CBDA Online Practice Questions and Answers

Correct Answer: A

A physical data model is the most detailed and specific type of data model, which shows how the data is stored, accessed, and manipulated in the database. It includes the table structures, column names, data types, primary and foreign keys, constraints, indexes, and other physical attributes of the data

Correct Answer: D

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.

Correct Answer: A

Stratified sampling is a technique that divides the population into homogeneous subgroups (strata) based on a relevant characteristic, such as the amount of purchases, and then randomly selects a proportional number of elements from each subgroup to form the sample. Stratified sampling ensures that the sample is representative of the population and reduces the sampling error and bias

Correct Answer: D

The business analyst is using the rough order of magnitude method to estimate this work. This method is based on expert opinion or experience from past projects, and it provides a quick and approximate estimate of the cost, time, or effort required for a project or a task. This method is useful when there is limited information or data available, or when a high-level estimate is needed for planning or budgeting purposes. However, this method also has a high degree of uncertainty and variability, and it should be refined as more details become available

Correct Answer: D

Ensuring the result set is statistically significant is important to the team because it means that the difference or relationship observed in the data is unlikely to be due to chance or sampling error. Statistical significance helps the team to assess the validity and reliability of their findings, and to draw meaningful conclusions and recommendations from the data. Statistical significance also helps the team to communicate their results with confidence and credibility to the stakeholders and decision makers

Correct Answer: C

When preparing an executive dashboard, the analyst should keep in mind that the purpose of the dashboard is to provide a quick and clear overview of the past year's performance and to support strategic decision making for the new year. Therefore, the analyst should keep the dashboard high-level, summarizing the key insights and metrics that are relevant and meaningful for the senior executives. The analyst should avoid cluttering the dashboard with too much detail or information that is not essential for the executives. The analyst should also use visual features, such as charts, graphs, and colors, to display the data in an organized and appealing way

Correct 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.

Correct Answer: D

According to the IIBA?Guide to Business Data Analytics, analytics work should be driven by well-defined business problems or opportunities that are aligned with the organization's strategic objectives1. Having an abundance of data does not necessarily mean that all of it is relevant, reliable, or useful for the analytics purpose. Therefore, the analytics team should focus on using the data to answer a limited number of key questions that are derived from the business context and that can generate actionable insights and outcomes. This approach can help the analytics team prioritize the most important data sources, methods, and tools, as well as avoid wasting time and resources on analysis that is not impactful or meaningful for the organization.

Correct Answer: B

Document analysis is a qualitative research technique that evaluates electronic and physical documents to interpret them and gain an understanding of their meaning1. It can be used to study various types of documents, such as informal, external, or contextual documents, and to explore their meanings, patterns, and themes. Data mining is a form of document analysis that involves applying statistical and computational methods to large datasets to discover hidden patterns, trends, or relationships2. Data mining can help analysts answer complex questions, generate hypotheses, or support decision making. Therefore, the correct answer is B, as data mining is a form of document analysis.

Correct Answer: D

According to the Guide to Business Data Analytics, data profiling is a technique that analyzes the structure, content, and quality of data sources. Data profiling can help identify data issues such as missing values, outliers, inconsistencies, duplicates, and errors. Data profiling can also provide information about the data types, formats, ranges, distributions, and relationships of data elements. Data profiling can help prepare data for data conversion, data cleansing, and data migration by providing a high level understanding of the current state of data and the potential challenges and risks involved in transforming and integrating data from different sources.