CBDA Practice Questions

Probabilistic linkage is a technique that uses statistical methods to match records from different data

sources based on the similarity of key variables, such as name, address, date of birth,

etc1. Probabilistic linkage can handle variations, errors, or missing values in the data, and assign a

score or probability to each potential match2. Probabilistic linkage would be useful in this situation,

as the address column may have different formats, spellings, or abbreviations in the internal and

external data sources, and a deterministic linkage (which requires exact matches) might miss some

valid matches or create false matches.

Deterministic linkage is a technique that uses predefined rules or criteria to match records from

different data sources based on the exact agreement of key variables, such as identifiers, codes, or

hashes3. Deterministic linkage would not be useful in this situation, as the address column may not

have consistent or unique values in the internal and external data sources, and a probabilistic linkage

(which allows for some variation or uncertainty) might find more accurate matches or avoid false

matches.

Genetic linkage is a term used in genetics to describe the tendency of genes or DNA sequences that

are located close together on a chromosome to be inherited together4. Genetic linkage is not

relevant to this situation, as it has nothing to do with matching records from different data sources

based on the address column.

Cuff linkage is a term used in sewing to describe the process of attaching a cuff to a sleeve by

stitching or fastening. Cuff linkage is not relevant to this situation, as it has nothing to do with

matching records from different data sources based on the address column.

Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 452: Data Linkage: The Definitive

Guide, Tableau, 3: Guide to Business Data Analytics, IIBA, 2020, p. 454: Genetic Linkage, National

Human Genome Research Institute, . : Cuff Linkage, Sewing Dictionary, . : Data Linkage: The

Definitive Guide, Tableau, . : Genetic Linkage, National Human Genome Research Institute, . : Cuff

Linkage, Sewing Dictionary, .

The analytics team should restart the work with formation of a new research question, because the

existing one may not be well-defined, relevant, or feasible. A well-formed research question is the

first step of the business data analytics cycle, and it guides the subsequent steps of sourcing,

analyzing, interpreting, and reporting data. If the data analysis does not yield meaningful insights,

the team should revisit the research question and refine it based on the business problem,

stakeholder needs, data availability, and analytical methods. Reference:

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Understanding the Guide to Business Data Analytics, page 10-11

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Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain

1: Identify the Research Questions

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CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam

Sample Questions and Self-Assessment,

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:

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Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain

4: Interpret and Report Results

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Understanding the Guide to Business Data Analytics, page 9

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Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 4: Interpret and

Report Results, Lecture 20: Data Validation and Verification

Descriptive analytics is a type of analytics that summarizes and visualizes the data to provide an

overview of what has happened or is happening, such as the attrition rates and the personality test

scores of the employees12. The team has been asked to perform descriptive analytics to explore

possible relationships between the data variables, without making any predictions or prescriptions

for the future. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 182: Business Analytics:

Data Analysis & Decision Making,

S. Christian Albright and Wayne

L. Winston, 2015, p. 5.

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:

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Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain

5: Use Results to Influence Business Decision Making

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Understanding the Guide to Business Data Analytics, page 9

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Acceptance and Evaluation Criteria | Business Analysis

The average time for conversion is the average number of days it takes to convert a lead into a

customer. This is an important question to consider when evaluating sales performance, because it

indicates the efficiency and effectiveness of the sales process. A shorter time for conversion means

that the sales team can close more deals in less time, and thus increase the revenue and profitability

of the company. A longer time for conversion may indicate that there are bottlenecks, challenges, or

inefficiencies in the sales process that need to be addressed. 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

•

Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 4: Interpret and

Report Results, Lecture 19: Sales Performance Metrics

The attributes ItemName and ItemPrice need to be normalized to avoid redundancies because they

depend on the attribute ItemId, which is not part of the primary key of the Order entity. This is a case

of partial dependency, which violates the second normal form (2NF) of database normalization. To

achieve 2NF, the Order entity should be split into two entities: Order and Item, where Item contains

the attributes ItemId, ItemName, and ItemPrice, and Order contains the attributes OrderId,

OrderDate, ItemId, and Quantity. This way, the ItemName and ItemPrice are stored only once for

each ItemId, and the Order entity

them through a foreign key12 Reference: 1: Balancing

Data Integrity and Performance: Normalization vs … 2: Normalization Process in DBMS -

GeeksforGeeks

Variation is the degree to which the data values differ from each other or from a central tendency

measure, such as the mean or median. Variation can affect the data quality across cities, as it can

indicate the presence of outliers, errors, noise, or inconsistency in the data collection or processing

methods. Variation can also influence the statistical analysis and interpretation of the results, as it

can affect the significance, confidence, and validity of the findings12. Reference: 1: Guide to Business

Data Analytics, IIBA, 2020, p. 302: Statistics for Business and Economics, David R. Anderson et al.,

2014, p. 83.

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 p

, and

thus achieve their strategic goal of becoming the market leader. Reference:

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Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain

3: Analyze Data

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Understanding the Guide to Business Data Analytics, page 17

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CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam

Sample Questions and Self-Assessment,

Validity is the extent to which a measure or a question accurately captures the intended concept or

construct1. The question “What is your salary?” is not a valid measure of job satisfaction, as it does

not reflect the various aspects of job satisfaction, such as work environment, recognition, autonomy,

growth, etc. Salary is only one possible factor that may influence job satisfaction, but it is not a direct

or comprehensive indicator of it23. Therefore, the question is not valid for measuring job

satisfaction. Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 302: Job Satisfaction:

Application, Assessment, Causes, and Consequences, Paul E. Spector, 1997, p. 23: Job Satisfaction

Survey, 1.