IIBA CBDA IIBA - CBDA Exam Practice Test Instant Access

Question 1

An analyst is doing a clinical study on the value of analyte among a large population of healthy people. The analyst is going to use a Gaussian Distribution to share the results. Which of the following represents a Gaussian Distribution?

Answer : B

The Gaussian distribution, also known as the normal distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, the Gaussian distribution will appear as a bell curve, which is the case with option A. It is characterized by its bell-shaped curve and is defined by the mean () and the standard deviation (). It is a common assumption for the distribution of independent, randomly generated variables.

Question 2

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 3

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 4

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 5

An operations manager for a new hotel is in need of determining the optimum number of vans to purchase to shuttle guests to/from the airport. It will be necessary to determine the most efficient routes and schedule to follow to ensure guests do not experience excessive delays. Which business analytics technique would lend itself to supporting these types of business decisions?

ALinear programming

BFactor analysis

CRegression

DK-means Clustering
Explanation:
Linear programming is a business analytics technique that can lend itself to supporting these types of business decisions.Linear programming is a mathematical method that optimizes the allocation of limited resources to achieve a desired objective, subject to a set of constraints1.Linear programming can help the operations manager to determine the optimum number of vans to purchase, the most efficient routes and schedule to follow, and the minimum cost or time to shuttle guests to/from the airport, by formulating a linear objective function and a system of linear inequalities that represent the relevant variables, parameters, and restrictions2.
The other options are not correct business analytics techniques for these types of business decisions.Factor analysis is a statistical method that reduces the dimensionality of a large set of correlated variables into a smaller set of uncorrelated factors that explain the underlying structure or patterns of the data3. Factor analysis can help the operations manager to identify the key factors that influence the guest satisfaction or loyalty, but it cannot help to optimize the resource allocation or efficiency. Regression is a statistical method that estimates the relationship between one or more independent variables and a dependent variable. Regression can help the operations manager to predict the demand or revenue of the hotel based on the variables such as season, price, or location, but it cannot help to optimize the resource allocation or efficiency. K-means clustering is a machine learning method that partitions a set of data points into a predefined number of clusters based on the similarity or distance between the data points.K-means clustering can help the operations manager to segment the guests into different groups based on their characteristics or preferences, but it cannot help to optimize the resource allocation or efficiency.

Answer : A

Question 6

A food and beverage company would like to administer a survey to obtain customer insights about a new cookie product recently launched. A data team is asked to build the survey paying careful attention to reduce the degree of sampling error. Which criteria would help the team meet this objective?

ALarge sample size and variation in the target population

BLarge sample size and random selection of the target population

CSmall sample size and specific subset of the target population

DSmall sample size and using customers who agreed to take the survey
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.

Answer : B

Question 7

A data scientist is working with a team of upper level managers to develop a strategy for creating an enterprise analytics program. What critical success factor would help ensure the organization obtains the most value from its data?

AManagement is aware of the value of data science and ensures support for all tactical initiatives

BA sponsor is identified that helps champion the work

CManagement thinks analytically and fosters a culture where data science thrives

DThe data science team supports the functional units and priorities
Explanation:
According to the Introduction to Business Data Analytics: An Organizational View, one of the critical success factors for creating an enterprise analytics program is to have a management team that thinks analytically and fosters a culture where data science thrives. This means that the management team should understand the potential value and impact of data science, promote a data-driven mindset and decision-making process, encourage innovation and experimentation, and support collaboration and learning among the data science team and other stakeholders. A management team that thinks analytically and fosters a culture where data science thrives can help create a strategic vision, align the goals and objectives, allocate the resources and investments, and overcome the challenges and barriers for the enterprise analytics program.

Answer : C

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