IIBA CBDA IIBA - CBDA Exam Practice Test Instant Access

Question 1

A data scientist is analyzing a dataset to determine if there is a strong relationship between two variables. A measure of covariance is done. Which of the following graphs indicate Zero Covariance between variables?

Answer : C

Covariance measures the directional relationship between the returns on two assets. A positive covariance means that asset returns move together while a negative covariance means they move inversely. Zero covariance indicates that the returns on the two assets move independently of each other. In the context of a scatter plot, zero covariance is represented by a plot where the points do not show any upward or downward trend but are rather scattered randomly on the graph with no discernible pattern.

Graph 4 displays such a pattern where there is no apparent relationship between the variables on the x and y axes, indicating that there is zero covariance between them.

Question 2

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 3

An organization has a customer database of 3000 customers and has accumulated 5 years of sales dat

a. 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:

AData Wrangling

BData Manipulation

CData Grouping

DData Sampling

Answer : D

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.Reference:

[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.

Question 4

A data dictionary is being developed for a dataset describing a company's customer base. Within the data dictionary, which of the following represents a composite data element?

AStreet address

BFirst name

CTotal sale

DBirthdate

Answer : A

A composite data element is a data element that is made up of smaller units called sub-elements, which are separated by a sub-element separator character, such as a colon (:). For example, ITEMNO is a composite data element that consists of three sub-elements: part number, aisle number, and bin number. A street address is also a composite data element that can consist of sub-elements such as street number, street name, city, state, and zip code. First name, total sale, and birthdate are simple data elements that do not have sub-elements.

Question 5

An analyst supporting the Marketing department for a specialty retailer has been asked to look through past sales data to help guide product decisions. The business sponsor for this initiative would first like to know 'What is the most profitable product line?'. What type of analytics is the analyst going to perform to address this question?

APredictive

BDiagnostic

CDescriptive

DPrescriptive
Explanation:
According to the Guide to Business Data Analytics, descriptive analytics is a type of analytics that summarizes and presents data in a meaningful way. Descriptive analytics uses techniques such as statistics, charts, tables, and dashboards to provide an overview of what has happened or is happening in the dat
a. Descriptive analytics can help answer questions such as who, what, when, where, and how. In this situation, the analyst has been asked to look through past sales data to help guide product decisions. The business sponsor for this initiative would first like to know 'What is the most profitable product line?'. This is a descriptive analytics question, as it involves summarizing and presenting the past sales data by product line and calculating the profit margin for each product line.

Answer : C

Question 6

An analytics team completed their research to determine why customers are abandoning items in their online shopping cart. The team suggests improvements to the website to address the problem. The Director of Sales proclaims that the current website is fine and indicates that the problem materialized when the company increased its shipping rates. The solution proposed by the team seems misaligned. What has gone wrong?

AThis scenario cannot be addressed with analytics

BThe team has not agreed on the root cause of the problem

CThe team did not agree on the business problem

DAn insufficient amount of planning was performed

Answer : C

Agreeing on the business problem is the first and most critical step in any analytics project, as it defines the scope, purpose, and objectives of the analysis, and aligns the expectations and interests of the stakeholders1.Agreeing on the business problem involves identifying the problem statement, the problem owner, the problem context, the problem impact, and the problem criteria2. If the team did not agree on the business problem, the solution proposed by the team may seem misaligned with the actual needs, preferences, or assumptions of the decision makers, and may not address the root cause or the main drivers of the problem. In this scenario, the team and the Director of Sales may have different views on what the business problem is, why it is important, and how it should be solved.

The other options are not correct explanations of what has gone wrong. This scenario can be addressed with analytics, as it involves using data to understand customer behavior, identify factors influencing cart abandonment, and recommend improvements to the website or the pricing strategy. The team may or may not have agreed on the root cause of the problem, but that is not the main issue, as the root cause analysis is a part of the data analysis step, not the problem definition step.The team may or may not have performed an insufficient amount of planning, but that is not the main issue, as the planning process is a subsequent step after the problem definition step, and it depends on the clarity and agreement of the business problem.

Question 7

As the organization looks to advance its analytics practices, the topic of provisioning access to executive dashboards and visualizations is under discussion. Establishing standards and implementing role based logins to executive dashboards will address:

AData management

BData security

CData governance

DContent management
Explanation:
According to the Guide to Business Data Analytics, data security is the protection of data from unauthorized access, use, modification, or destruction. Data security includes the policies, procedures, and technologies that ensure the confidentiality, integrity, and availability of dat
a. Data security is an important aspect of data management, which is the planning, execution, and oversight of the data lifecycle. Data security is also related to data governance, which is the establishment and enforcement of rules, roles, and responsibilities for data quality, access, and usage. Data security is not the same as content management, which is the creation, storage, distribution, and maintenance of digital content.
One of the ways to enhance data security is to provision access to executive dashboards and visualizations based on the roles and permissions of the users. This can help prevent unauthorized or inappropriate access to sensitive or confidential data, as well as ensure compliance with data privacy and ethical standards. By establishing standards and implementing role based logins to executive dashboards, the organization can address the data security needs of its analytics practices.

Answer : B

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