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NEW QUESTION 23
The users collection in the database is given. Which field will be the best candidate for an index in terms of cardinality?

• A. age (medium cardinality)
• B. gender (low cardinality)
• C. email (high cardinality)

Answer: C

Explanation:
https://docs.mongodb.com/manual/indexes/

NEW QUESTION 24
We have a movies collection with the following document structure: { _id: ObjectId("573a1390f29313caabcd6223"), genres: [ 'Comedy', 'Drama', 'Family' ], title: 'The Poor Little Rich Girl', released: ISODate("1917-03-05T00:00:00.000Z"), year: 1917, imdb: { rating: 6.9, votes: 884, id: 8443 } }, { _id: ObjectId("573a13e3f29313caabdc08a4"), genres: [ 'Horror', 'Thriller' ], title: 'Mary Loss of Soul', year: 2014, imdb: { rating: '', votes: '', id: 2904798 } } We need to use Aggregation Framework to calculate the following aggregates: -> average imdb rating -> minimum imdb rating -> maximum imdb rating Expected output: [ { _id: null, avg_rating: 6.6934040649367255, min_rating: 1.6, max_rating: 9.6 } ] Please note that some documents have "" (empty string) for the field "imdb.rating". Exclude these documents before aggregation. Which pipeline should you use?

• A. [{ $match: { "imdb.rating": { $ne: "" } } }, { $group: { _id: null, avg_rating: { avg: "$imdb.rating" }, min_rating: { min: "$imdb.rating" }, max_rating: { max: "$imdb.rating" } } }]
• B. [{ $group: { _id: null, avg_rating: { $avg: "$imdb.rating" }, min_rating: { $min: "$imdb.rating" }, max_rating: { $max: "$imdb.rating" } } }]
• C. [{ $match: { "imdb.rating": { $ne: "" } } }, { $group: { _id: null, avg_rating: { $avg: "imdb.rating" }, min_rating: { $min: "imdb.rating" }, max_rating: { $max: "imdb.rating" } } }]
• D. [{ $match: { "imdb.rating": { $ne: "" } } }, { $group: { _id: null, avg_rating: { $avg: "$imdb.rating" }, min_rating: { $min: "$imdb.rating" }, max_rating: { $max: "$imdb.rating" } } }]

Answer: D

Explanation:
https://docs.mongodb.com/manual/aggregation/

NEW QUESTION 25
You have the following index in a products collection: { product_category: 1, product_name: 1, origin_country: -1 } Which of the following queries can use this index? Check all that apply.

• A. db.products.find({ product_category: 'shoes', product_name: 'AIR MAX 90', origin_country: 'Bangladesh' })
• B. db.products.find({ origin_country: 'Bangladesh', product_name: 'AIR MAX 90', product_category: 'shoes' })
• C. db.products.find({ product_name: 'AIR MAX 90', product_category: 'shoes', origin_country: 'Bangladesh' })
• D. db.products.find({ product_name: 'AIR MAX 90', origin_country: 'Bangladesh' })

Answer: A,B,C

Explanation:
https://docs.mongodb.com/manual/indexes/

NEW QUESTION 26
Let's consider a one-to-many relationship observed between an instructor and the courses he is created on an e-learning platform. We assume that a course can only have one instructor, and an instructor can have multiple courses. Which of the following are the correct ways to represent this one-to-many relationship with a document model in MongoDB?

• A. Have a collection for the instructors and a collection for the courses with each course document having a field to reference the document of its instructor.
• B. Embed all the fields for a course as a subdocument in the corresponding instructor document.
• C. Embed the entities for the courses as an array of sub-documents in the corresponding instructor document.

Answer: A,C

Explanation:
Embed all the fields for a course as a subdocument in the corresponding instructor document.
These subdocuments must be put into an array, not stored directly as a subdocument in its parent.
This way we can have a variable number of referenced documents and use a multi-key index to search the "many" side. This is not the best way to do it.
https://docs.mongodb.com/manual/tutorial/model-embedded-one-to-many-relationships-between-documents/

NEW QUESTION 27
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