Microsoft Azure Cosmos DB Developer Specialty (DP-420)

Question 1

A retail company runs an Azure Cosmos DB account with a single write region in East US. Their customer base has expanded significantly to Western Europe, and European users are reporting slow write latency when submitting orders, while reads remain acceptable. The team wants to reduce write latency for European customers with minimal application changes and without sacrificing the ability to resolve conflicting concurrent writes. Which configuration change should they make?

Accepted Answer

Enable multi-region writes (multi-region writes) and add West Europe as an additional write region.

Answer

Add West Europe as a read region only and enable automatic failover with West Europe as the highest failover priority.

Answer

Change the consistency level from session to eventual to reduce write acknowledgment time.

Answer

Perform a manual failover to make West Europe the single write region instead of East US.

Question 2

You maintain a Cosmos DB container named 'orders' partitioned by /customerId. A reporting dashboard runs the query SELECT VALUE MAX(c.orderTotal) FROM c to display the single largest order value across all customers. The dashboard runs this query once every few seconds and RU consumption has become a concern. Which approach reduces the RU cost of repeatedly obtaining this value most effectively?

Accepted Answer

Maintain a separate 'stats' item updated via the change feed that stores the running maximum orderTotal, and issue a point read against it from the dashboard.

Answer

Add an ORDER BY c.orderTotal DESC clause with a TOP 1 to the existing query so the engine can use the index more efficiently.

Answer

Enable autoscale on the container so the query automatically gets more RU/s when the dashboard runs.

Answer

Switch the SDK from gateway mode to direct mode so the aggregate query bypasses cross-partition fan-out.

Question 3

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 4

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 5

A healthcare application stores patient records in Azure Cosmos DB for NoSQL. Compliance requires that the 'nationalId' property be encrypted client-side so that plaintext is never visible to the database engine. Developers must still be able to run queries that filter documents using an exact-match equality predicate on 'nationalId'. When configuring Always Encrypted for this property, which encryption type must you select?

Accepted Answer

Deterministic encryption

Answer

Randomized encryption

Answer

Service-managed key encryption at rest

Answer

Transparent Data Encryption (TDE) with a customer-managed key

Question 6

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 7

You maintain a Cosmos DB container of blog posts where each document has a 'tags' property that is an array of strings, for example ["azure", "cosmosdb", "nosql"]. Your application must return all posts that include the tag "azure" anywhere in the array. You want a single SQL query expression that correctly filters these documents. Which WHERE clause should you use?

Accepted Answer

WHERE ARRAY_CONTAINS(c.tags, "azure")

Answer

WHERE c.tags = "azure"

Answer

WHERE ARRAY_LENGTH(c.tags) > 0 AND c.tags[0] = "azure"

Answer

WHERE CONTAINS(c.tags, "azure")

Question 8

A financial services company runs a single-write-region Cosmos DB account with the write region in East US and read regions in West US and North Europe. They have enabled service-managed (automatic) failover and configured failover priorities as: 0 = East US, 1 = North Europe, 2 = West US. During a regional outage that makes East US unavailable, what happens to the write region?

Accepted Answer

Cosmos DB automatically promotes North Europe to the new write region based on the configured failover priority.

Answer

Writes are unavailable until an administrator manually triggers a failover to a healthy region.

Answer

Cosmos DB automatically promotes West US to the new write region because it has the lowest network latency to clients.

Answer

The account is converted to a multi-region write account so all remaining regions accept writes.

Question 9

You manage a Cosmos DB container for an e-commerce catalog. Traffic is highly variable: it sits near 800 RU/s for most of the day but spikes to roughly 9,000 RU/s for two to three unpredictable hours during flash sales. The team wants to avoid throttling during spikes while minimizing cost during idle periods, and they do not want to manually adjust throughput or write scaling code. Which throughput configuration should you choose?

Accepted Answer

Configure autoscale throughput with a maximum of 10,000 RU/s so the container scales between 1,000 and 10,000 RU/s automatically.

Answer

Provision standard (manual) throughput at 9,000 RU/s to guarantee capacity for the spikes.

Answer

Use serverless mode so you only pay per request consumed with no capacity planning.

Answer

Provision standard (manual) throughput at 1,000 RU/s and rely on burst capacity to absorb the flash-sale spikes.

Question 10

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 11

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 12

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 13

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 14

You operate a production Azure Cosmos DB for NoSQL account with a single container using autoscale throughput. Users intermittently report HTTP 429 errors during peak hours. You want to be proactively notified the moment the underlying cause—a physical partition consistently approaching its throughput ceiling—begins to occur, before users experience widespread throttling. Which Azure Monitor metric should you build the alert rule on to detect this specific condition?

Accepted Answer

Normalized RU Consumption, alerting when it approaches 100%

Answer

Total Request Units, alerting when the sum exceeds the provisioned maximum

Answer

Server Side Latency, alerting when average latency exceeds 10 ms

Answer

Total Requests, alerting when the count of 200 status codes drops

Question 15

An e-commerce team needs to ingest 500,000 order documents nightly into a Cosmos DB container partitioned by /customerId. The documents span thousands of different customerId values, and each document is independent — partial failures are acceptable and should simply be retried. The team wants maximum ingestion throughput with the least code complexity. Which approach should they use?

