Question 1

When designing a MongoDB schema for an application that tracks user activities and their corresponding locations, which approach best facilitates efficient data retrieval and updates?

Options :

A : Create a multidimensional array within each user document to store activities and their corresponding locations.

B : Store user activities and location data in separate documents within a single collection, linking them with unique identifiers.

C : Normalize all data, storing user activities in one collection and locations in another, without using any form of document references.

D : Embed user location data directly within each activity document to capitalize on document locality.

Answer: D

Question 2

Regarding MongoDB schema design and document structure, which two of the following statements are accurate?

Options :

A : All documents within a collection must have the same fields and data types.

B : Embedding documents is recommended for frequently accessed data that is read together.

C : Schema validation rules are enforced by default on all collections upon creation.

D : Denormalization can reduce the need for expensive joins and improve read performance.

Answer: B,D

Question 3

A MongoDB data modeler is designing a schema for a content management system (CMS) that stores articles, each with multiple tags and categories. The CMS will feature frequent searches by tags and categories, with more read operations than writes. The schema must optimize for query performance and scalability. What schema design pattern is most appropriate for this use case?

Options :

A : Use a separate collection for tags and categories, with references in the article documents.

B : Implement a Bucket Pattern, grouping articles by tags and categories in the same document.

C : Utilize the Polymorphic Pattern, allowing tags and categories to adapt to different types of articles.

D : Embed tags and categories directly within each article document.

Answer: D

Question 4

A MongoDB data modeler needs to optimize a collection named orders to efficiently query documents based on the status field, which can have values like 'pending', 'completed', and 'canceled'. The collection has millions of documents, and the queries should return results quickly even under heavy load. Which of the following approaches best achieves this requirement while maintaining the collection's scalability and performance?

Options :

A : Create a separate collection for each status value and distribute the documents accordingly, querying the relevant collection based on the status.

B : Index the status field using a single field index, ensuring queries filtering by 'status' are more efficient.

C : Convert the status field into an array and use a multi-key index, ensuring multiple status values can be queried simultaneously.

D : Use a hashed index on the status field to evenly distribute the documents across the collection.

Answer: B

Question 5

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

Free Associate-Data-Modeler Mock Exam – Practice Online Confidently

Buying Options: