Free MongoDB C100DEV Exam Questions

Question 1

As a MongoDB Developer, you are tasked with the deletion of specific documents in a collection named users. The documents to be deleted are those that meet the following criteria: the age field is less than 18 and the status field is "inactive". After the deletion, you need to know the count of the deleted documents. Which of the following commands would correctly perform this operation?

A :

B :

C :

D :

E :

Answer: A

Question 2

Consider a MongoDB database containing a collection of documents representing product information for an e-commerce website. The documents have the following structure:

{

"_id": ObjectId("5f95a1d11a12b400001b75c0"),

"product_name": "Smartphone",

"brand": "Apple",

"price": 800,

"categories": [ "Electronics", "Smartphones" ],

"reviews": [

{ "username": "user1", "rating": 4, "comment": "Great product!" },

{ "username": "user2", "rating": 5, "comment": "Excellent!" },

{ "username": "user3", "rating": 3, "comment": "Good but overpriced." }

]

}

Select the MongoDB aggregation pipeline that returns the average rating of all products grouped by brand. The result should include only brands with an average rating greater than or equal to 4. The output should have the following format:

A : [ { "$unwind": "$reviews" }, { "$group": { "_id": "$brand", "avg_rating": { "$avg": "$reviews.rating" } } }, { "$match": { "avg_rating": { "$gte": 4 } } }]

B : [ { "$group": { "_id": "$brand", "avg_rating": { "$avg": "$reviews.rating" } } }, { "$match": { "avg_rating": { "$gte": 4 } } }, { "$project": { "brand": "$_id", "avg_rating": "$avg_rating", "_id": 0 } }]

C : [ { "$unwind": "$reviews" }, { "$group": { "_id": "$brand", "avg_rating": { "$avg": "$reviews.rating" } } }, { "$project": { "brand": "$_id", "avg_rating": "$avg_rating", "_id": 0 } }]

D : [ { "$unwind": "$reviews" }, { "$group": { "_id": "$brand", "avg_rating": { "$avg": "$reviews.rating" } } }, { "$match": { "avg_rating": { "$gte": 4 } } }, { "$project": { "brand": "$_id", "avg_rating": "$avg_rating", "_id": 0 } }]

Answer: D

Question 3

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" } )

A : IXSCAN

B : SORT

C : FETCH

D : COLLSCAN

Answer: D

Question 4

What is the difference between a sparse index and a non-sparse index in MongoDB?

A : A sparse index only indexes documents that contain the indexed field, while a non-sparse index indexes all documents in the collection, including those that do not contain the indexed field.

B : A sparse index indexes all documents in the collection, including those that contain the indexed field, while a non-sparse index only indexes documents that do not contain the indexed field.

C : A sparse index only indexes documents that do not contain the indexed field, while a non-sparse index indexes all documents in the collection, including those that contain the indexed field.

D : A sparse index indexes all documents in the collection, including those that do not contain the indexed field, while a non-sparse index only indexes documents that contain the indexed field.

Answer: A

Question 5

We have an accounts collection with the following document structure:

{

_id: ObjectId("5ca4bbc7a2dd94ee5816239d"),

account_id: 864905,

limit: 10000,

products: [ 'Commodity', 'InvestmentStock' ]

},

{

_id: ObjectId("5ca4bbc7a2dd94ee5816239e"),

account_id: 299072,

limit: 10000,

products: [ 'InvestmentFund', 'InvestmentStock' ]

},

{

_id: ObjectId("5ca4bbc7a2dd94ee5816239f"),

account_id: 137994,

limit: 10000,

products: [ 'CurrencyService', 'InvestmentStock' ]

}

We need to use Aggregation Framework to find the distribution of products field. Sort the result set by decreasing total number of products.

Expected output:

[

{ _id: 'InvestmentStock', total: 1746 },

{ _id: 'CurrencyService', total: 742 },

{ _id: 'Brokerage', total: 741 },

{ _id: 'InvestmentFund', total: 728 },

{ _id: 'Commodity', total: 720 },

{ _id: 'Derivatives', total: 706 }

]

Which pipeline should you use?

A : [{ $group: { _id: "$products", total: { $sum: 1 } }}]

B : [{ $unwind: { path: "$products" }}, { $group: { _id: "$products", total: { $sum: 1 } }}, { $sort: { total: -1 }}]

C : [{ $unwind: { path: "products" }}, { $group: { _id: "products", total: { $sum: 1 } }}, { $sort: { total: -1 }}]

D : [{ $group: { _id: "$products", total: { $sum: 1 } }}, { $sort: { total: -1 }}]

Answer: B