Data+ Practice Questions

Truncating the axis, specifically the Y-axis on a bar chart, is a common way to mislead viewers. Bar charts represent quantity through the length of the bars, and this length should be proportional to the value. By starting the axis at a value other than zero, this proportionality is broken, and the visual differences between the bars are exaggerated. It is a best practice for bar chart axes representing magnitude to always start at zero.

The primary differentiator of a dashboard is its ability to provide a high-level, visual overview of multiple key metrics on a single screen. Dashboards are designed for at-a-glance monitoring and are typically interactive, allowing users to filter and drill down into the data. Reports, in contrast, are often static and provide more detailed, specific information, frequently in a tabular format.

GDPR is a regulation in EU law on data protection and privacy for all individuals within the European Union and the European Economic Area. It also addresses the transfer of personal data outside the EU and EEA areas. Any organization, regardless of its location, that processes the personal data of EU residents must comply with GDPR.

A data dictionary is a centralized repository of information about data, such as its meaning, relationships to other data, origin, usage, and format. It is a critical component of data governance as it provides clarity and consistency in data definitions across the organization, ensuring everyone is using and interpreting data in the same way.

A star schema is the optimal choice for this scenario. It is designed specifically for data warehousing and business intelligence applications where query performance and simplicity are paramount. By denormalizing dimensions (e.g., including region information directly in the store dimension), it reduces the number of joins required for queries, directly addressing the consortium's primary requirement for faster, simpler analysis compared to the more complex, normalized snowflake schema.

Ordinal data is a categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories are not known. 'Very Satisfied' is higher than 'Satisfied', establishing an order, but the exact difference in satisfaction is not measurable, making it ordinal. Nominal data has no order, interval data has order and a known difference but no true zero, and ratio data has all these properties plus a true zero.

A star schema is the fundamental structure of a dimensional model in a data warehouse. It consists of a central fact table containing quantitative data (measures) connected to a set of smaller dimension tables, which contain descriptive attributes. This denormalized structure is optimized for fast querying and is the most common schema used for data analysis and business intelligence.

A dimensional model using a star or snowflake schema is the industry standard for data warehousing and analytics. By creating a central fact table for sales metrics and separate dimension tables for store, product, and date, queries become simpler and much faster. This design directly supports the business requirement of slicing and dicing data by these key dimensions.

A join operation is used to combine rows from two or more tables based on a related column between them. In this scenario, the customer_id column exists in both datasets and can be used as the key to join the customer names with their corresponding order amounts. Append/Union operations stack datasets vertically and are not suitable for this task.

This statement is true. A primary key is a fundamental constraint in relational database design. Its core purpose is to uniquely identify each record in a table. To achieve this, it enforces two rules: the values must be unique (no duplicates) and they cannot be NULL.