Explanation:
User backups typically contain a large amount of redundant data, such as documents, images, or other user-created files, which are prime candidates for compression. Compression algorithms work by identifying and reducing redundancy in data, making it highly effective for backup files, which often contain large amounts of repetitive or static data.
B. User backups: User backups typically contain a variety of file types, many of which (such as text documents, spreadsheets, and other office files) can be highly compressible. Compressing these backups can lead to significant storage savings.
User backups, with their diverse and often highly compressible content, stand to gain the most in terms of reduced storage consumption through compression.
The effectiveness of compression depends on the nature of the data being compressed. Generally, file types that contain repetitive patterns or redundant information benefit the most from compression because they can be significantly reduced in size.
Out of all of the options only the relational database has data that is FOR SURE repeated (because its relational).
When you compress data, you trade physical bit size for compute. Small disk size, but more compute to read the data. Typically you only compress stuff that will be in storage and not read that much. A,C,D are all in use, so you would increase compute for the smaller size benefit.
This has nothing to do with what the question is asking. What would benefit most from compression means what will compress the best. You're reading too much into the question
C saves over 20-80%
B saves 20-70%
Ultimately both user backups and relational databases can benefit from compression, but the decision should be based on specific requirements, such as storage efficiency, performance considerations, and resource utilization in regards which method would yield the greatest overall return.
The file type that would benefit the MOST from compression among the options provided is:
C. Relational database
Relational databases often contain structured data with repetitive patterns, making them well-suited for compression. Compressing a relational database can significantly reduce storage consumption without sacrificing data integrity or performance.
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