Free MongoDB Associate-Data-Modeler Exam Questions

Question 1

A team is developing a logistics application using MongoDB to track shipments in real-time. Each shipment record updates frequently with GPS coordinates, and the system requires immediate access to the latest location data for efficient routing and delivery updates. The application also needs to maintain a compact record of each shipment's path without unnecessary data duplication. Which MongoDB feature or design pattern should be used to model the shipment tracking system?

A : The Bucket Pattern, grouping location updates by day for each shipment.

B : The Preallocation Pattern, by preallocating space for each shipment document to accommodate location updates.

C : The Document Versioning Pattern, where each update creates a new version of the shipment document.

D : A standard collection with an index on the lastUpdated field.

Answer: D

Question 2

In a blogging application, the main entity Article contains text, a title, and metadata. Each article can have multiple comments made by users. The application frequently retrieves an article along with all its comments for display. How should the developer optimize the schema for read performance?

A : Embed Comment documents directly within an Article document in an array of subdocuments.

B : Store each Comment in a separate collection and reference the Article ID in every Comment.

C : Use a third collection to manage relationships between Article and Comment documents with document references.

D : Duplicate the Article information in each Comment document to avoid joins.

Answer: A

Question 3

In the process of optimizing a MongoDB schema for a social media application that features user profiles, posts, and interactions (likes, comments), a data modeler is evaluating the use of embedded documents versus referencing. Considering the application experiences frequent reads and writes on user interactions, and the volume of interactions is high: What is the primary advantage of embedding user interactions directly within user documents instead of referencing them in separate collections?

A : Embedding interactions allows for more complex transactions across documents.

B : Embedding interactions facilitates more efficient reads by reducing the need for join operations.

C : Embedding interactions improves write efficiency by reducing the number of write operations.

D : Embedding interactions ensures that data cardinality remains low, improving indexing efficiency.

Answer: B

Question 4

In the context of a MongoDB database application for an online news platform, the data modeler is tasked with optimizing the storage and retrieval of news articles that are frequently accessed by readers. The articles undergo occasional updates by the editorial team and include metadata such as publication date, author, and a set of tags. The application has a high read-to-write ratio, with the need for efficient retrieval of articles based on tags and publication dates. What data modeling strategy should be adopted to maximize query performance for retrieving articles based on metadata?

A : Adopt the Bucket Pattern, grouping articles into buckets by publication month or year.

B : Apply the Reference Pattern, creating separate collections for articles and metadata, with references between them.

C : Employ the Indexing Pattern, creating indexes on tags and publication dates within the articles collection.

D : Implement the Embedded Documents Pattern, embedding metadata within each article document.

Answer: C

Question 5

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?

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