Data & Analysis Assessment Questions

Which of the following lists the key steps to start an analytics project?

• Define objectives, collect data, analyze data, interpret results

• Collect data, skip analysis, present random results

• Ignore objectives, analyze without data, interpret randomly

• Start with results, then collect data, ignore objectives

TD professionals can align projects with business goals by:

• Focusing solely on technical skills

• Ignoring stakeholder input

• Ensuring projects support organizational objectives

• Prioritizing personal interests

Which of the following are three key elements of stakeholder analysis?

• Identifying stakeholders, assessing their interests, determining their influence

• Setting project goals, creating a budget, assigning tasks

• Developing a marketing plan, launching a product, measuring sales

• Hiring employees, training staff, evaluating performance

Which of the following best explains the difference between qualitative and quantitative data?

• Qualitative data describes qualities or characteristics, while quantitative data involves numbers and measurements.

• Qualitative data is always numerical, while quantitative data is always descriptive.

• Qualitative data is used only in mathematics, while quantitative data is used only in literature.

• Qualitative data and quantitative data mean the same thing.

Which of the following is a common mistake to avoid in data analysis?

• Ignoring data cleaning

• Using appropriate statistical methods

• Validating results thoroughly

• Documenting all steps

The purpose of each type of analytics is as follows: descriptive (summarizes past data), diagnostic (explains why something happened), predictive (forecasts future outcomes), and prescriptive (recommends actions). Which of the following correctly matches the analytics type with its purpose?

• Descriptive: forecasts future outcomes; Diagnostic: recommends actions; Predictive: summarizes past data; Prescriptive: explains why something happened.

• Descriptive: summarizes past data; Diagnostic: explains why something happened; Predictive: forecasts future outcomes; Prescriptive: recommends actions.

• Descriptive: recommends actions; Diagnostic: summarizes past data; Predictive: explains why something happened; Prescriptive: forecasts future outcomes.

• Descriptive: explains why something happened; Diagnostic: forecasts future outcomes; Predictive: recommends actions; Prescriptive: summarizes past data.

Which of the following is considered a best practice for data visualization?

• Using excessive colors and effects

• Presenting data clearly and accurately

• Overloading charts with information

• Ignoring the target audience

Storytelling is important in data presentation because it:

• Makes data more engaging and understandable

• Increases the amount of data shown

• Removes the need for visuals

• Focuses only on numbers

What should be considered when comparing data using benchmarks?

• The context and relevance of the benchmarks

• The color of the data charts

• The number of benchmarks available

• The font used in the report

Examples of appropriate charts for different data types include:

• Bar chart for categorical data, line chart for time series data, scatter plot for numerical data

• Pie chart for time series data, histogram for categorical data, line chart for geographical data

• Scatter plot for categorical data, bar chart for numerical data, pie chart for time series data

• Histogram for categorical data, bar chart for time series data, scatter plot for geographical data