Explore key principles of data import and integration in business intelligence platforms with this quiz. Assess your understanding of data sources, connection methods, transformation processes, and best practices for combining and preparing data for analysis.
Which of the following is considered an external data source for importing into an analytics platform?
Explanation: A local spreadsheet file is an example of an external data source that can be imported for analysis or reporting. Dashboard filters and report visualizations are part of the analytical tool itself, not external sources. Similarly, a theme setting pertains to user interface customization, not data input. Only the spreadsheet option fits the context of external data import.
During data integration, why might you perform a data transformation such as converting dates to a standard format?
Explanation: Standardizing dates through transformation makes data consistent, enabling smooth merging and analysis. Increasing file size and deleting important fields are usually undesirable, and making integration slower is not a valid reason. Standardization addresses problems caused by different date formats during integration.
What does an incremental data import achieve when integrating with a large transactional database?
Explanation: Incremental data import brings in only new or modified records, making updates efficient and saving time. Importing all records repeatedly is inefficient and unnecessary. Deleting old records is not part of incremental import, and compressing the database is unrelated to the import process.
Suppose you have sales data stored online in a remote server; what kind of connection method allows you to access it directly for import?
Explanation: A direct cloud connector enables access to remotely stored data for seamless import. Manual text entry is impractical and error-prone for large datasets. Theme customization and local printing are unrelated to data access or import processes. This makes the direct connector method the most appropriate choice.
When integrating two tables with customer records using a shared customer ID, what process is taking place?
Explanation: Joining on a key field lets you combine information based on shared attributes like customer ID. Sorting simply arranges data, not integrates it. Applying a color theme is entirely visual, and ignoring duplicates is a separate data quality action, not integration.
Which step should you take before integrating data from two sources if values like 'N/A' or missing fields are present?
Explanation: Cleaning or replacing missing values ensures data quality and integration accuracy. Deleting all data is not a solution, skipping validation can lead to problems, and duplicating records may introduce inconsistencies. Addressing data quality is essential before merging datasets.
If you need your reports to always display up-to-date information from an external source, what should you configure?
Explanation: Setting up a regular data refresh schedule ensures information remains current by updating data at specified intervals. Color schemes and print previews pertain only to appearance and output, while manual calculation mode is unrelated to automatic updates. A refresh schedule is therefore necessary for up-to-date data.
Which file type is commonly supported for data import into analytical tools?
Explanation: CSV files are structured text files ideal for importing tabular data, making them a standard choice. MP4, JPG, and EXE are formats for video, images, and programs, respectively, and are not designed for structured data import. Thus, CSV is the correct file type.
If you notice multiple identical entries after combining datasets, which data integration step applies?
Explanation: Removing duplicates ensures data accuracy and prevents inflated analytics. Changing text color and enlarging headers modify appearance but don't address data issues. Turning off notifications has no effect on dataset content or integration steps.
Why is data mapping important when importing two datasets with different column names referring to the same information, for example, 'Email' and 'e-mail address'?
Explanation: Data mapping matches different names that represent the same data, ensuring accurate merging. Encryption protects data confidentiality, but not alignment. Printing summaries and hiding fields are unrelated to the issue of matching corresponding data headers during integration.