Explanation:
A graph visualization is a type of visualization that shows the relationships among data points by using nodes (or vertices) to represent the data points and edges (or links) to represent the connections between them1. A graph visualization can help reveal patterns, clusters, outliers, or hierarchies in the data2. In a graph visualization, an edge represents a link between two data points, indicating that they have some kind of association, interaction, similarity, or dependency3. For example, in a study on protein interactions, an edge could represent a physical or functional interaction between two proteins, such as binding, signaling, or regulation4.
A single data point, a collection of data points and links, and a dedicated algorithm that calculates the node positions are not correct definitions of an edge in a graph visualization. A single data point is represented by a node, not an edge, in a graph visualization. A collection of data points and links is the whole graph, not an edge, in a graph visualization. A dedicated algorithm that calculates the node positions is a method of graph layout, not an edge, in a graph visualization. A graph layout is the way the nodes and edges are arranged in a graph visualization, which can affect the readability, aesthetics, and interpretation of the graph.
References:1: Guide to Business Data Analytics, IIBA, 2020, p. 692: Data Visualization: The Definitive Guide, Tableau, 3: Graph Visualization: The Definitive Guide, Tableau, 4: Protein Interaction Networks, Nature, . : Graph Visualization: The Definitive Guide, Tableau, . : Guide to Business Data Analytics, IIBA,
2020, p. 69. : Data Visualization: The Definitive Guide, Tableau, . : Graph Visualization: The Definitive Guide, Tableau, . : Protein Interaction Networks, Nature, . : Graph Visualization: The Definitive Guide, Tableau, .
NEW QUESTION # 30
A call center has requested to review their sales conversion data for the month. The analyst working on this request is trying to identify the chart that will effectively present the data, which includes: the number of leads, the number of calls made, the number of calls completed, the number of customers interested and the number of sales. What chart should the analyst use to show the values across each stage of the pipeline?
- A. Bullet chart
- B. Pie chart
- C. Bar chart
- D. Funnel chart
Answer: D
Explanation:
Explanation
A funnel chart is a type of chart that shows the values of different stages of a process, such as a sales pipeline, where each stage represents a subset of the previous one. A funnel chart is useful for showing the conversion rate, the drop-off rate, and the potential revenue or profit at each stage12. A funnel chart would be an effective way to present the data requested by the call center, as it would show the number of leads, calls, customers, and sales, as well as the percentage of change between each stage. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 662: Data Visualization: A Practical Introduction, Kieran Healy, 2018, p. 233.
NEW QUESTION # 31
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?
- A. The source data sets could not be merged
- B. Data once analyzed had significant data quality issues
- C. Difficult to identify the KPIs to measure
- D. Data the team had planned to use was not available
Answer: C
Explanation:
A 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.
NEW QUESTION # 32
The analytics team has been asked to assess sales data from their company's website with the hopes of providing insights to help increase online sales. It's the first time the team is looking at this specific data and they are concerned about the quality of data that has been captured. They decide to use the following approach as the next step:
- A. Trend Analysis
- B. Classification analysis
- C. Exploratory analysis
- D. Data Analysis
Answer: C
Explanation:
Exploratory analysis is the approach that the analytics team should use as the next step, because it is a technique that allows them to examine the quality, structure, and characteristics of the data, without making any assumptions or hypotheses. Exploratory analysis can help the team identify any issues or anomalies in the data, such as missing values, outliers, or errors, and decide how to handle them. Exploratory analysis can also help the team discover any patterns, trends, or relationships in the data, and generate new research questions or hypotheses for further analysis. References:
*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3:
Analyze Data
*Understanding the Guide to Business Data Analytics, page 16
*CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 8
NEW QUESTION # 33
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