Accepted Answer

Enable bulk execution (AllowBulkExecution = true) in the SDK and issue concurrent CreateItemAsync calls

Answer

Group the documents and use TransactionalBatch operations across the different customerId values

Answer

Wrap all inserts in a single stored procedure executed once per night

Answer

Insert documents sequentially with individual CreateItemAsync calls to preserve ordering

Question 16

A financial services company runs a globally distributed Cosmos DB account with a single write region in East US and read regions in West Europe and Southeast Asia. Compliance requires that reads never lag the write region by more than a defined number of write operations OR a defined time interval, whichever comes first, so auditors can bound the maximum data divergence. Which consistency level satisfies this requirement, and what must the team configure?

Accepted Answer

Bounded staleness, configuring the maximum lag as both a number of versions (K) and a time interval

Answer

Session consistency, configuring a session token TTL to bound divergence

Answer

Consistent prefix, configuring a maximum prefix length per region

Answer

Strong consistency, configuring the acceptable replication window in milliseconds

Question 17

A developer is writing a .NET application to migrate 50 million JSON documents into an Azure Cosmos DB for NoSQL container that has been provisioned with autoscale up to 40,000 RU/s. The migration runs from an Azure VM in the same region as the Cosmos DB account and must complete as quickly as possible while maximizing throughput utilization. Which combination of SDK configuration settings should the developer use?

Accepted Answer

Enable Bulk mode by setting AllowBulkExecution = true on CosmosClientOptions, and use Direct connection mode.

Answer

Use Gateway connection mode with AllowBulkExecution = true to reduce the number of open TCP connections.

Answer

Wrap all inserts in a single stored procedure and call it repeatedly to guarantee transactional consistency across the import.

Answer

Issue individual CreateItemAsync calls sequentially with Direct connection mode and rely on automatic retries for throttled requests.

Question 18

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 19

Your team runs an Azure Functions app with a Cosmos DB trigger that processes the change feed from an 'Orders' container. During a recent sales event, downstream systems reported that order-processing events were arriving several minutes late. You need to programmatically measure how far behind the change feed processor is falling so you can trigger alerts and scale the function before the backlog grows. Which approach should you implement?

Accepted Answer

Use the change feed estimator to report the number of remaining (unprocessed) items per lease, and emit that value as a custom metric.

Answer

Query the lease container directly and count the number of lease documents to determine processing lag.

Answer

Enable the integrated cache on a dedicated gateway so change feed reads are served faster and lag is eliminated.

Answer

Increase the container's provisioned throughput (RU/s) until the reported 429 rate reaches zero.

Question 20

You are building an audit-and-replication pipeline on a Cosmos DB for NoSQL container. The downstream system must observe every intermediate change to each item — including updates that happened between reads — and must also be notified when items are deleted (so it can remove them from a search index). You will consume the change feed using the .NET SDK with the change feed processor. Which change feed configuration meets these requirements?

Accepted Answer

Use the change feed in all-versions-and-deletes mode, which surfaces intermediate updates and delete operations within the configured retention window.

Answer

Use the change feed in latest-version (default) mode, which delivers only the most recent version of each changed item and includes deletes.

Answer

Enable Time to Live (TTL) on the container so that deletes are emitted, then read the latest-version change feed for updates.

Answer

Query the container with SELECT * ORDER BY _ts on a timer, since the change feed cannot report deletes under any mode.

Question 21

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 22

You built a change feed consumer using the .NET SDK's change feed processor to project order events into a downstream reporting container. To maximize throughput, you configured the processor to checkpoint only after every 500 processed changes rather than after each batch. During a load test, the host process crashed unexpectedly. When the processor restarts, what behavior should you expect, and what is the primary trade-off of your checkpoint configuration?

Accepted Answer

The processor resumes from the last checkpoint, so up to 500 already-processed changes may be delivered again, requiring your delegate to handle duplicates idempotently.

Answer

The processor resumes exactly where it stopped with no reprocessing, because the SDK persists an in-memory offset independent of the lease container.

Answer

The processor skips all changes that occurred while the host was down, causing permanent data loss in the reporting container.

Answer

The processor automatically re-reads the entire change feed from the beginning because a crash invalidates all lease documents.

Question 23

You are building a .NET consumer using the change feed processor from the Cosmos DB SDK to process an order-events container. During peak hours, a single processor host cannot keep up, and the change feed estimator reports growing lag. Your monitored container has 6 physical partitions. You deploy 3 additional identical instances of the same application (4 total), all configured with the same processor name, the same lease container, and the same instance-specific 'instanceName'... except two instances were accidentally deployed with an identical 'instanceName'. What is the effect on lease (partition) distribution and processing?

Accepted Answer

The change feed processor uses instanceName to uniquely identify each host; two hosts sharing the same instanceName can cause leases to be reassigned back and forth between them, leading to duplicate processing and unstable distribution.

