- (Topic 1)
An analyst at a supermarket chain has been asked to extract data from multiple data sources to complete a study on customer spending habits. The analyst is going to query data from various databases. Which statement is true about database querying?

1. A. Querying can be used to create predictive data models
2. B. Irrespective of the querying language used, data results retrieved are always in a tabular format
3. C. A querying language is independent of the type of database being used
4. D. Querying is a structured way of searching, manipulating and managing data

Correct Answer: D
Querying is a technique that allows analysts to access, filter, join, aggregate, and transform data from various databases using a specific syntax and logic1. Querying can be used for different purposes, such as data exploration, data preparation, data analysis, and data visualization2. Querying is not limited to creating predictive data models, nor does it always produce tabular results. Moreover, querying languages may vary depending on the type and structure of the database, such as relational, hierarchical, or document-based3. References: 1: Guide to Business Data Analytics, IIBA, 2020, p. 552: Data Analysis Using SQL and Excel, Gordon S. Linoff, 2016, p. 33: Database Systems: Design, Implementation, and Management, Carlos Coronel and Steven Morris, 2019, p. 17.

- (Topic 2)
A marketing department has established an analytics team. The analytics practice is stand- alone and analysts have limited insights into corporate strategy. Which is an expected result for analytics practices operating at the business unit level?

1. A. Analytics work will be driven by the organization's business plan
2. B. Insights derived from data analysis will be used to guide strategic decisions
3. C. The analytics team may conduct analysis that is of minimal value to the organization
4. D. The organization will use analytics as a means to obtain a competitive advantage

Correct Answer: C
According to the IIBA® Guide to Business Data Analytics, analytics practices operating at the business unit level are characterized by a lack of alignment with the organization??s strategic objectives, a limited scope of analysis, and a siloed approach to data and insights1. This can result in analytics work that is not relevant, timely, or impactful for the organization as a whole, and that may not address the most critical business problems or opportunities. Therefore, the analytics team may conduct analysis that is of minimal value to the organization, or even detrimental if it leads to suboptimal decisions or actions.
References:1: IIBA® Guide to Business Data Analytics, Chapter 2: Business Data Analytics in Context, page 14-15