How to connect to mongod instance and authenticate as root?

• A.mongoroot --authenticationDatabase admin
• B.mongo --username root --password root123 --authenticationDatabase admin
• C.mongo --authenticationDatabase admin

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You have a developers collection with the following document structure: { _id: 1, fname: 'John', lname: 'Smith', tech_stack: ['sql', 'git', 'python', 'linux', 'django', 'aws'] }, { _id: 2, fname: 'Michael', lname: 'Doe', tech_stack: [ 'git', 'python', 'sqlite', 'linux', 'flask' ] } Which of the following queries will return only the first three elements of the array in the tech_stack field?

• A.db.developers.find( {}, { tech_stack: [0, 1, 2] } )
• B.db.developers.find( { tech_stack: { $slice: [0, 3] } } )
• C.db.developers.find( {}, { tech_stack: [0, 3] } )
• D.db.developers.find( {}, { tech_stack: { $slice: [0, 3] } } )

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

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

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

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A collection called players contains the following documents: [ { _id: 1, user: 'Tom', scores: [ 23, 56, 3, 52, 62 ], bonus: 5 }, { _id: 2, user: 'Jane', scores: [ 42, 50, 10 ], bonus: 3 } ] You want to add additional fields to each document: -> total_score (sum of the scores Array) -> avg_score (average score in scores Array) Expected output: [ { _id: 1, user: 'Tom', scores: [ 23, 56, 3, 52, 62 ], bonus: 5, total_score: 196, avg_score: 39.2 }, { _id: 2, user: 'Jane', scores: [ 42, 50, 10 ], bonus: 3, total_score: 102, avg_score: 34 } ] Which query do you need to use?

• A.db.players.aggregate([{ $addFields: { total_score: { $sum: '$scores' }, avg_score: { $avg: '$scores' } } }])
• B.db.players.aggregate([{ $addFields: { total_score: { $sum: 'scores' }, avg_score: { $avg: 'scores' } } }])
• C.db.players.aggregate([{ $add: { total_score: { $sum: '$scores' }, avg_score: { $avg: '$scores' } } }])

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