Answer

instanceName is ignored by the processor; leases are always split evenly by processor name alone, so all 4 instances process correctly regardless of duplicate names.

Answer

The duplicate instanceName causes both affected hosts to be rejected at startup, so only 2 of the 4 instances will acquire leases and process changes.

Answer

Because there are 6 partitions and 4 instances, the processor automatically renames duplicate hosts using a GUID suffix, so distribution is unaffected.

Question 24

An e-commerce team stores orders in a Cosmos DB container partitioned by /customerId. A separate reporting service needs a continuously updated materialized view aggregating daily sales totals per product into a second container partitioned by /productId. The team wants a low-latency, resilient mechanism that reacts to each new or updated order without polling or scheduled full scans. Which approach should they implement?

Accepted Answer

Use the Azure Cosmos DB change feed (via the change feed processor or an Azure Function trigger) to read order changes and write aggregates to the product container.

Answer

Create a post-trigger on the orders container that writes to the product container within the same operation.

Answer

Schedule a nightly cross-partition query with the SDK to scan all orders and rebuild the product aggregates.

Answer

Enable a stored procedure that runs on a timer to aggregate orders and upsert into the product container.

Question 25

A logistics company stores shipment documents in a Cosmos DB for NoSQL container. A dashboard runs a frequent query that filters on `WHERE c.warehouseId = @id` and returns results ordered by two fields: `ORDER BY c.status ASC, c.createdDate DESC`. The query currently fails at runtime with an error stating that an ORDER BY query requires a corresponding composite index. Which change to the indexing policy will allow this query to execute?

Accepted Answer

Add a composite index defined as [{ path: "/status", order: "ascending" }, { path: "/createdDate", order: "descending" }]

Answer

Add two separate range indexes, one on /status and one on /createdDate, since the default indexing policy already indexes every property

Answer

Add a composite index defined as [{ path: "/createdDate", order: "descending" }, { path: "/status", order: "ascending" }]

Answer

Add a spatial index on /status and /createdDate to support multi-field ordering

Question 26

A retail analytics team runs a frequent query against a Cosmos DB container that returns the newest, highest-value orders: SELECT * FROM c WHERE c.status = 'shipped' ORDER BY c.orderDate DESC, c.totalAmount DESC. The query currently fails at runtime with an error indicating the ORDER BY clause cannot be served. The container uses the default indexing policy. What is the most appropriate change to make this multi-property ORDER BY query succeed with minimal RU overhead?

Accepted Answer

Add a composite index on (orderDate DESC, totalAmount DESC) to the indexing policy.

Answer

Add range indexes on both /orderDate/? and /totalAmount/? individually as included paths.

Answer

Enable the wildcard included path /* so all properties are indexed for sorting.

Answer

Switch the container's indexing mode to lazy so ORDER BY across multiple properties is supported.

Question 27

A social media application writes a sequence of user actions to a single-region Cosmos DB account: a user posts a comment (write 1), then edits it (write 2), then deletes it (write 3). The team wants readers in the same region to never see these writes out of order (for example, seeing the delete before the edit), but they want the lowest possible read latency and RU cost that still guarantees ordered reads. They do NOT require reads to always return the most recent write. Which consistency level should they configure?

Accepted Answer

Consistent prefix

Answer

Strong

Answer

Eventual

Answer

Bounded staleness

Question 28

An e-commerce reporting service queries a Cosmos DB container with millions of order documents. The query returns thousands of matching results, but the frontend displays only 50 items per page. Currently the code executes the query, materializes the entire result set into memory, and slices out 50 items per request — consuming large amounts of RUs and memory on every page load. You must minimize RU cost and memory usage while preserving the ability to fetch subsequent pages. What should you do?

Accepted Answer

Set MaxItemCount to 50 on the query request options and use the continuation token returned by each FeedResponse to fetch the next page.

Answer

Add an OFFSET 50 LIMIT 50 clause that increases the offset for each page and re-run the full query per page.

Answer

Enable the integrated cache so the full result set is served from the dedicated gateway on every page request.

Answer

Create a composite index on the queried properties so the entire result set is returned with fewer RUs.

Question 29

A security team wants operations engineers to be able to create and modify Azure Cosmos DB accounts, adjust throughput settings, and manage failover priorities through the Azure portal and ARM, but they must be prevented from reading or writing any document data inside containers. Which approach satisfies these requirements with the least privilege?

Accepted Answer

Assign the engineers a control-plane Azure RBAC role such as Cosmos DB Operator, and do not grant any Cosmos DB data-plane role assignments.

Answer

Assign the engineers the Cosmos DB Built-in Data Contributor data-plane role scoped to the account.

Answer

Distribute the account's primary read-write key to the engineers so they can manage the account.

Answer

Assign the engineers the Owner role on the resource group containing the Cosmos DB account.

Question 30

A retail catalog container stores product documents, each with a nested 'reviews' array containing objects like { rating, verified, text }. A developer needs a query that returns each product's id and title along with only the count of reviews that have a rating of 5 AND verified = true. The team wants to minimize RU charge by avoiding returning the full reviews array to the client and by evaluating the filter server-side. Which SQL query approach best meets these requirements?

