Which features can reduce the amount of I/O processed and are unique to the Exadata Storage Server?

You are getting ready to prepare your client to obtain the maximum benefit from using the Sun
Oracle Database Server. Which features can reduce the amount of I/O processed and are unique
to the Exadata Storage Server?

A.
Partitioning

B.
Parallelism

C.
Storage Indexes and Predicate Filtering

D.
Database Resource Manager

Explanation:
Storage indexes: Storage indexes reduce disk I/O volumes by tracking high and low
values in memory for each 1-megabyte storage region. They can be used to give partition pruning
benefits without requiring the partition key in the WHERE clause, as long as one of these columns
is correlated with the partition key. For example, if a table has order_date and processed_date
columns, is partitioned on order_date, and if orders
are processed within 5 days of receipt, the storage server can track which processed_date values
are included in each order partition, giving partition pruning for queries referring to either
order_date or processed_date. Other data sets that are physically ordered on disk, such as
incrementing keys, can also benefit.
Smart scans: Smart scans are Exadata’s headline feature. They provide three main benefits:
reduced data transfer volumes from storage servers to databases, CPU savings on database
servers as workload is transferred to storage servers, and improved buffer cache efficiency thanks
to column projection. Smart scans use helper processes that function much like parallel query
processes but run directly on the storage servers. Operations off-loadable through smart scans
include the following:
* Predicate filtering—processing WHERE clause comparisons to literals, including logical
operators and most SQL functions.
* Column projection—by looking at a query’s SELECT clause, storage servers return only the
columns requested, which is a big win for wide tables.
* Joins—storage servers can improve join performance by using Bloom filters to recognize rows
matching join criteria during the table scan phase, avoiding most of the I/O and temporary space
overhead involved in the join processing.
* Data mining model scoring—for users of Oracle Data Mining, scoring functions like PREDICT()
can be evaluated on storage servers.
http://structureddata.org/2011/01/11/making-the-most-of-oracle-exadata-a-technical-review/