Free MongoDB C100DEV Exam Questions

Question 1

As a MongoDB Developer, you are tasked with optimizing a MongoDB database. Your collection has documents with the following structure:

{

"_id": ObjectId(),

"user_id": Number,

"user_data": {

"name": String,

"email": String,

"phone": String,

"address": {

"city": String,

"state": String,

"country": String

}

},

"transactions": [

{

"transaction_id": Number,

"amount": Number,

"date": Date

}

]

}

The application frequently queries for the total transaction amount for users based on the city. Which index should you create to optimize the performance of these queries?

A : db.collection.createIndex({"user_data.address.city": 1})

B : db.collection.createIndex({"user_data.address.city": 1, "transactions.amount": 1})

C : db.collection.createIndex({"user_id": 1})

D : db.collection.createIndex({"transactions.amount": 1})

E : db.collection.createIndex({"transactions": 1})

Answer: B

Question 2

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 3

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

C : [{ $match: { "imdb.rating": { $ne: "" } }}, { $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: C

Question 4

You are a MongoDB Associate Developer and need to demonstrate how to load the Atlas Sample Dataset into your MongoDB Atlas cluster. Your manager has asked you to guide a junior developer through this process to ensure they can independently load the dataset. What steps should the junior developer follow to successfully load the Atlas Sample Dataset?

A : In MongoDB Atlas, navigate to the "Clusters" tab, click on the "Load Sample Dataset" button, and wait for the dataset to be fully loaded.

B : In MongoDB Compass, connect to your cluster, go to the "Import Data" tab, and import the sample dataset by uploading the sample-dataset.json file.

C : Use the MongoDB Atlas API to send a POST request to the /loadSampleDataset endpoint, specifying your cluster ID in the request body.

D : Open the MongoDB Shell, connect to your cluster, and run the command mongoimport --db sample --collection datasets --file sample-dataset.json.

Answer: A

Question 5

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

A : It doesn't matter when we apply $match stage.

B : MongoDB does not have a $match stage.

C : The later in the pipeline, the better.

D : 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.

Answer: D