Define and Implement an Indexing Strategy for Azure Cosmos DB for NoSQL DP-420 Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   An application stores IoT sensor readings in Azure Cosmos DB for NoSQL. The application primarily performs point reads by device ID and timestamp. Writes are infrequent. Which indexing decision best supports this read-heavy pattern?

   • AUse a hash index on the partition key and a range index on timestamp
   • BDisable indexing on the timestamp property to reduce write RU cost
   • CCreate a composite index on (deviceId, timestamp)
   • DSet the container indexing mode to None and use the device ID as the partition key
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2. 7foundation · easy

   A telemetry ingestion service writes high volumes of sensor data to Azure Cosmos DB for NoSQL. The data is archived and rarely queried, but the service occasionally needs to retrieve a single document by its unique identifier. Which indexing approach best optimizes this write-heavy workload?

   • AUse the default indexing policy and add a range index on the sensorId property
   • BDisable indexing on all properties except the partition key
   • CSet the indexing mode to None and rely on the partition key for reads
   • DCreate a custom indexing policy that includes only the id path with a hash index
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4. 8foundation · easy

   A query in Azure Cosmos DB for NoSQL filters on two properties: status and createdDate. The query uses an equality filter on status and a range filter on createdDate. Which composite index configuration would best support this query?

   • AComposite index on (createdDate, status)
   • BSpatial index on createdDate
   • CHash index on status and range index on createdDate separately
   • DComposite index on (status, createdDate)
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5. 9application · medium

   A media streaming company uses Azure Cosmos DB for NoSQL to store user viewing history. Each document has `userId`, `contentId`, `timestamp`, and `durationWatched`. The application runs frequent queries that filter on `userId` and `timestamp` to retrieve a user's viewing history for a specific time range. The team notices high RU consumption for these queries. What should you do to reduce RU consumption?

   • AAdd a hash index on `userId` and a range index on `timestamp`.
   • BRemove the index on `timestamp` and rely on the `userId` index only.
   • CAdd a range index on `userId` and a hash index on `timestamp`.
   • DAdd a composite index on (`userId` ASC, `timestamp` DESC).
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6. 10application · medium

   An e-commerce platform stores order documents in Azure Cosmos DB for NoSQL. The application frequently runs queries that filter orders by status (a string) and sort by orderDate (a string in ISO format). The team wants to optimize these queries while keeping the index size small. What should the indexing policy include?

   • AAdd a hash index on status and a range index on orderDate
   • BAdd a range index on status and a hash index on orderDate
   • CAdd a range index on both status and orderDate
   • DAdd a composite index on (status ASC, orderDate ASC)
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