[NEW] Azure Cosmos DB Developer Specialty Certification

Detailed Exam Domain CoverageTo pass the Microsoft Certified: Azure Cosmos DB Developer Specialty exam, you need to master specific architectural and development patterns. This practice test suite directly mimics the official weightage and technical depth of the actual exam blueprint:Design and Implement Data Models (38%)Designing highly efficient data partitioning strategies for the Azure Cosmos DB Core (NoSQL) API.Applying advanced modeling patterns (denormalization, referencing, and combining multiple entity types within a single container).Choosing appropriate container schemas and optimized partition keys to avoid hot partitions.Implementing data models programmatically using the official SDKs (C#, Java, Python, and JavaScript).Selecting the correct API for specific operational workloads (Core API, MongoDB, Table, or Gremlin).Design and Implement Data Distribution (8%)Configuring and testing the five consistency models (Strong, Bounded Staleness, Session, Consistent Prefix, and Eventual) via the Azure Portal and SDK.Connecting to multi-region write accounts and managing regional failovers within SDK application code.Planning cross-region replication to balance global throughput distribution.Using Azure CLI and Azure Resource Manager (ARM) templates to automate the provisioning of regional resources.Integrate an Azure Cosmos DB Solution (8%)Executing high-throughput data ingestion using bulk import tools, the SDK bulk execution mode, or Azure Data Factory.Processing real-time change-feed events using Azure Functions and the Change Feed Processor library.Integrating account metrics with Azure Monitor and Application Insights for deep diagnostics.Connecting Azure Cosmos DB natively to downstream services like Logic Apps.Optimize an Azure Cosmos DB Solution (18%)Customizing indexing policies (including/excluding paths, composite indexes) to optimize complex query patterns.Measuring and minimizing Request Unit (RU) consumption and latency across high-volume workload scenarios.Adjusting provisioned throughput programmatically via Azure CLI or PowerShell scripts.Developing User-Defined Functions (UDFs) and stored procedures to handle specialized query logic efficiently.Maintain an Azure Cosmos DB Solution (28%)Troubleshooting performance issues, transient errors, and 429 exceptions using the SDK logging and Azure Monitor metrics.Implementing disaster recovery strategies, including point-in-time recovery (PITR) and continuous backups.Securing data at rest and in transit using Azure Key Vault, firewall configurations, and role-based access control (RBAC).Deploying database infrastructure reliably using DevOps CI/CD pipelines.Course DescriptionI designed these practice tests to solve a specific problem: many developers know how to write basic queries, but struggle with the highly specific, architectural decision-making questions found on the actual exam. Passing this certification requires more than just memorizing documentation; you must understand the deep trade-offs behind partitioning, Request Unit (RU) optimization, and global consistency levels.Instead of generic questions, I have built a comprehensive scenario-based question bank that puts you in the shoes of a cloud architect. You will face problems involving hot partitions, unexpected cross-region latencies, and tricky index behaviors.Every single question in this course includes a thorough, step-by-step breakdown. I do not just tell you which answer is right—I explain exactly why the correct option fits the scenario best, and why the other five choices will fail or cause performance bottlenecks in production. This approach helps you identify gaps in your knowledge and teaches you how to think like the exam creators.Sample Practice Questions PreviewQuestion 1: Data Modeling PartitioningYou are designing an Azure Cosmos DB Core API container for a logistics application that tracks real-time delivery vehicle locations. The container will handle millions of writes per hour. Most queries filter by VehicleId and return the most recent status updates sorted by timestamp. You need to choose a partition key that maximizes write throughput, avoids hot partitions, and maintains efficient query performance.Options:A. Use StatusDate as the partition key.B. Use VehicleId as the partition key.C. Combine VehicleId and a random suffix number as a synthetic partition key.D. Use CompanyId as the partition key where each company manages thousands of vehicles.E. Use a GUID generated uniquely for each telemetry write as the partition key.F. Use StateProvince as the partition key based on where the vehicle is currently located.Correct Answer:B. Use VehicleId as the partition key.Detailed Explanation of All Options:A is incorrect: Using StatusDate creates a classic "hot partition" anti-pattern. Because millions of writes happen continuously throughout the current day, all incoming traffic will target the same physical partition block dedicated to today's date, bottlenecking your throughput.B is correct: VehicleId provides a high-cardinality key, distributing writes evenly across multiple physical partitions. Because your primary query pattern filters directly by VehicleId, this choice allows the query engine to route requests directly to a single partition, completely avoiding expensive, resource-intensive cross-partition queries.C is incorrect: While a synthetic partition key with a random suffix distributes writes effectively, it breaks your query efficiency. To fetch updates for a specific vehicle, your application would have to query across all random suffixes, forcing a cross-partition query that drains RUs.D is incorrect: CompanyId has relatively low cardinality compared to millions of individual vehicles. This will result in large logical partitions that could eventually hit the 20 GB storage limit per logical partition, causing future write failures.E is incorrect: A unique GUID provides excellent write distribution, but it severely penalizes your query pattern. Because queries filter by VehicleId, searching via