Which solution meets these requirements?

Explanations:

Using a dense storage instance for all the data would not be cost-effective, as it would store both recent and historical data locally, which is inefficient for 100 TB of data. Provisioning enough instances to support fluctuating queries also adds complexity and potential cost.

Storing the most recent data on dense storage instances and using Amazon S3 for historical data with Redshift Spectrum could be a good approach for cost efficiency. However, this option does not address the fluctuating query load, which requires more dynamic handling (such as concurrency scaling or elastic resize).

This option is the most suitable, as it efficiently stores recent data on Redshift dense storage and uses Amazon S3 with Redshift Spectrum for historical data. Enabling Amazon Redshift Concurrency Scaling helps handle the fluctuating query load by automatically adding capacity for high-demand periods.

While this option stores recent data on dense storage and uses Redshift Spectrum for historical data, leveraging elastic resize alone does not address the fluctuating query load effectively. Elastic resize requires manual intervention and is not as responsive as Concurrency Scaling for variable query demands.