Serverless Databases: DynamoDB, Cosmos DB u0026 Firestore Quiz Quiz

Explore core concepts of serverless databases including scalable data models, consistency, partitioning, indexing, and query limitations. This quiz is designed to help you understand the foundational principles and best practices for working with cloud-native NoSQL database services.

  1. Understanding Data Models

    Which data model is most commonly used by serverless NoSQL databases to organize large-scale datasets for rapid retrieval?

    1. Hierarchical tree
    2. Tabular row-column
    3. Graph-based
    4. Document-oriented

    Explanation: Document-oriented databases efficiently store and retrieve semi-structured data using formats like JSON, making them well-suited for flexible and scalable access patterns. Tabular row-column models are typical of relational systems, and hierarchical or graph-based structures serve more specialized use cases. While the distractors might be used in some systems, document-oriented models best match the flexibility and scaling needs common in serverless NoSQL platforms.

  2. Consistency in Serverless Databases

    When designing a global mobile app, which consistency model ensures the fastest possible data reading even if updates may not be immediately visible?

    1. Strong consistency
    2. Immediate correctness
    3. Causal consistency
    4. Eventual consistency

    Explanation: Eventual consistency allows fast reads by permitting stale data until updates propagate, which is useful in highly distributed environments. Strong consistency provides up-to-date data at the cost of latency, while causal consistency maintains event order but can also delay some reads. Immediate correctness is not a standard term; thus, the best answer is eventual consistency for speed and availability.

  3. Partitioning Strategies

    In serverless databases, what is the primary benefit of choosing an effective partition key for your data?

    1. It enables multi-threading
    2. It helps balance data and query load evenly
    3. It provides write-ahead logging
    4. It guarantees zero latency

    Explanation: An effective partition key distributes data across storage nodes, helping to evenly balance both storage and query traffic. Multi-threading and write-ahead logging relate to how systems execute and persist operations, but do not address data distribution. Guaranteeing zero latency is unrealistic; partitioning primarily seeks to avoid hotspots and improve scalability.

  4. Indexing and Query Performance

    Suppose you need to efficiently filter items by a non-primary attribute in a large dataset; which feature typically supports this requirement in serverless NoSQL databases?

    1. Snapshots
    2. Global secondary index
    3. Stored procedures
    4. Binary large objects (BLOBs)

    Explanation: A global secondary index enables queries on non-primary attributes, improving query flexibility and performance. BLOBs refer to storing large binary data and do not help with indexing. Stored procedures are used for server-side logic, while snapshots are backups and do not affect how queries are performed. Thus, the correct answer is global secondary index.

  5. Query Limitations

    What is a common limitation when querying data in serverless NoSQL databases compared to traditional relational systems?

    1. Lack of query language support
    2. No support for data encryption
    3. Inability to perform multi-table joins
    4. Only supports synchronous operations

    Explanation: Serverless NoSQL databases often do not support complex multi-table joins, which are typical in relational databases, to maintain scalability and speed. Claims about no support for encryption or query languages are incorrect, as these databases usually provide such features. Synchronous and asynchronous operations are both generally supported, making 'inability to perform multi-table joins' the correct choice.