Explanation
One of the steps in identifying the research questions for business data analytics is to assess the feasibility and desirability of the proposed solution or change1. This involves understanding the needs, preferences, and satisfaction of the existing and potential customers. Therefore, asking whether the existing customers really like the product is a relevant research question for the analytics team. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 22.

A journey map is a visual representation of the interactions and experiences of a customer or stakeholder with an organization, product, or service over time. A journey map can help identify pain points, gaps, opportunities, and emotions along the customer journey. A journey map can also help understand the current state of the customer experience and how it can be improved or innovated using business analytics.
References:Guide to Business Data Analytics, page 55; Introduction to Business Data Analytics: An Organizational View, page 18.

A normal probability plot is a graphical technique that can be used to check if a variable follows a Gaussian distribution. It plots the observed values of the variable against the expected values under the normal distribution. If the variable is normally distributed, the points should form a straight line. A scatterplot, a boxplot, and a lowess curve are not suitable for testing normality, as they do not compare the observed values with the theoretical values of the normal distribution.
https://www.graphpad.com/support/faq/testing-data-for-normal-distrbution/

Explanation
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.

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.