Accepted Answer

Use a correlated subquery in the SELECT: SELECT p.id, p.title, (SELECT VALUE COUNT(1) FROM r IN p.reviews WHERE r.rating = 5 AND r.verified = true) AS topReviews FROM p

Answer

SELECT p.id, p.title, p.reviews FROM p, then filter and count the reviews in application code after retrieval

Answer

SELECT p.id, p.title, COUNT(r) FROM p JOIN r IN p.reviews WHERE r.rating = 5 AND r.verified = true GROUP BY p.id, p.title, with no subquery

Answer

SELECT p.id, p.title, ARRAY_LENGTH(p.reviews) AS topReviews FROM p WHERE ARRAY_CONTAINS(p.reviews, { "rating": 5, "verified": true }, true)

Question 31

You maintain a Cosmos DB for NoSQL container named 'Orders' partitioned on '/customerId'. A reporting dashboard runs the query: SELECT * FROM c WHERE c.status = 'Shipped' AND c.region = 'EU'. Neither 'status' nor 'region' is the partition key, and the container spans 40 physical partitions. Users complain the query consumes excessive RUs. Which explanation and remediation is most accurate?

Accepted Answer

The query is a cross-partition (fan-out) query that must be evaluated on all physical partitions; adding a composite index on (status, region) or restructuring to include customerId in the filter reduces the cost.

Answer

The query is a point read that is inefficient because status is not indexed; enabling the default indexing policy will convert it to a single-partition lookup.

Answer

The high cost is caused by the gateway connection mode; switching to direct mode eliminates the fan-out entirely.

Answer

The query already targets a single partition because status equals a constant; the RU cost is unrelated to partitioning and can only be lowered by increasing provisioned throughput.

Question 32

Your company uses customer-managed keys (CMK) stored in Azure Key Vault to encrypt an Azure Cosmos DB account. During a security incident review, an administrator accidentally revokes the key permissions granted to the Cosmos DB account's managed identity in the Key Vault access policy. What is the immediate impact on the Cosmos DB account, and what is the correct remediation?

Accepted Answer

The account becomes unavailable for data operations, and you must restore access by re-granting the Get, Unwrap Key, and Wrap Key permissions to the account's managed identity in Key Vault.

Answer

The account continues to function normally because Cosmos DB caches the encryption key indefinitely; no action is required.

Answer

The account is permanently corrupted and must be deleted and recreated from a periodic backup.

Answer

Only write operations fail while reads continue, and you must rotate to a new customer-managed key to restore write access.

Question 33

Your company's compliance team requires that all encryption keys for an Azure Cosmos DB account be managed and rotated by your organization rather than by Microsoft. You plan to configure the account to use customer-managed keys (CMK) stored in Azure Key Vault. Which combination of settings is REQUIRED on the Key Vault so that Cosmos DB can use the key and so that accidental deletion does not permanently lock you out of your data?

Accepted Answer

Enable soft delete and purge protection on the Key Vault, and grant the Cosmos DB identity Get, Unwrap Key, and Wrap Key permissions.

Answer

Enable only soft delete on the Key Vault, and grant the Cosmos DB identity full administrator access to all keys, secrets, and certificates.

Answer

Disable soft delete but enable purge protection, and grant the Cosmos DB identity Sign and Verify permissions on the key.

Answer

Enable purge protection only, and grant the Cosmos DB identity Decrypt and Encrypt permissions at the secret scope.

Question 34

A financial services company requires that all access to a Cosmos DB for NoSQL account be auditable and that primary/secondary account keys never be used by applications reading and writing documents. Developers must authenticate using Microsoft Entra ID identities, and each application's ability to read or write items must be governed by role assignments. Which approach satisfies these requirements for data-plane operations?

Accepted Answer

Assign the built-in 'Cosmos DB Built-in Data Contributor' role to the application's Entra ID identity using the Cosmos DB native RBAC data-plane role definitions.

Answer

Assign the Azure control-plane role 'DocumentDB Account Contributor' to the application identity so it can read and write items.

Answer

Store the primary key in Azure Key Vault and grant the application a Key Vault access policy so it can retrieve the key at runtime.

Answer

Enable customer-managed keys (CMK) with Key Vault so all item reads and writes are authorized through Entra ID.

Question 35

You design a Cosmos DB for NoSQL solution for an e-commerce site. Product documents are stored in a 'products' container, and customer reviews are stored in a separate 'reviews' container partitioned by /productId. The most common query on the reviews page displays each review along with the product's current display name. Product names are edited rarely (a few times per year), but review pages are read millions of times per day. Which data modeling approach minimizes RU cost for the high-volume read?

Accepted Answer

Denormalize (embed a copy of) the product display name into each review document, and update the copies with a background process when a product name changes.

Answer

Store only productId in each review and perform a separate point read against the products container for each review displayed.

Answer

Use a cross-partition JOIN query across the products and reviews containers to combine the data at read time.

Answer

Move reviews into the products container as an embedded array so every review is retrieved with the product.

