The appropriate answer is d. mail merge. Mail merge uses a database of addresses that are used to create pre-addressed mailing labels that are generally used when sending letters to a very large group. This type of application is used by utility companies or any other organizations that requires mass mailings. Mail merge is found in the Microsoft Word application. Excell spreadsheets can also be used to complete tasks similar to that of mail merge.
Answer:
decide that when online customers and retail stores order bikinis, they will never have to wait more than two weeks for the order to arrive.
Explanation:
Based on the information provided within the question it can be said that in this scenario the best action that Helena can take is to decide that when online customers and retail stores order bikinis, they will never have to wait more than two weeks for the order to arrive. Otherwise she will begin to lose customers constantly by not having stock or shipping the product in time. These losses will continue to amass and may eventually ruin her business.
Hey there!
I believe your answer will be the "Shift" key. You can hold down Shift and select a cell from your current cell position to select everything between the original position and the cell you clicked on without having to drag your mouse across the cells. If you were to hold down the Ctrl or Command key, you would only have your original selection and your new selection highlighted.
Your answer will be Shift.
Hope this helped you out! :-)
Answer:
First one is INHERITANCE. Second one is AGGREGATION.
Answer:
Collaborative filtering
Explanation:
This is one out of five on the Recommender system apart from most popular items, Association and Market Basket based Analysis, Content-based analysis, self and hybrid analysis where we use both content-based and collaborative based approach together. And the Recommender system is a very important topic in Data science. For this question, remember that Collaborative filtering focuses on user and various other user's choices which are mathematically alike to concerned users, and which we find with the study of a large data set. Thus, we can predict from our above study that what are going to be likes of concerned users, and at the item level, whether that item will be liked by the concerned user or not. And this is prediction, and we use this approach in Machine learning these days. For this question, and as mentioned in question the requirements, answer is Collaborative filtering.