a GUID partition key forces a full scatter-gather cross-partition query across every single physical partition in your cluster.F is incorrect: Vehicles group heavily around major distribution hubs or populous states. This uneven distribution leads to storage and throughput imbalances where a few state partitions become overloaded while others remain completely idle.Question 2: Consistency ModelsA global e-commerce enterprise uses a multi-region Azure Cosmos DB account with write regions in East US and West Europe. Users edit their account profile information frequently. The application requires that when a user updates their shipping address, they must instantly see the updated address if they refresh their browser window. However, users in other regions can tolerate a slight delay before seeing the updated profile details. You need to configure the default consistency level to minimize latency while meeting this requirement.Options:A. Strong ConsistencyB. Bounded Staleness ConsistencyC. Session ConsistencyD. Consistent Prefix ConsistencyE. Eventual ConsistencyF. Multi-master Write Conflict ResolutionCorrect Answer:C. Use Session ConsistencyDetailed Explanation of All Options:A is incorrect: Strong consistency guarantees global data uniformity immediately, but it requires synchronous replication across distant geographic regions before a write acknowledges. This introduces massive write latency and decreases overall availability during regional network hiccups.B is incorrect: Bounded Staleness limits read lag to a specific time window or operation count. While useful for predictable data updates, it does not guarantee immediate "read-your-own-writes" visibility for a specific user unless you set the staleness window to zero, which effectively mimics strong consistency and destroys performance.C is correct: Session consistency is scoped directly to a specific client session. It guarantees that the user who made the update will always see their own modifications immediately ("read-your-own-writes"). For all other users outside that session, data replicates asynchronously, maximizing performance and keeping costs low.D is incorrect: Consistent Prefix ensures that reads never see out-of-order writes, but it does not guarantee that a user will see their own latest update immediately upon a page refresh.E is incorrect: Eventual consistency offers the lowest possible latency and highest availability, but it provides no ordering guarantees. A user refreshing their browser right after an update could easily see stale data, violating the core requirement.F is incorrect: Multi-master conflict resolution is a configuration mechanism used to merge overlapping changes from different regions; it is not a consistency level that dictates data visibility guarantees to a client application.Question 3: Integrating Change FeedYou are developing an event-driven microservices architecture where an Azure Function must process real-time changes from an Azure Cosmos DB Core API container. Whenever a document updates, the function must transmit the data to an external data warehouse. During heavy traffic bursts, the Azure Function times out, and you notice missed documents in your destination warehouse. You need to configure the change feed integration to scale reliably and guarantee zero missing messages.Options:A. Increase the maxItemsPerInvocation property in the Azure Function host configuration.B. Switch the Azure Function hosting plan to a shared App Service Plan running on a single instance.C. Implement a custom timer trigger in Azure Functions that queries the container using a modified timestamp field.D. Configure the Azure Function to use the Cosmos DB Trigger with an isolated leases container.E. Enable automated point-in-time database restoration to re-read missing records.F. Increase the provisioned throughput (RU/s) of the leases container to handle heavy coordination state updates.Correct Answer:D. Configure the Azure Function to use the Cosmos DB Trigger with an isolated leases container.Detailed Explanation of All Options:A is incorrect: Increasing maxItemsPerInvocation forces the function to process larger batches of documents at once. During high-traffic spikes, this actually increases processing time per execution, making your function more likely to hit execution timeouts and crash mid-batch.B is incorrect: Shifting to a single-instance App Service plan limits your function's ability to scale horizontally. The change feed processor needs to distribute lease tokens across multiple scaling instances to process partitions concurrently.C is incorrect: Building a custom timer-based query engine introduces architectural complexity and misses rapid, intermediate document updates. It also causes heavy, unnecessary RU consumption compared to the native change feed engine.D is correct: The native Azure Functions Cosmos DB Trigger utilizes the Change Feed Processor library internally. Using a dedicated leases container allows the runtime to track progress checkpoints safely. If an instance fails or times out, another instance automatically picks up the lease from the exact last successful checkpoint, guaranteeing no data loss.E is incorrect: Point-in-time recovery is a disaster recovery mechanism designed to restore deleted or corrupted databases. It cannot be used as an active integration pattern to fix real-time application processing bottlenecks.F is incorrect: While the leases container needs a baseline level of throughput to operate, simply increasing its RU/s will not prevent function timeouts or fix architectural scaling issues if your execution logic or partition tracking is unoptimized.Welcome to the Mock Exam Practice Tests Academy to help you prepare for your Microsoft Certified: Azure Cosmos DB Developer Specialty.You can retake the exams as many times as you wantThis is a huge original question bankYou get support from instructors if you have questionsEach question has a detailed explanationMobile-compatible with the Udemy appI hope that by now you're convinced! And there are a lot more questions inside the course.