Online Access Free C100DEV Practice Test

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 } } We need to use Aggregation Framework to fetch all movies from this collection where 'Drama' is in genres list and the minimum 'imdb.votes' is at least 100. Additionally, in the projection stage, we want to leave only the following fields: -> title -> genres -> imdb.votes We also want to sort the result set by decreasing imdb votes. Example output: [ { imdb: { votes: 1521105 }, genres: [ 'Crime', 'Drama' ], title: 'The Shawshank Redemption' }, { imdb: { votes: 1513145 }, genres: [ 'Crime', 'Drama' ], title: 'The Shawshank Redemption' }, { imdb: { votes: 1495351 }, genres: [ 'Action', 'Crime', 'Drama' ], title: 'The Dark Knight' }, ... Which pipeline should you use?

• A.[{ $match: { genres: { $in: ['Drama'] }, 'imdb.votes': { $gte: 100 } } }, { $project: { _id: 0, title: 1, genres: 1, 'imdb.votes': 1 } }, { $sort: { 'imdb.votes': 1 } }]
• B.[{ $match: { genres: { $in: ['Drama'] }, 'imdb.votes': { $gte: 100 } } }, { $project: { _id: 0, title: 1, genres: 1, 'imdb.votes': 1 } }, { $sort: { 'imdb.votes': -1 } }]
• C.[{ $match: { genres: { $in: ['Drama'] }, 'imdb.votes': { $gte: 100 } } }, { $project: { _id: 0, title: 1, genres: 1, 'imdb.votes': 1 } }, { $limit: { 'imdb.votes': -1 } }]

Suppose you have a products collection with an index: { product_category: 1 } For which of the following queries can MongoDB look at only a subset of the index entries, rather than all of the index entries?

• A.db.products.find( { product_category: /^A./ } )
• B.db.products.find( { product_category: /S./ } )
• C.db.products.find( { product_category: /A./ } )

What is the best practice in using the $match operator?

• A.It doesn't matter when we apply $match stage.
• B.The later in the pipeline, the better.
• C.The earlier in the pipeline, the better. Not only because we will be using expression filters which reduce the number of documents to be processed, but also because we can use indexes.

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' } } }])

Select all true statements regarding to duplicate documents in MongoDB.

• A.MongoDB can store duplicate documents in the same collection (with the same _id value).
• B.MongoDB can store duplicate documents in the same collection, as long as their _id values are different.