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Question 1

Which of the following scenarios is best suited for applying the Attribute Pattern?

A : Product categories in the online store.

B : You need to apply document validation before inserting/modifying.

C : The documents in the collection are quite large.

D : Some fields share a similar characteristics, and you want to search across those fields.

Answer: D

Question 2

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 3

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

{

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

"order_number": "ORD-001",

"order_date": ISODate("2022-01-01T00:00:00.000Z"),

"customer": {

"name": "John Doe",

"email": "johndoe@example.com"

},

"items": [

{ "product_name": "Smartphone", "quantity": 2, "price": 800 },

{ "product_name": "Laptop", "quantity": 1, "price": 1500 }

],

"shipping_address": {

"street": "123 Main St",

"city": "New York",

"state": "NY",

"zipcode": "10001"

},

"total_amount": 3000

}

Select an aggregation pipeline that returns the total sales amount by state and month, for orders placed in the year 2022. The result should have the following format:

A :

[
   {
      "$match": {
         "order_date": {
            "$gte": ISODate("2022-01-01T00:00:00.000Z"),
            "$lt": ISODate("2023-01-01T00:00:00.000Z")
         }
      }
   },
   {
      "$group": {
         "_id": { "state": "$shipping_address.state", "month": { "$month": "$order_date" } },
         "sales_amount": { "$sum": "$total_amount" }
      }
   },
   {
      "$project": {
         "state": "$_id.state",
         "month": "$_id.month",
         "sales_amount": "$sales_amount",
         "_id": 0
      }
   }
]

B :

[
   {
      "$group": {
         "_id": { "state": "$shipping_address.state", "month": { "$month": "$order_date" } },
         "sales_amount": { "$sum": "$total_amount" }
      }
   },
   {
      "$project": {
         "state": "$_id.state",
         "month": "$_id.month",
         "sales_amount": "$sales_amount",
         "_id": 0
      }
   }
]

C :

[
   {
      "$match": {
         "order_date": {
            "$gte": ISODate("2022-01-01T00:00:00.000Z"),
            "$lt": ISODate("2023-01-01T00:00:00.000Z")
         }
      }
   },
   {
      "$group": {
         "sales_amount": { "$sum": "$total_amount" }
      }
   },
   {
      "$project": {
         "state": "$_id.state",
         "month": "$_id.month",
         "sales_amount": "$sales_amount",
         "_id": 0
      }
   }
]

D :

[
   {
      "$match": {
         "order_date": {
            "$gte": ISODate("2022-01-01T00:00:00.000Z"),
            "$lt": ISODate("2023-01-01T00:00:00.000Z")
         }
      }
   },
   {
      "$project": {
         "state": "$_id.state",
         "month": "$_id.month",
         "sales_amount": "$sales_amount",
         "_id": 0
      }
   }
]

Answer: A

Question 4

You are developing a MongoDB application where write performance is a critical requirement. Given a scenario where your application primarily performs insert operations and rarely performs updates or deletes, which of the following MongoDB features would best optimize this heavy-write workload?

A : Change Streams

B : Capped Collections

C : Aggregation Pipeline

D : Sharding

Answer: D

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

Name:	MongoDB Certified Developer Associate
Exam Code:	C100DEV
Certification:	MongoDB Certified Developer Associate
Vendor:	MongoDB
Total Questions:	411
Last Updated:	May 17, 2025

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