Integrate an Azure Cosmos DB solution — DP-420 Practice Quiz

Question 1

A retail company exposes its Azure Cosmos DB for NoSQL product catalog through an Azure AI Search index using an indexer. Products are frequently discontinued, and when they are, the corresponding document is physically deleted from the Cosmos DB container. Users complain that discontinued products still appear in search results even after the indexer runs on schedule. Which action should you take so the indexer removes deleted products from the search index?

Accepted Answer

Enable the indexer's soft-delete deletion detection policy and add a soft-delete marker field to documents instead of physically deleting them.

Answer

Enable the high water mark change detection policy using the _ts field on the Cosmos DB documents.

Answer

Reset and re-run the indexer on a more frequent schedule so it performs a full re-crawl of the container each time.

Answer

Configure a field mapping that maps the Cosmos DB id property to the search index key field.

Question 2

A retail company stores product documents in an Azure Cosmos DB for NoSQL container. The product catalog team wants to add a semantic product search feature to their website that returns products based on meaning rather than exact keyword matches. They plan to use Azure AI Search over the Cosmos DB data. The team already generates text embeddings for each product description. Which Azure AI Search capability should they configure to satisfy this semantic-similarity requirement?

Accepted Answer

Define a vector field in the index and use the Cosmos DB indexer with a field mapping to store the precomputed embeddings, then issue vector queries against that field.

Answer

Enable the built-in Cosmos DB analytical store and query it directly from Azure AI Search using T-SQL.

Answer

Configure a scoring profile that boosts documents based on the freshness of the product's last-modified timestamp.

Answer

Create a synonym map that lists related product terms so the search engine can expand keyword queries automatically.

Question 3

A retail analytics team enables Azure Synapse Link on a Cosmos DB for NoSQL container. The transactional data must be purged after 30 days to control storage costs, but the data science team needs to query up to 2 years of historical data through the analytical store via Synapse serverless SQL pools. How should the team configure retention to meet both requirements?

Accepted Answer

Set the container's transactional TTL to 30 days and set a separate analytical store TTL (AnalyticalStoreTimeToLiveInSeconds) to approximately 2 years.

Answer

Set the transactional TTL to 2 years so the analytical store retains the same data, then filter to 30 days in transactional queries.

Answer

Set the analytical store TTL to 30 days and rely on change feed to archive older records into blob storage for the 2-year requirement.

Answer

Disable TTL entirely because enabling Synapse Link automatically retains all analytical data for 2 years by default.

Question 4

A retail team stores a product catalog in an Azure Cosmos DB for NoSQL container. They want to expose full-text and faceted search over the catalog using Azure AI Search, and the index must automatically pick up new and modified products without re-crawling the entire container each run. Products are updated frequently throughout the day. What is the most appropriate way to configure the Azure AI Search indexer for this requirement?

Accepted Answer

Configure an Azure AI Search indexer with a change detection policy based on the container's _ts property so the indexer processes only documents changed since the last run.

Answer

Schedule the indexer to run a full reset and re-index of all documents on every execution to guarantee the index stays current.

Answer

Push documents into Azure AI Search directly from the application on every write instead of using an indexer.

Answer

Enable the analytical store on the container and point the Azure AI Search indexer at the analytical store for change tracking.

Question 5

An e-commerce platform stores individual order documents in a Cosmos DB container partitioned by /customerId. The analytics team needs a separate 'customerSummary' container that maintains a running total of each customer's lifetime spend and order count, updated within seconds of any new order. The solution must scale automatically and avoid expensive periodic full-container scans. Which approach best meets these requirements?

Accepted Answer

Deploy an Azure Function with a Cosmos DB trigger bound to the orders container's change feed that reads each changed order and upserts the aggregated totals into the customerSummary container.

Answer

Schedule a timer-triggered Azure Function that runs a cross-partition GROUP BY query over the entire orders container every minute and rewrites the customerSummary container.

Answer

Enable analytical store on the orders container and query it with Azure Synapse Link to compute the aggregates on demand for the summary.

Answer

Create a post-trigger on the orders container that recomputes each customer's totals by querying all their orders whenever a new order is inserted.

Question 6

An e-commerce platform stores each customer's order in an 'orders' container partitioned by /customerId. The team needs an Azure Function triggered by the Cosmos DB change feed that, for every new or updated order, must (1) write a denormalized summary document to a 'customerOrderHistory' container and (2) push a message to a Service Bus queue for downstream fulfillment. The function must process changes reliably and scale across partitions automatically. Which approach correctly implements this integration?

Accepted Answer

Use the Cosmos DB trigger with a lease container, and within the same function bind an output to the 'customerOrderHistory' container and a Service Bus output binding, so each change is fanned out to both destinations.

Answer

Use a Timer trigger that queries the orders container every minute for recently modified documents, then writes to both destinations, since the change feed cannot invoke multiple outputs.

Answer

Use the Cosmos DB trigger but disable the lease container to avoid duplicate processing, and write only to 'customerOrderHistory' because a single function cannot have more than one output binding.

Answer

Use an HTTP trigger invoked by a change-feed pull-model loop in a separate service, then use the SDK directly to write to both destinations because output bindings are not supported for Cosmos DB writes.

Question 7

Your team built an Azure Function with a Cosmos DB trigger to process the change feed of an 'Orders' container for real-time fraud scoring. During a marketing campaign, order volume increases tenfold. You observe that a single Function instance is processing all changes and cannot keep up, causing rising latency. The container has 20 physical partitions. What is the correct way to enable the Function to scale out and process changes in parallel across multiple instances?

