CLF-C02 Cloud Technology and Services Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Amazon RDS for MySQL

  Why it's wrong here

  Amazon RDS for MySQL presents a traditional single-primary relational database with optional read replicas, built with row-based storage and default InnoDB indexes that are optimized for frequent, small transactional operations like inserts, updates, and point lookups. It has no distributed parallelism; a single instance executes queries serially and its maximum storage is 64 TiB, so scanning billions of rows for multi-way joins and aggregations would be slow and would compete with production traffic. RDS also lacks columnar compression and MPP orchestration, meaning it cannot scale out across hundreds of nodes to execute the analytical workloads that Redshift is purpose-built for. As a result, it is not a fit for petabyte-scale data warehouse use cases.

• ✗

  Amazon DynamoDB

  Why it's wrong here

  Amazon DynamoDB is a serverless, key-value and document NoSQL database that guarantees single-digit-millisecond latency for transactional reads and writes by distributing data across partitions based on a partition key. It is schema-less and scales horizontally, but its query interface is narrow: you can access items only by primary key or through limited secondary indexes, and while PartiQL provides some SQL-like syntax, it does not support multi-table joins, window functions, or complex analytical aggregations. DynamoDB is also priced for point operations and its scans are expensive and slow across large tables, so it is intentionally unsuited for ad-hoc business intelligence queries. Thus, it cannot replace an analytical engine like Redshift.

• ✓

  Amazon Redshift

  Why this is correct

  Redshift is AWS's managed data warehouse service specifically designed for OLAP. It uses columnar storage and parallel query execution to efficiently run complex SQL queries across petabytes of data and integrates with standard BI tools.

• ✗

  Amazon Aurora

  Why it's wrong here

  Amazon Aurora is a fully managed relational database engine that is wire-compatible with MySQL and PostgreSQL, and it is architected for online transaction processing (OLTP) workloads such as user-facing applications that require low-latency reads and writes. Its storage layer is a distributed, SSD-backed volume that maximizes durability and availability, but it stores data in row-oriented format rather than columnar format. Aurora does not use Redshift's massive parallel processing (MPP) architecture or columnar compression, and its practical scale tops out at 128 TiB, far below Redshift's petabyte-scale data warehouse capacity. Therefore, while Aurora can handle some analytical queries, it is not designed for large-scale, complex OLAP workloads.

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