IIBA CBDA IIBA - CBDA Exam Practice Test Instant Access

Question 1

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?

ADimension Reduction

BMean Centering

CNormalization

DClustering
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.

Answer : C

Question 2

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?

AWith its colour scheme, it is easy to compare the variables within a Treemap

BWith its rectangles and straight lines, a Treemap is optimized to include more

CA Treemap is meant to represent a hierarchical result set as opposed to a Sunburst chart

DA Treemap shows all the hierarchical levels of data as opposed to a Sunburst chart
Explanation:
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, respectively1.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, respectively2. 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 area3. 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.

Answer : B

Question 3

A future state data model is created to depict how information will be structured in a proposed solution but the analyst is also interested in modeling how and when data is transformed throughout various processes across the organization. In which model would the analyst find this information?

AProcess flows

BData flow diagram

CData transformation model

DPhysical data model

Answer : B

A data flow diagram (DFD) is a graphical representation of how data flows and transforms through a system or process. A DFD shows the sources and destinations of data, the data inputs and outputs, the data transformations and logic, and the data stores and flows. A DFD can help the analyst model how and when data is transformed throughout various processes across the organization, as well as identify potential data quality issues, bottlenecks, and redundancies. A DFD can also complement a future state data model by showing the relationships and dependencies among the data entities and attributes.Reference:

Certification in Business Data Analytics (IIBA - CBDA), IIBA, accessed on January 20, 2024.

Business Data Analytics Certification - CBDA Competencies | IIBA, IIBA, accessed on January 20, 2024.

Guide to Business Data Analytics, IIBA, 2020, p. 19-20.

Data Flow Diagram - Everything You Need to Know About DFD, Visual Paradigm, accessed on January 20, 2024.

Question 4

A financial institution is interested in leveraging analytics to address a recent surge in credit card fraud. The company has decided to invest in streaming analytics to obtain instant access to real-time data to stop fraudulent behavior before it occurs. Which practice will help the financial institution integrate the data as it is collected?

AData quality

BData management

CData security

DData architecture

Answer : D

Data architecture is the practice of designing and implementing the structures, models, standards, and processes that enable data integration, storage, and consumption. Data architecture is essential for streaming analytics, as it defines how data is collected, processed, and delivered in real time from multiple sources. Data architecture helps the financial institution integrate the data as it is collected by ensuring data compatibility, consistency, and quality across the streaming pipeline. Data architecture also supports data security, scalability, and performance for streaming analytics.Reference:

Certification in Business Data Analytics (IIBA - CBDA), IIBA, accessed on January 20, 2024.

Business Data Analytics Certification - CBDA Competencies | IIBA, IIBA, accessed on January 20, 2024.

Guide to Business Data Analytics, IIBA, 2020, p. 17-18.

What is Streaming Analytics? | Google Cloud, Google Cloud, accessed on January 20, 2024.

What is Data Integration? | IBM, IBM, accessed on January 20, 2024.

Question 5

A 3rd party is marketing an application for financial institutions to use for credit scoring. This application is an example of what type of analytics?

ADescriptive analytics

BPrescriptive analytics

CExploratory

DInferential
Explanation:
Prescriptive analytics is the type of analytics that provides recommendations or suggestions for optimal actions or decisions based on data analysis. Prescriptive analytics uses techniques such as optimization, simulation, and decision analysis to generate and evaluate various scenarios and outcomes. Prescriptive analytics can help financial institutions to use credit scoring to determine the best loan offers, interest rates, and repayment terms for their customers, as well as to manage risk and compliance. Prescriptive analytics is the most advanced and complex type of analytics, as it requires a high level of data quality, integration, and modeling, as well as human judgment and domain expertise.Reference:
Certification in Business Data Analytics (IIBA - CBDA), IIBA, accessed on January 20, 2024.
Business Data Analytics Certification - CBDA Competencies | IIBA, IIBA, accessed on January 20, 2024.
Guide to Business Data Analytics, IIBA, 2020, p. 15-16.

Answer : B

Question 6

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?

AData conversion

BData cleansing

CData migration

DData profiling
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.

Answer : D

Question 7

When reviewing the results of their analysis, the team is determining if the data supports their hypothesis and can be presented to decision makers. They are reviewing measures of variation, sample size and statistical significance. They realize that the p-value of 0.02 is lower than the initial target. This clearly indicates the team can:

AAccept the null hypothesis and accept the alternative

BAccept the null hypothesis and reject the alternative

CReject the null hypothesis in favor of the alternative

DReject the null hypothesis and reject the alternative
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.

Answer : C

IIBA CBDA Certification in Business Data Analytics IIBA - CBDA Exam Practice Test