Question 36

A developer reports that an application writing to an Azure Cosmos DB for NoSQL container intermittently fails. Inspecting the SDK diagnostics, you notice the failed requests return HTTP status code 503 (Service Unavailable), not 429. The container uses autoscale throughput and the Normalized RU Consumption metric peaks at only 62%. What is the MOST likely cause of these failures, and the appropriate action?

Accepted Answer

Transient connectivity or service-side unavailability is occurring; ensure the SDK's built-in retry policy is enabled and implement retry logic for 503 responses.

Answer

The account is exceeding provisioned RU/s; increase the maximum autoscale throughput to eliminate the errors.

Answer

The partition key is causing a hot partition; redesign the partition key to distribute writes more evenly.

Answer

The requests violate the consistency level; lower the consistency from Strong to Session to allow the writes to succeed.

Question 37

Your team needs to investigate individual data-plane requests against an Azure Cosmos DB for NoSQL account — including the RU charge, status code, partition key range, and request duration for each operation over the past week. Metrics in Azure Monitor only show aggregated values and are insufficient for this per-request analysis. What must you configure to enable this level of investigation?

Accepted Answer

Create diagnostic settings on the Cosmos DB account to send the DataPlaneRequests log category to a Log Analytics workspace, then query it with KQL.

Answer

Enable the Azure Cosmos DB integrated cache on a dedicated gateway to capture per-request telemetry.

Answer

Increase the metrics retention period in Azure Monitor Metrics to 30 days and filter by the RequestCharge dimension.

Answer

Turn on continuous backup mode so that point-in-time request history is retained for auditing.

Question 38

Your operations team must retain Azure Cosmos DB data-plane request logs for seven years to satisfy a compliance audit, while also sending a 90-day copy to a Log Analytics workspace for interactive KQL queries. Costs must be minimized for the long-term retention copy, which is queried only rarely. Which configuration should you implement?

Accepted Answer

Create one diagnostic setting that sends DataPlaneRequests to a Log Analytics workspace and, in the same setting, also archives the logs to an Azure Storage account for long-term retention.

Answer

Create a single diagnostic setting that streams DataPlaneRequests to Log Analytics only, then rely on the workspace's data retention policy set to 2555 days.

Answer

Enable continuous backup on the account and use point-in-time restore to recover the request logs when the auditor requests them.

Answer

Query the Normalized RU Consumption metric in Azure Monitor Metrics and export the chart to CSV every 90 days for archival.

Question 39

You maintain an Azure Cosmos DB for NoSQL container storing customer orders, partitioned by /customerId. Each order document contains an array named items, where each element has a productId and quantity. A reporting service must return, for a single specified customerId, the number of distinct products that customer has ever ordered across all their order documents. You want the most RU-efficient query that returns a single scalar value. Which query approach should you use?

Accepted Answer

SELECT VALUE COUNT(1) FROM (SELECT DISTINCT i.productId FROM c JOIN i IN c.items WHERE c.customerId = 'C123') AS d

Answer

SELECT c.items FROM c WHERE c.customerId = 'C123', then count distinct productIds in application code after loading all documents

Answer

SELECT DISTINCT i.productId FROM c JOIN i IN c.items WHERE c.customerId = 'C123' and read the returned row count from the SDK

Answer

SELECT COUNT(DISTINCT i.productId) FROM c JOIN i IN c.items WHERE c.customerId = 'C123'

Question 40

You are designing a Cosmos DB data model for an e-commerce platform. Each order contains between 1 and 30 line items. Line items are always read together with the order, are never queried independently, and are never updated after the order is placed. Orders are read frequently and the entire order document averages 8 KB. Which data modeling approach best fits this access pattern?

Accepted Answer

Embed the line items as an array inside the order document.

Answer

Store each line item as a separate document referencing the order by orderId.

Answer

Create a separate container for line items and join it to orders at query time.

Answer

Store line items in a normalized relational-style structure with foreign keys across three containers.

Question 41

A development team runs an ASP.NET Core web app on Azure App Service that connects to Azure Cosmos DB. Security auditors flag that the account's primary key is stored in an App Service application setting. The team must remove all shared keys from configuration and authenticate using Microsoft Entra ID, while granting the app only the ability to read and write items in a single container (no control-plane management rights). What should the team implement?

Accepted Answer

Assign a system-assigned managed identity to the App Service, then use the Cosmos DB data plane RBAC to assign a built-in or custom role scoped to the container, and connect the SDK with TokenCredential.

Answer

Assign the built-in Azure RBAC 'Cosmos DB Account Reader' control-plane role to the App Service managed identity and connect using DefaultAzureCredential.

Answer

Rotate to the secondary key and store it in Azure Key Vault, referencing it from App Service configuration with a Key Vault reference.

Answer

Enable Always Encrypted with a customer-managed key so the SDK no longer needs the account key to read data.

Question 42

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 43

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 44

A global gaming leaderboard application uses Azure Cosmos DB with read regions in North America, Europe, and Asia. The product team prioritizes the absolute lowest read latency, highest read availability, and the lowest read RU charge. They explicitly state that occasionally showing slightly out-of-order or stale scores to players is completely acceptable. Which consistency level should you configure to best meet these requirements?

