Data Warehousing 11g Essentials: 1Z0-515

Correct Answer: A

Explanation:

Bitmap indexes are a highly compressed index type that tends to be used primarily for data warehouses.

Characteristic of Bitmap Indexes

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For columns with very few unique values (low cardinality)

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Columns that have low cardinality are good candidates (if the cardinality of a column is <= 0.1 % that the column is ideal candidate, consider also 0.2% 1%)

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Tables that have no or little insert/update are good candidates (static data in warehouse)

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Stream of bits: each bit relates to a column value in a single row of table

References:

Correct Answer: A

Explanation: Oracle Optimized Warehouse Reference Configurations are best practice guides to choosing the right server, storage and networking components to build an Oracle data warehouse. These best practice guides encapsulate years of configuration expertise from Oracle and its partners, helping customers take the risk out of implementing a data warehouse.

References:

Correct Answer: AD

Explanation:

A: Even when you don't name a specific partition in a SQL statement, the fact that a table is partitioned might still influence the manner in which the statement accesses the table. When a SQL statement accesses one or more partitioned tables, the Oracle optimizer attempts to use the information in the WHERE clause to eliminate some of the partitions from consideration during statement execution. This process, called partition pruning, speeds statement execution by ignoring any partitions that cannot satisfy the statement's WHERE clause. To do so, the optimizer uses information from the table definition combined with information from the statement's WHERE clause.

D: A partition wise join is a join between (for simplicity) two tables that are partitioned on the same column with the same partitioning scheme. In shared nothing this is effectively hard partitioning locating data on a specific node / storage combo. In Oracle is is logical partitioning.

If you now join the two tables on that partitioned column you can break up the join in smaller joins exactly along the partitions in the data. Since they are partitioned (grouped) into the same buckets, all values required to do the join live in the equivalent bucket on either sides. No need to talk to anyone else, no need to redistribute data to anyone else... in short, the optimal join method for parallel processing of two large data sets.