The statement which best explains how algorithms running on a computer can be used to solve problems is; D. Some problems cannot be solved by an algorithm.
<h3>What is an algorithm?</h3>
An algorithm is simply a standard formula or procedures which is made up of a set of finite steps and instructions that must be executed on a computer, so as to proffer solutions to a problem or solve a problem under appropriate conditions.
However, it should be noted that it is not all problems that can be solved by an algorithm, especially because the required parameters and appropriate conditions are not feasible or met.
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Answer:
B. Using a Rate Rule in the Community Manager
Explanation:
Rate rules are techniques to protect your community from spammers and web bots that attack or spam your community by posting the similar message several times in a row. You can build rate rules to alert your community moderators of suspicious spammer-like activities in your community or freeze a member on the spot.
Take for example, when the universal shipping introduces a rate rule to their comunity, Community Moderators will now be alerted when a member post more than one file to the community within a 15-minute window thereby making them frooze the account accordingly.
Answer:
SELECT Count(order_invoice) as number_of_invoices, Max(order_invoice) as maximum_invoice, Min(order_invoice) as minimum_invoice, Avg(order_invoice) as average_invoice
FROM vendor JOIN invoice ON invoice.id = vendor.id
WHERE order_invoice > 1
ORDER BY number_of_invoices DESC
Explanation:
The select statement of the SQL or structured query language returns twelve rows of four columns from the inner join of the vendor and invoice table in a database where the order_invoice column in the invoice table is greater than one. The result of the query is ordered by the alias column "number_of_invoices" in descending order.
Answer:
C.<em> improve data quality and reduce redundancies, reduce increased and staggering storage management costs</em>
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Explanation:
Excess data retention can lead to retention of redundant and irrelevant data, which can reduce data handling and processing efficiency, while at the same time increasing the cost of data storage and management. This can be curbed be reducing data to the right size by using data governance techniques like data cleansing and de-duplication
