DRAG DROP (Drag and Drop is not supported)You are developing a solution for a hospital to support the following use cases: ● The most recent patient status details must be retrieved even if multiple users in different locations have updated the patient record. ● Patient health monitoring data retrieved must be the current version or the prior version. ● After a patient is discharged and all charges have been assessed, the patient billing record contains the final charges.You provision an Azure Cosmos DB for NoSQL database and set the default consistency level for the database account to Strong.You set the value for Indexing Mode to Consistent.You need to minimize latency and any impact to the availability of the solution. You must override the default consistency level at the query level to meet the required consistency guarantees for the scenarios.Which consistency levels should you implement? To answer, drag the appropriate consistency levels to the correct requirements. Each consistency level may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.Note: Each correct selection is worth one point.Select and Place:
Answer(s): A
Box 1: Strong Strong consistency must be used at the query level to meet this requirement.Linearizability Guarantee: Strong consistency ensures that a read operation always returns the most recent committed version of an item.Global Consensus: Because multiple users in different locations are updating the patient record, any reader across any region must wait for the updates to be fully committed globally before reading. This prevents any user from reading stale or out-of-order data, which is critical for patient status details.Box 2: Bounded Staleness To minimize latency and maximize availability while ensuring that retrieved patient health monitoring data is either the current version or the immediate prior version, you should override the consistency level at the query level to Bounded Staleness.Version Guarantee: Bounded Staleness ensures that reads are not older than a specific time window or a configured number of versions K versions behind the latest write). Setting the version staleness to 1 (K=1 ) guarantees that the data read is either the current version or the prior version.Lower Latency: Bounded Staleness offers significantly lower latency than the account's default Strong consistency. Strong consistency requires a global majority acknowledgment before a read or write completes, increasing latency.Higher Availability: By backing down from Strong consistency to Bounded Staleness for this query, you reduce the strict synchronization requirements, thereby minimizing impacts on availability during localized network disruptions.Box 3: Eventual Eventual consistency is the correct consistency level to use at the query level for this scenario.Maximizes Performance: Eventual consistency offers the lowest latency and highest availability among all consistency levels.Matches Scenario Needs: The billing record is final and no longer changing, so there is no risk of reading stale, in-progress updates.Optimizes Costs: Eventual consistency queries consume half the Request Units (RUs) compared to Strong or Bounded Staleness queries.
https://learn.microsoft.com/en-us/azure/cosmos-db/consistency-levels
HOTSPOT (Drag and Drop is not supported)You develop an application that sells AI generated images based on user input. You recently started a marketing campaign that displays unique ads every second day.Sales data is stored in Azure Cosmos DB with the date of each sale being stored in a property named 'whenFinished'.The marketing department requires a view that shows the number of sales for each unique ad.You need to implement the query for the view.How should you complete the query? To answer, select the appropriate options in the answer area.Note: Each correct selection is worth one point.Hot Area:
Box 1: count(c.whenFinished) For the number of sales we need to use the count function.Box 2: DateTimeBin(c.whenFinished, 'day', 2) Box 3: DateTimeBin(c.whenFinished, 'day', 2)
https://learn.microsoft.com/en-us/cosmos-db/query/datetimebin
DRAG DROP (Drag and Drop is not supported)You are implementing a Retrieval-Augmented Generation (RAG) system by using the native vector search capabilities of Azure Cosmos DB for NoSQL API.You have a container named Documents that stores technical articles. Each article includes a property named embedding.You must ensure that the system can perform efficient similarity searches between user queries and the stored articles.You need to configure the database resources to support semantic retrieval.Which configurations should you use? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.Note: Each correct selection is worth one point.Select and Place:
Box 1: Configure a vector index. To support semantic retrieval and perform mathematical distance calculations using the native vector search capabilities of Azure Cosmos DB for NoSQL, you must configure a vector index.Azure Cosmos DB for NoSQL relies on a specialized indexing structure (such as DiskANN or QuantizedFlat) to organize vector data. Defining a vector index policy within the container configurations is a strict requirement for enabling high-speed similarity searches (using functions like VectorDistance) without requiring full, unindexed scans of documents.Box 2: Store data as a numeric array Azure Cosmos DB for NoSQL, you must store the embedding property as a numeric array (an array of floating-point numbers like float32) inside your JSON documents.
https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/agentic-retrieval
DRAG DROP (Drag and Drop is not supported)A semantic search application queries Azure Database for PostgreSQL and stores document embeddings and metadata in a table with the following columns: ● embedding (pgvector) ● department ● created_atThe application must return the top five most similar documents for a given query embedding only from the finance department.You need to implement semantic retrieval with metadata filtering.Which query components should you select? To answer, move the appropriate query components to the correct requirements. You may use each query component once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.Note: Each correct selection is worth one point.Select and Place:
Box 1: WHERE department = 'Finance'Box 2: ORDER BY embedding <=> :query_vector LIMIT 5 To rank and return the top five most similar documents by similarity in Microsoft Learn, use the ORDER BY embedding <=> [query_vector] expression combined with LIMIT 5, where <=> computes the cosine distance.
https://learn.microsoft.com/en-us/azure/postgresql/extensions/how-to-use-pgvector
You develop an AI application that stores telemetry documents in Azure Cosmos DB for NoSQL.Each document contains 30 properties. Queries filter only on deviceId and timestamp.Write operations consume more RUs than expected.You need to reduce RU consumption for write operations while maintaining query performance.What should you do?
Modify the indexing policy to include only deviceId and timestamp paths, and exclude all other paths. By default, Azure Cosmos DB indexes every property in a document, which drastically inflates the Request Units (RUs) consumed during write operations.
https://www.datacamp.com/tutorial/azure-cosmos-db
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