- (Topic 1)
The analytics team is identifying research questions to address a business problem. The business analysis professional reminds the team that the most important dimension to consider is the:

1. A. Sources of data
2. B. Quality of the data
3. C. Timeframe of analysis
4. D. Measurement scale

Correct Answer: B
The quality of the data is the most important dimension to consider when identifying research questions, as it affects the validity, reliability, and accuracy of the analysis and the results. Data quality refers to the degree to which the data meets the requirements and expectations of the stakeholders and the purpose of the analysis12. Poor data quality can lead to erroneous conclusions, ineffective decisions, and wasted resources3. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 282: Data Quality Assessment, Arkady Maydanchik, 2007, p. 33: Data Quality: The Field Guide, Thomas C. Redman, 2001, p. 1.

- (Topic 1)
Which attributes from the Order entity will need to be normalized to avoid redundancies?
. Orderld
. OrderDate
. Itemld
. ItemName
. Quantity
. ItemPrice

1. A. OrderDate ItemPrice
2. B. ItemName ItemPrice
3. C. OrderDate ItemName
4. D. Item Name Quantity

Correct Answer: B
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 references them through a foreign key12 References: 1: Balancing Data Integrity and Performance: Normalization vs ?? 2: Normalization Process in DBMS - GeeksforGeeks

- (Topic 2)
A toy manufacturing company wants to improve operational efficiencies as a means of reducing costs. The Operational Manager wants an analytics study to identify areas of improvement within their operational processes. During a meeting with the analyst, the Operational Manager mentions concerns about old machinery and suggests this be the area of focus for the study. They can have a touchpoint in three weeks to assess progress. Has the Operational Manager limited the potential of this study?

1. A. By providing the focus area of the study, the Operational Manager has limited the scope of the study with their biased opinion
2. B. The Operational Manager is the expert, so there is no problem in the manager providing guidance to the analyst
3. C. The Operational Manager has limited the scope of the budget by providing a timeline ofthree weeks
4. D. Since the study is being funded by the Operational Manager, providing the focus areas helps the analyst stay on track with time and budget

Correct Answer: A
According to the Guide to Business Data Analytics, one of the key competencies of a business data analyst is to identify the research questions that guide the analytics work1. The research questions should be based on the business problem or opportunity, the stakeholder needs, and the data availability and quality2. By providing the focus area of the study, the Operational Manager has limited the scope of the study with their biased opinion, as they have not considered other possible factors that might affect theoperational efficiencies, such as demand, inventory, quality, labor, or customer satisfaction. The Operational Manager has also not involved other stakeholders who might have different perspectives or interests in the study. This could lead to a narrow or incomplete analysis that might miss some important insights or recommendations. The Operational Manager should instead collaborate with the analyst to define the research questions that are relevant, specific, measurable, achievable, and time-bound3.
The other options are not correct, as they do not address the issue of defining the research questions. The Operational Manager is not necessarily the expert on the operational processes, as they might have a limited or biased view of the situation. The Operational Manager has not limited the scope of the budget by providing a timeline of three weeks, as this is a reasonable time frame for an analytics study, depending on the complexity and availability of the data. The Operational Manager has not helped the analyst stay on track with time and budget by providing the focus areas, as this might actually waste time and resources if the focus areas are not aligned with the actual business problem or opportunity.
References:1: Guide to Business Data Analytics, IIBA, 2020, p. 312: Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 113: Introduction to Business Data Analytics: An Organizational View, IIBA, 2019, p. 12.

- (Topic 1)
A large telecommunications company wants to increase their Average Revenue Per User per month by 5%, by end of year, to increase revenue in a highly competitive market. From a SMART target perspective, what is missing?

1. A. T - The increase should be seen sooner
2. B. A - It is too easy of a target to attain
3. C. R - Since competition is high, focus should be on increasing customer base and not on ARPU
4. D. S - There is no mention of which product group/line the target pertains to

Correct Answer: D
A SMART target is one that is specific, measurable, achievable, relevant, and time-bound1. The target of increasing the Average Revenue Per User (ARPU) per month by 5%, by end of year, to increase revenue in a highly competitive market is missing the specificity criterion, as it does not mention which product group or line the target applies to. The target should be more specific and clear about the scope and context of the desired outcome, such as which segment, region, or service the target relates to23. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 192: SMART Goals: How to Make Your Goals Achievable, MindTools, 2021, 13: How to Set SMART Marketing Goals, CoSchedule, 2021, 2.

- (Topic 2)
An insurance company would like to develop a range of insurance products for different types of customers. The analytics team is asked to conduct some research and share their insights with senior management. Which technique would be useful to divide the customer base into groups?

1. A. Linear regression
2. B. Survey sampling
3. C. Factor analysis
4. D. K-means clustering

Correct Answer: D
K-means clustering is a technique that partitions a set of data points into a predefined number of clusters, based on their similarity or distance. This technique can be useful to divide the customer base into groups that have similar characteristics, preferences, or behaviors, and then design insurance products that cater to each group??s needs and expectations. K-means clustering can also help identify outliers or anomalies in the customer data that may require further investigation or attention.
References: Guide to Business Data Analytics, page 58-59; CBDA Exam Blueprint, page 7; [Introduction to Business Data Analytics: A Practitioner View], page 17.