Question 1

You are tasked with developing a high-performance application using MongoDB as your primary database. The application heavily relies on queries that retrieve user profiles based on user names and their location. Given this requirement, you decide to implement a strategy to make these queries more efficient. What strategy should you implement to ensure these queries are covered queries?

Options :

A : Use the $project operator in the aggregation pipeline to include only the username and location fields in the result set.

B : Create separate indexes on the username and location fields and include only these fields in the queries.

C : Use the $match operator in the aggregation pipeline to filter documents based on username and location.

D : Create a compound index on the username and location fields and project only these two fields in the queries.

Answer: D

Question 2

You have a MongoDB collection named students, where each document represents a student. The documents include fields like name, age, and courses. The courses field is an array that lists the course codes each student is enrolled in. You want to find all students enrolled in the course with the code "CS101". Which query will correctly retrieve all students enrolled in "CS101"?

Options :

A : db.students.find({ "courses": { $elemMatch: { $eq: "CS101" } } })

B : db.students.find({ "courses": [ "CS101" ] })

C : db.students.find({ "courses": { $in: "CS101" } })

D : db.students.find({ "courses": "CS101" })

Answer: D

Question 3

Which of the following is the best practice for managing MongoDB data growth in a production environment (scale-out)?

Options :

A : Increasing the RAM size of the existing server.

B : Using replication.

C : Sharding the data across multiple servers.

D : Adding more disk space to the existing server.

Answer: C

Question 4

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:

Options :

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 5

Where is the MongoDB configuration file usually located?

Options :

A :

B :

C :

D :

E :

Answer: B

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