Accepted Answer

Ensure a lease container is configured for the trigger; the Consumption plan will automatically scale out Function instances, distributing partition key ranges (leases) across them.

Answer

Increase the MaxItemsPerInvocation setting so a single instance reads larger batches, which removes the need for additional instances.

Answer

Switch the Cosmos DB account to multi-region writes so each region's Function app processes a subset of the change feed independently.

Answer

Manually create 20 separate Function apps, one per physical partition, and hard-code each to poll a specific partition key range.

Question 8

A retail company stores order documents in an Azure Cosmos DB for NoSQL container. Compliance requires that once an order document reaches a 'Completed' status, a copy must be moved to Azure Blob Storage for long-term archival, and the archived record must never be lost even if processing fails temporarily. The team wants a serverless, low-maintenance approach that automatically reacts to changes. Which design best meets these requirements?

Accepted Answer

Deploy an Azure Function with a Cosmos DB trigger (change feed) that writes completed orders to Blob Storage, and rely on the lease container and built-in checkpointing/retry to guarantee each change is processed.

Answer

Schedule an Azure Function on a timer trigger every 5 minutes to query the container for completed orders and write them to Blob Storage.

Answer

Enable analytical store with Azure Synapse Link and run a nightly Spark job that exports completed orders to Blob Storage.

Answer

Configure a post-trigger on the container that writes each completed order directly to Blob Storage during the write operation.

Question 9

An e-commerce platform stores Customers and Orders in separate Azure Cosmos DB for NoSQL containers. When a customer record is soft-deleted (a 'deleted' flag set to true), the business requires that all of that customer's orders be automatically marked as orphaned within seconds, without impacting the write latency of the customer-facing application. Which approach best satisfies this requirement?

Accepted Answer

Deploy an Azure Function with a Cosmos DB trigger on the Customers container that reacts to change feed events and updates the related orders in the Orders container.

Answer

Add a post-trigger to the Customers container that queries and updates all matching order documents in the Orders container within the same operation.

Answer

Enable Azure Synapse Link on the Customers container and run a nightly Spark job to reconcile orphaned orders in the analytical store.

Answer

Configure a stored procedure on the Customers container that transactionally updates both the customer and all their orders in a single batch.

Question 10

A retail company streams order events from an Azure Cosmos DB container into Azure Event Hubs using an Azure Function bound to the change feed. The analytics team wants every raw event automatically persisted to an Azure Data Lake Storage Gen2 account in Avro format for long-term compliance archiving, without writing or maintaining any additional consumer code. What should you configure?

Accepted Answer

Enable Event Hubs Capture on the event hub and point it at the Data Lake Storage Gen2 account

Answer

Add a second Azure Function triggered by Event Hubs that writes each event to Data Lake Storage

Answer

Enable the Cosmos DB analytical store and connect Microsoft Fabric mirroring to the lake

Answer

Configure a Kafka Connect sink connector to move events from Event Hubs to Data Lake Storage

Question 11

A retail company stores order documents in an Azure Cosmos DB for NoSQL container. The architecture team wants every new or updated order to be streamed to multiple independent downstream systems: a real-time fraud-detection service, a Spark streaming job, and a long-term data lake archive. Each consumer must process the same stream independently at its own pace, and the solution should require minimal custom polling code against Cosmos DB. Which approach best meets these requirements?

Accepted Answer

Use an Azure Function with a Cosmos DB trigger (change feed) that publishes each changed document to an Azure Event Hub, then let each downstream system consume via its own Event Hubs consumer group.

Answer

Have each downstream system directly query the Cosmos DB container on a schedule using the API for NoSQL and filter by a lastModified timestamp.

Answer

Enable analytical store with Azure Synapse Link and point all three downstream systems at the analytical store.

Answer

Configure a single Azure Function change feed processor that writes directly to the data lake, and have the other two systems read the archived files.

Question 12

Your company runs an operational Azure Cosmos DB for NoSQL account that backs a retail order-processing app. The BI team wants to run Spark and T-SQL queries over the operational data in Microsoft Fabric with near real-time freshness, without provisioning analytical throughput, writing ETL pipelines, or affecting the transactional workload's RU consumption. They also want the replicated data stored in OneLake in an open Delta/Parquet format so it can be reused by other Fabric engines. Which approach best meets these requirements?

Accepted Answer

Configure Fabric mirroring for the Cosmos DB account, which continuously replicates the data into OneLake as Delta tables at no RU cost to the transactional workload.

Answer

Enable Azure Synapse Link analytical store on the container and connect a Synapse Spark pool, then copy results into Fabric with a scheduled pipeline.

Answer

Build an Azure Function triggered by the change feed that writes each change into a Fabric Lakehouse table via the REST API.

Answer

Use Azure Data Factory to run hourly Copy activities from Cosmos DB into a Fabric Lakehouse in Parquet format.

Question 13

Your team is starting a brand-new analytics initiative on top of an existing Azure Cosmos DB for NoSQL account. The goal is to run large-scale analytical queries and build Power BI reports over operational data without impacting transactional RU consumption, and without building or maintaining ETL pipelines. Following current Microsoft recommendations for new analytics projects, which approach should you choose?

Accepted Answer

Mirror the Cosmos DB database into Microsoft Fabric, then query the mirrored data through the Fabric OneLake SQL analytics endpoint

Answer

Enable Azure Synapse Link on the containers and query the analytical store from a Synapse serverless SQL pool

Answer

Provision a dedicated SQL pool in Synapse and run scheduled Azure Data Factory copy jobs from the transactional store

Answer

Read the change feed with an Azure Function and write each document into an Azure SQL Database for reporting

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