Accepted Answer

Eventual

Answer

Consistent prefix

Answer

Session

Answer

Bounded staleness

Question 45

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 46

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

Question 47

A startup is building a proof-of-concept application and wants to minimize Azure Cosmos DB costs during initial development. The team decides to enable the free tier when creating a new Azure Cosmos DB for NoSQL account. Their expected steady-state workload needs about 800 RU/s of provisioned throughput and 20 GB of storage. Which statement correctly describes what the free tier provides and the impact on this workload?

Accepted Answer

The free tier provides the first 1000 RU/s and 25 GB of storage free per account, so this workload runs entirely without throughput or storage charges.

Answer

The free tier only applies to serverless accounts, so the team must switch to serverless to receive any discount.

Answer

The free tier can be enabled on multiple accounts per Azure subscription, allowing the team to spread the workload across accounts to stay free.

Answer

The free tier waives request charges but still bills for all provisioned storage, so the 20 GB will incur storage costs.

Question 48

You are developing a .NET application that uses the Azure Cosmos DB SDK to perform high-volume point reads and writes with the lowest possible latency. The application runs inside a corporate network where outbound traffic is restricted by a strict firewall that only permits HTTPS traffic on port 443. During load testing in this environment you notice that the SDK cannot establish connections when configured for the fastest connectivity mode. Which change should you make to allow the SDK to connect through the firewall while understanding the trade-off involved?

Accepted Answer

Switch the SDK to Gateway mode, accepting slightly higher latency because all requests route through the gateway over port 443.

Answer

Keep Direct mode and ask the network team to open the TCP port range 10000–20000 for the account.

Answer

Switch to Gateway mode, which uses direct TCP connections to replica nodes and provides lower latency than Direct mode.

Answer

Enable bulk execution mode in the SDK, which automatically bypasses the firewall by tunneling over port 443.

Question 49

You maintain a Cosmos DB container for an e-commerce platform that stores orders, partitioned by /customerId. A reporting feature must return, for a single specified customer, the total amount spent per month. The query currently uses GROUP BY on month and executes across all partitions, consuming more RUs than expected. Which change will most effectively reduce the RU cost of this specific query?

Accepted Answer

Add a WHERE clause filtering on customerId so the query targets a single logical partition.

Answer

Switch the container's partition key to /month to co-locate all orders by month.

Answer

Move the GROUP BY logic into a stored procedure so aggregation runs server-side.

Answer

Enable autoscale throughput so the query has more RUs available during peak reporting.

Question 50

A gaming company stores telemetry in a Cosmos DB container partitioned by /gameId. One extremely popular game generates over 40 GB of telemetry for its single gameId value, and writes for that game are now failing. The team wants to keep gameId as the primary way to group data while resolving the storage ceiling. Which redesign best addresses the problem?

Accepted Answer

Recreate the container with a hierarchical partition key of /gameId then /sessionId, distributing each game's data across many logical partitions.

Answer

Increase the provisioned throughput (RU/s) on the container so the large partition can accept more writes.

Answer

Enable autoscale on the container to automatically expand the storage limit of the logical partition.

Answer

Switch the account to serverless mode, which removes the 20 GB per-logical-partition storage limit.

Question 51

You are designing a Cosmos DB container for a logistics platform that stores shipment events. Each shipment belongs to a specific carrier, and there are only 8 carriers. Within a carrier, events are queried almost exclusively by shipmentId. The largest carrier will generate roughly 400 GB of event data over the container's lifetime. Most read queries filter by both carrierId and shipmentId, while a nightly report scans all events for a single carrier. Which partitioning strategy best balances query efficiency and avoids exceeding logical partition limits?

Accepted Answer

Use a hierarchical partition key of carrierId then shipmentId

Answer

Use carrierId alone as the partition key

Answer

Use shipmentId alone as the partition key

Answer

Use a synthetic partition key concatenating a random 1–100 suffix with carrierId

Question 52

A high-throughput orders container ingests thousands of writes per second. Documents contain dozens of properties, but analysts only ever filter on 'status' and 'customerId', and sort on 'orderDate'. The team notices write RU charges are very high due to the default indexing policy indexing every path. They want to minimize write RU cost while still supporting the known query patterns efficiently. Which indexing policy change best achieves this?

Accepted Answer

Change the includedPaths to only '/status/?', '/customerId/?', and '/orderDate/?', set excludedPaths to '/*', and add a composite index on ('status', 'orderDate').

Answer

Set the indexing mode to 'none' so writes incur no indexing overhead, then rely on cross-partition scans for the analyst queries.

Answer

Keep includedPaths at '/*' and add each frequently queried property to excludedPaths individually.

Answer

Enable wildcard indexing on '/*/?' and disable the automatic indexing flag to reduce write cost.

Question 53

A data engineering team must perform a one-time bulk migration of 500 million documents into a new Cosmos DB container. During the load, no queries will run against the container; queries only begin after the migration completes and is validated. The team wants to minimize the RU cost and maximize write throughput during the bulk load, while still supporting rich queries afterward. What is the most effective indexing approach?

