Question 1

A MongoDB data modeler is optimizing the performance of an application that frequently executes the following query:

db.orders.find(

{ status: "shipped", deliveryDate: { $lte: new Date("2023-12-31") } }

).sort({ deliveryDate: -1 })

The explain method reveals the following key details indicating the query is not efficiently utilizing indexes:

{

"queryPlanner": {

...

"winningPlan": {

"stage": "SORT",

"sortPattern": { "deliveryDate": -1 },

...

"inputStage": {

"stage": "FETCH",

"inputStage": {

"stage": "IXSCAN",

"keyPattern": { "status": 1 },

...

}

},

"executionStats": {

"executionSuccess": true,

"nReturned": 100,

"executionTimeMillis": 120,

"totalKeysExamined": 500,

"totalDocsExamined": 500,

...

},

...

}

What action should the data modeler take to enhance the performance of this query?

Options :

A : Create a compound index on status and deliveryDate in descending order to match the sort pattern.

B : Create an index on the deliveryDate field to optimize the sort operation.

C : Create a compound index on status and deliveryDate in ascending order.

D : Remove the sort operation to decrease the complexity of the query.

Answer: A

Question 2

In the design of a MongoDB database for an application that manages user profiles with varying degrees of completeness and privacy settings, what are the positives and negatives of relying on in-app enforcement versus schema validation to ensure data integrity and compliance with business requirements?

Options :

A : Schema validation ensures that all data writes adhere to the defined schema, reducing the need for additional application logic but lacks flexibility for handling complex conditional validation.

B : In-app enforcement is more performance-efficient because it avoids the overhead of database-level checks but can result in data integrity issues if the application crashes.

C : In-app enforcement allows for more complex logic and conditional validation but may lead to inconsistency if not implemented correctly across all application components.

D : Schema validation simplifies application code by offloading rules to the database but can lead to performance bottlenecks under heavy write loads.

Answer: C

Question 3

When optimizing a MongoDB collection's schema for a high-traffic application, what factor is crucial for determining the most effective document structure?

Options :

A : Modeling documents to reflect the application-s access patterns, prioritizing query performance and data retrieval efficiency.

B : Designing documents based on the visual appeal of the JSON structure in the database management interface.

C : Ensuring all documents in a collection have a uniform size by adding placeholder data where necessary.

D : Organizing fields in alphabetical order within documents to facilitate easier indexing.

Answer: A

Question 4

A database designer is structuring a database for a book publishing company using MongoDB. In this design, there are two entities: Author and Book. An Author can write multiple Books, but each Book can be written by only one Author. What is the relationship between Authors and Books?

Options :

A : Squillions-to-one

B : One-to-one

C : One-to-many

D : Many-to-one

E : Many-to-many

Answer: C

Question 5

A data modeler is tasked with designing a database for a movie streaming service. The database needs to efficiently store and retrieve information about movies, including the movie's title, director, genre(s), release year, and average viewer rating. How should the data modeler structure the Movie entity?

Options :

A : Model Movie as a movie-s title, director, genre(s), release year, and average viewer rating.

B : Model Movie as a movie-s title, director, and genre(s).

C : Model Movie as a movie-s title, director, genre(s), release year, average viewer rating, and the total number of viewers.

D : Model Movie as the movie-s title only.

Answer: A