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NEW QUESTION 50
Given a movies collection where each document has the following structure: { _id: ObjectId(“573a1390f29313caabcd60e4”), genres: [ ‘Short’, ‘Comedy’, ‘Drama’ ], title: ‘The Immigrant’, year: 1917, imdb: { rating: 7.8, votes: 4680, id: 8133 }, countries: [ ‘USA’ ] } This time we are using the Aggregation Framework to search our database. Which of the following queries will find all movies with a rating greater than 8?
- A. db.movies.aggregate([ { $match: { imdb.rating: { $gt: 8 } } } ])
- B. db.movies.aggregate([ { $match: { “imdb.rating”: { $gt: 8 } } } ])
- C. db.movies.aggregate([ { $match: { “imdb.rating”: { $gte: 8 } } } ])
Answer: B
Explanation:
db.movies.aggregate([ { $match: { imdb.rating: { $gt: 8 } } } ]) This is incorrect because we need to use quotes in this case. db.movies.aggregate([ { $match: { “imdb.rating”: { $gte: 8 } } } ]) This is incorrect because $gte also checks for equality. https://docs.mongodb.com/manual/reference/method/db.collection.aggregate/
NEW QUESTION 51
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” } } }]
Answer: D
Explanation:
https://docs.mongodb.com/manual/aggregation/
NEW QUESTION 52
How can we represent a one-to-one relationship in MongoDB?
- A. We can embed fields as a child document in the document.
- B. We can embed fields in the document.
- C. We can link to a single document in another collection.
Answer: A,B,C
Explanation:
https://docs.mongodb.com/manual/tutorial/model-embedded-one-to-one-relationships-between-documen ts/
NEW QUESTION 53
Suppose you added the following index to a products collection: { product_category: 1 } Which of the following operations can potentially decrease performance?
- A. db.products.insertOne( { product_category: ‘clothes’ } )
- B. db.products.find( { product_category: ‘clothes’ } )
- C. db.products.updateOne( { product_category: ‘shoes’ }, { $set: company: ‘Nike’ } )
Answer: A,C
Explanation:
The updateOne and insertOne operations are correct because adding indexes affects write performance.
NEW QUESTION 54
Which of the following constraints might impact your data model for MongoDB?
- A. RAM
- B. Network
- C. Security & Compliance
- D. Operating System
Answer: A,B,C
Explanation:
https://docs.mongodb.com/manual/core/data-modeling-introduction/
NEW QUESTION 55
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