Accepted Answer

Set the indexing mode to 'none' during the bulk load, then switch to 'consistent' mode after the load to trigger a background reindex.

Answer

Keep the default 'consistent' indexing policy with all paths indexed throughout the migration.

Answer

Use 'lazy' indexing mode during the bulk load and leave it as 'lazy' permanently for lowest write cost.

Answer

Exclude all paths except '/id' using an includedPaths/excludedPaths policy and keep it that way permanently.

Question 54

A high-throughput order-ingestion container writes 5,000 documents per second. Each document has 60 properties, but analysts only ever filter and sort on 4 of them: orderStatus, customerId, orderDate, and region. The team notices write RU charges are very high and wants to reduce them without breaking the analyst queries. What indexing policy change should they apply?

Accepted Answer

Set includedPaths to explicitly list only the 4 queried property paths and add '/*' to excludedPaths to exclude everything else.

Answer

Set the indexing mode to 'none' so that no properties are indexed, eliminating index maintenance overhead on writes.

Answer

Set includedPaths to '/*' and add the 4 queried properties to excludedPaths so only those are indexed.

Answer

Keep the default indexing policy but increase provisioned throughput so writes no longer throttle.

Question 55

A logistics company stores shipment tracking documents in an Azure Cosmos DB for NoSQL container. Each document contains a large nested 'rawSensorPayload' object with dozens of properties that are never used in query filters, ORDER BY, or JOIN clauses — they are only returned when a document is read by id. The write workload is heavy, and the team notices high RU charges on every insert and update. Queries only ever filter on 'shipmentId', 'status', and 'region'. What is the most effective change to reduce write RU consumption while preserving query performance?

Accepted Answer

Configure a custom indexing policy that excludes the '/rawSensorPayload/*' path while keeping the queried properties indexed.

Answer

Set the indexing mode to 'none' so that no properties are indexed during writes.

Answer

Move 'rawSensorPayload' into a separate container referenced by shipmentId to avoid indexing it.

Answer

Enable autoscale throughput so the container automatically absorbs the higher write RU cost.

Question 56

A retail analytics team enabled the integrated cache via a dedicated gateway to reduce RU charges on a set of frequently repeated queries. Most queries now return RU charge of 0, but one high-volume query keeps showing full RU cost on every execution even though it is identical each time. Investigation shows the SDK client for that query sets the request consistency level to Strong, while the account default is Session. What is the most likely cause of the cache never being used for that query?

Accepted Answer

The integrated cache only serves requests made with Session or Eventual consistency; requests using Strong or Bounded Staleness bypass the cache entirely.

Answer

The MaxIntegratedCacheStaleness value must be explicitly set to zero for Strong consistency requests to be cached.

Answer

The integrated cache caches only point reads, so any query with a filter always incurs full RU cost regardless of consistency.

Answer

Strong consistency requires provisioned throughput above 100,000 RU/s before the integrated cache can be enabled.

Question 57

A retail analytics dashboard issues the same set of point reads and queries thousands of times per minute against an Azure Cosmos DB for NoSQL container. The data changes only a few times per hour, but the team is alarmed by the RU consumption and cost from these repetitive reads. They want to dramatically reduce RU charges for the repeated reads without changing the queries themselves. Which approach best meets this requirement?

Accepted Answer

Provision a dedicated gateway and configure the integrated cache, connecting the SDK in gateway mode with a suitable MaxIntegratedCacheStaleness.

Answer

Switch the SDK to direct connectivity mode to bypass the gateway and cache results client-side.

Answer

Enable Azure Synapse Link and query the analytical store for the repeated reads.

Answer

Increase provisioned throughput with autoscale so the repeated reads no longer throttle.

Question 58

A retail application uses an Azure Cosmos DB dedicated gateway with the integrated cache enabled to serve a product catalog. The catalog is updated a few times per hour, but the team is willing to serve slightly stale data to maximize cache hits and reduce RU consumption on repeated reads. Currently, every read request is configured with the default MaxIntegratedCacheStaleness of 5 minutes, but the team wants cached entries to remain valid for up to 30 minutes to further cut costs. Which action correctly achieves this while ensuring the cache is actually used?

Accepted Answer

Set the MaxIntegratedCacheStaleness request option to 30 minutes on the read requests and route them through the dedicated gateway connection string.

Answer

Increase the dedicated gateway instance size so the cache holds entries longer, keeping the default staleness setting.

Answer

Change the account default consistency to eventual, which automatically extends the integrated cache retention to 30 minutes.

Answer

Set MaxIntegratedCacheStaleness to 30 minutes but continue using the direct-mode connection string for lowest latency.

Question 59

Your team enabled the Azure Cosmos DB integrated cache by provisioning a dedicated gateway and routing the .NET SDK client through gateway mode with the dedicated gateway endpoint. A frequently repeated query still shows full RU charges on most executions rather than the expected near-zero cost. Investigation reveals the application sets the request consistency level to Strong on each query. What is the most likely reason the integrated cache is not serving these queries?

