MongoDB C100DEV Actual Free Exam Questions & Community Discussion

In your database there is a collection named companies with the following document structure: { name: 'Wize', relationships: [ { is_past: false, title: 'Head of Product', person: { first_name: 'Ethan', last_name: 'Smith', permalink: 'ethan-smith' } }, { is_past: true, title: 'Director, Business Development', person: { first_name: 'Stephanie', last_name: 'Quay', permalink: 'stephanie-quay' } }, { is_past: true, title: 'Sr. Engineer', person: { first_name: 'Stefan', last_name: 'Antonowicz', permalink: 'stefan-antonowicz' } } ] } Which of the following queries should you use to extract all companies that have "Co-Founder" title in relationships field (Array)?

Which cursor method should you use to return the number of documents in the result set?

Suppose you have a mobile_games collection with the following document structure: { game: "Fishing Clash", company: "Ten Square Games", platforms: ['Android', 'IOS'], ... release_USA: ISODate("2017-04-09T01:00:00+01:00"), release_France: ISODate("2017-04-09T01:00:00+01:00"), release_Italy: ISODate("2017-08-17T01:00:00+01:00"), ... } Are there any problems with this document structure?

Select all true statements about MongoDB documents.

Which of the following actions are granted to the built-in read role?

Why is MongoDB using BSON instead of JSON to store data? Select all that apply.

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?

Suppose you have a reviews collection in your database and you want to optimize the following query: db.reviews.find( { "votes": { "$gt": 100 }, "rating": 4.5 } ).sort( { "date": 1 } ) Which index on this collection will be the most performant for the above query? Keep in mind the equality, sort, range rule.

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?

Suppose you have an accounts collection in your database. Only the following documents are stored in this collection: { _id: ObjectId("61af47c6e29861661d063714"), account_id: 1010, type: 'investment', limit: 2000000 }, { _id: ObjectId("61af47c6e29861661d063715"), account_id: 4336, type: 'derivatives', limit: 100000 }, { _id: ObjectId("61af47c6e29861661d063716"), account_id: 4336, type: 'commodity', limit: 1000 }, { _id: ObjectId("61af47c6e29861661d067825"), account_id: 7355, type: 'commodity', limit: 500000 }, { _id: ObjectId("61b1bde1ceb6f770f56b0cd9"), account_id: 4915, type: 'investment', limit: 2000000 } How many documents will be returned in response to the following aggregation pipeline? [{ $group: { _id: "$type", number_of_accounts: { $sum: 1 } }}, { $match: { number_of_accounts: { $gt: 1 } }}]

Suppose you have an accounts collection with the following document structure: { _id: ObjectId("61af47c6e29861661d063714"), account_id: 1010, type: 'investment', limit: 1000000 }, { _id: ObjectId("61af47c6e29861661d063715"), account_id: 4336, type: 'derivatives', limit: 100000 }, { _id: ObjectId("61af47c6e29861661d063716"), account_id: 4336, type: 'commodity', limit: 1000 } Which of the following documents would be modified by this update? db.accounts.updateMany( { "type": "investment" }, { "$set": { "limit": 2000000 } } )

Suppose you have a restaurants collection with the following document structure: { _id: ObjectId("5eb3d668b31de5d588f42931"), address: { building: '6409', coord: [ -74.00528899999999, 40.628886 ], street: '11 Avenue', zipcode: '11219' }, borough: 'Brooklyn', cuisine: 'American', grades: [ { date: ISODate("2014-07-18T00:00:00.000Z"), grade: 'A', score: 12 }, { date: ISODate("2013-07-30T00:00:00.000Z"), grade: 'A', score: 12 }, { date: ISODate("2013-02-13T00:00:00.000Z"), grade: 'A', score: 11 }, { date: ISODate("2012-08-16T00:00:00.000Z"), grade: 'A', score: 2 }, { date: ISODate("2011-08-17T00:00:00.000Z"), grade: 'A', score: 11 } ], name: 'Regina Caterers', restaurant_id: '40356649' } You don't have any indexes so far. What will the query plan look like for the following query?
db.restaurants.find( { "cuisine": "American" } )