MCQs on Advanced Concepts and Optimization | Azure Cosmos DB MCQs Questions
Master Azure Cosmos DB with These Advanced MCQ Questions and Answers
Explore key advanced features of Azure Cosmos DB with this comprehensive set of Azure Cosmos DB MCQ questions and answers. These questions cover critical topics such as multi-region writes, conflict resolution, change feed processing, geo-replication, failover strategies, performance tuning, cost optimization, and consistency models. Designed for professionals aiming to deepen their knowledge of Azure Cosmos DB, this guide helps you excel in managing globally distributed, highly available, and scalable databases. Test your skills and enhance your understanding of real-time data processing, transactional handling, and optimization techniques for Azure Cosmos DB.
MCQs on Multi-Region Writes and Conflict Resolution
- What does enabling multi-region writes in Azure Cosmos DB provide?
a) Faster backups
b) Higher availability and low-latency writes
c) Automatic schema migrations
d) Reduced cost for data storage - When using multi-region writes, conflict resolution in Azure Cosmos DB is handled by:
a) Manually configuring conflict resolution policies
b) Using default last-write-wins resolution
c) Disabling multi-master setup
d) Migrating data to a single region - Which property is essential for custom conflict resolution in Azure Cosmos DB?
a) _ts (timestamp)
b) _etag
c) _conflict
d) partitionKey - Conflict resolution policy types in Azure Cosmos DB include:
a) Write-through and read-through
b) Custom and last-write-wins
c) Indexing and sharding
d) Multi-threading and load balancing - Azure Cosmos DB uses which factor to resolve write conflicts?
a) Storage latency
b) Resource throughput
c) Timestamps or custom properties
d) Global request units - Multi-region writes in Cosmos DB are supported in which consistency model?
a) Eventual consistency only
b) Strong and bounded-staleness consistency
c) Session consistency only
d) Strong consistency exclusively
MCQs on Change Feed Processing for Real-Time Data
- What does the change feed in Azure Cosmos DB do?
a) Tracks changes in container data in real time
b) Manages container backups
c) Optimizes data partitions
d) Schedules performance tasks - A common use case for Azure Cosmos DB change feed is:
a) Serving API requests
b) Real-time analytics and ETL processes
c) Data encryption
d) Backup and recovery - Which Azure service is often used with Cosmos DB change feed for event-driven architectures?
a) Azure Monitor
b) Azure Functions
c) Azure Kubernetes Service
d) Azure Storage - How does change feed deliver changes?
a) As raw SQL queries
b) Through ordered and incremental log streams
c) Using REST API responses
d) Through manual polling - To consume a change feed, you need:
a) A pre-configured index policy
b) A lease container for checkpointing
c) A virtual machine
d) A multi-region write setup - Change feed works at the level of:
a) Individual documents only
b) Containers in a Cosmos DB account
c) Global database accounts
d) Request units
MCQs on Geo-Replication and Failover Strategies
- Geo-replication in Azure Cosmos DB is primarily used for:
a) Optimizing resource throughput
b) Ensuring high availability and disaster recovery
c) Data encryption
d) Indexing JSON data - How many regions can you replicate your data to in Azure Cosmos DB?
a) Only two regions
b) As many regions as needed
c) Maximum of five regions
d) Only the primary region - Which strategy minimizes downtime during a regional outage in Cosmos DB?
a) Auto-failover
b) Manual backups
c) Request unit throttling
d) Schema migrations - In a geo-replication scenario, a write region is also known as:
a) A primary region
b) A failover region
c) A standby region
d) A read-only replica - Manual failover in Cosmos DB requires:
a) Configuring custom indexing
b) Selecting a new primary region manually
c) Adjusting consistency levels
d) Migrating data to other regions - Automatic failover in Cosmos DB ensures:
a) Data encryption during replication
b) Switching primary regions during outages
c) Reduced request unit costs
d) Manual intervention for failover
MCQs on Tuning Performance and Cost Optimization
- What does scaling throughput in Azure Cosmos DB involve?
a) Adjusting storage size
b) Configuring request units (RUs) per second
c) Migrating data to a new account
d) Optimizing JSON schema - Partition keys are essential for:
a) Securing data access
b) Ensuring even distribution of data and throughput
c) Enabling change feed processing
d) Managing backups - Which feature helps reduce costs for infrequently accessed data?
a) Autoscale throughput
b) Time-to-live (TTL)
c) Multi-region writes
d) Strong consistency - How do you monitor performance in Cosmos DB?
a) By enabling auto-indexing
b) Using metrics in Azure Monitor
c) Adjusting consistency models
d) By using SQL queries - To optimize costs, Azure Cosmos DB offers:
a) Pay-as-you-go pricing model
b) Dedicated cluster billing
c) Request unit pooling across containers
d) Fixed daily quotas - Query performance in Cosmos DB is impacted by:
a) Consistency models, indexing policies, and partitioning
b) Request unit costs only
c) Data storage location
d) Backup frequency
MCQs on Handling Transactions and Consistency Models
- Transactions in Azure Cosmos DB are supported within:
a) Multiple containers
b) A single logical partition
c) All geo-replicated regions
d) A request unit scope - Which consistency level offers the strongest guarantees?
a) Session consistency
b) Eventual consistency
c) Strong consistency
d) Bounded-staleness consistency - How does bounded-staleness consistency differ from eventual consistency?
a) It guarantees global order of reads and writes
b) Allows for read-your-own-writes behavior
c) Ensures minimal replication lag
d) Provides guaranteed latency - Which operation ensures atomicity in Cosmos DB?
a) Stored procedures
b) Manual failover
c) Index transformations
d) Schema validation - Eventual consistency is best suited for:
a) Scenarios requiring minimal latency and strong guarantees
b) Real-time collaboration apps where latency is critical
c) Write-heavy and read-scalable workloads
d) Disaster recovery operations - The default consistency level in Cosmos DB is:
a) Eventual consistency
b) Bounded-staleness consistency
c) Session consistency
d) Strong consistency
Answer Key
| QNo | Answer |
|---|---|
| 1 | b) Higher availability and low-latency writes |
| 2 | b) Using default last-write-wins resolution |
| 3 | c) _conflict |
| 4 | b) Custom and last-write-wins |
| 5 | c) Timestamps or custom properties |
| 6 | b) Strong and bounded-staleness consistency |
| 7 | a) Tracks changes in container data in real time |
| 8 | b) Real-time analytics and ETL processes |
| 9 | b) Azure Functions |
| 10 | b) Through ordered and incremental log streams |
| 11 | b) A lease container for checkpointing |
| 12 | b) Containers in a Cosmos DB account |
| 13 | b) Ensuring high availability and disaster recovery |
| 14 | b) As many regions as needed |
| 15 | a) Auto-failover |
| 16 | a) A primary region |
| 17 | b) Selecting a new primary region manually |
| 18 | b) Switching primary regions during outages |
| 19 | b) Configuring request units (RUs) per second |
| 20 | b) Ensuring even distribution of data and throughput |
| 21 | b) Time-to-live (TTL) |
| 22 | b) Using metrics in Azure Monitor |
| 23 | a) Pay-as-you-go pricing model |
| 24 | a) Consistency models, indexing policies, and partitioning |
| 25 | b) A single logical partition |
| 26 | c) Strong consistency |
| 27 | c) Ensures minimal replication lag |
| 28 | a) Stored procedures |
| 29 | b) Real-time collaboration apps where latency is critical |
| 30 | c) Session consistency |