Accepted Answer

The integrated cache only serves requests using Session or Eventual consistency; requests with Strong or Bounded Staleness bypass the cache.

Answer

The integrated cache only works with point reads, never with queries, so query results are never cached regardless of configuration.

Answer

The integrated cache requires the SDK to use Direct mode, so gateway mode disables caching entirely.

Answer

The integrated cache must be explicitly enabled per query with a MaxIntegratedCacheStaleness of 0 to force cache hits.

Question 60

An e-commerce container stores order documents where each order has a nested array named 'items', with each item containing a 'category' field. A developer needs a query that returns one row per order-item combination, filtered to only items where category = 'Electronics', so downstream reporting can list each electronics line individually. Which SQL query correctly accomplishes this in Azure Cosmos DB for NoSQL?

Accepted Answer

SELECT o.id, i.category FROM orders o JOIN i IN o.items WHERE i.category = 'Electronics'

Answer

SELECT o.id, o.items.category FROM orders o WHERE o.items.category = 'Electronics'

Answer

SELECT o.id, i.category FROM orders o WHERE ARRAY_CONTAINS(o.items, 'Electronics')

Answer

SELECT o.id, i.category FROM orders o JOIN orders i WHERE i.category = 'Electronics'

Question 61

Your company runs an on-premises order processing system that publishes messages to Apache Kafka. You need to continuously ingest these Kafka messages into an Azure Cosmos DB for NoSQL container, and separately stream all document changes from a different Cosmos DB container back out to a downstream Kafka topic for analytics. You want to use the official Cosmos DB Kafka connectors with minimal custom code. Which configuration should you deploy?

Accepted Answer

Use the Cosmos DB sink connector to ingest Kafka messages into the container, and the Cosmos DB source connector (which reads the change feed) to publish document changes to the downstream Kafka topic.

Answer

Use the Cosmos DB source connector to ingest Kafka messages into the container, and the Cosmos DB sink connector to publish document changes to the downstream Kafka topic.

Answer

Use the Cosmos DB sink connector for both directions, configuring one instance to read and one to write.

Answer

Use Azure Data Factory copy activities in both directions since the Kafka connectors only support one-way movement from Cosmos DB to Kafka.

Question 62

A financial services company runs an Azure Cosmos DB account with a single write region in East US and read regions in West Europe and Southeast Asia. Automatic failover is enabled. The operations team wants to validate their disaster recovery runbook by promoting West Europe to be the write region during a controlled maintenance window, without simulating an actual regional outage. What should they do?

Accepted Answer

Trigger a manual failover, which lets the team explicitly change the write region to West Europe in a controlled, no-data-loss manner.

Answer

Increase the automatic failover priority of West Europe to the highest level so it immediately becomes the write region.

Answer

Enable multi-region writes so that West Europe automatically accepts writes during the maintenance window.

Answer

Temporarily shut down the East US region from the Azure portal to force automatic failover to West Europe.

Question 63

You are modeling a blogging platform in Azure Cosmos DB for NoSQL. Each Post can have many Tags, and each Tag applies to many Posts (a many-to-many relationship). Tag metadata (name, description, color) is edited rarely but read constantly. Users frequently query 'all posts for a given tag' and 'all tags on a given post'. Tag descriptions must stay reasonably current across the site. Which modeling approach best balances read performance and update maintainability?

Accepted Answer

Store Tags in their own container as the source of truth, and embed only lightweight tag references (tag id plus denormalized name) in each Post.

Answer

Embed the full Tag document (name, description, color) inside every Post that uses it, with no separate Tag container.

Answer

Embed the full list of Post documents inside each Tag document so a tag lookup returns all its posts in one read.

Answer

Create a single container where every Post-Tag combination is stored as a separate item duplicating both the full post body and full tag metadata.

Question 64

You are designing a Cosmos DB container for a social media platform. Each user document embeds an array of posts. Product analytics show that active users can accumulate tens of thousands of posts over time, and the document grows continuously. Users frequently read their own profile but only occasionally query all their posts at once. What is the most appropriate data modeling decision?

Accepted Answer

Reference posts as separate items in a posts container partitioned by userId, and keep only a small profile summary embedded in the user document.

Answer

Keep embedding all posts in the user document and rely on autoscale throughput to absorb the larger reads.

Answer

Embed the posts but enable TTL on the user document so old posts are automatically purged.

Answer

Split the posts array across multiple properties in the same document to stay under the size limit.

Exam domain	Exam weight	Practice
Design and implement data models	38%	Practice this topic
Design and implement data distribution	8%	Practice this topic
Integrate an Azure Cosmos DB solution	8%	Practice this topic
Optimize an Azure Cosmos DB solution	18%	Practice this topic
Maintain an Azure Cosmos DB solution	28%	Practice this topic

Microsoft Azure Cosmos DB Developer Specialty (DP-420) (DP-420) practice exam & study guide

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Design and implement data models (38%)

Design and implement data distribution (8%)

Integrate an Azure Cosmos DB solution (8%)

Optimize an Azure Cosmos DB solution (18%)

Maintain an Azure Cosmos DB solution (28%)