Stay ahead with 100% Free Associate Data Modeler Associate-Data-Modeler Dumps Practice Questions
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?
To improve the performance of queries on a logEntries collection, a MongoDB developer needs to find and analyze slow queries that have been executed. Which command should be used to identify and provide details on slow operations for analysis?
When optimizing a MongoDB collection's schema for a high-traffic application, what factor is crucial for determining the most effective document structure?
In MongoDB, how should the analysis of query and analytics patterns influence the decision on indexing strategies, particularly with respect to the operational context of queries being part of a transaction?
A database administrator is tasked with designing a MongoDB schema for a social media application that includes features for user profiles, posts, and friend connections. The application requires efficient access to a user's profile information, their posts, and their list of friends. Considering the need for high performance in both read and write operations, which schema design approach is most appropriate?
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