Correct Answer: C

Explanation:

There are two approaches to consider for data integration: ELT and ETL. The difference between ETL and ELT lies in the environment in which the data transformations are applied. In traditional ETL, the transformation takes place when the data is en route from the source to the target system. In ELT, the data is loaded into the target system, and then transformed within the target system environment.

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: D

References:

Correct Answer: C

Explanation:

Association is a data mining function that discovers the probability of the co-occurrence of items in a

collection. The relationships between co-occurring items are expressed as association rules.

Market-Basket Analysis

Association rules are often used to analyze sales transactions. For example, it might be noted that

customers who buy cereal at the grocery store often buy milk at the same time. In fact, association

analysis might find that 85% of the checkout sessions that include cereal also include milk. This

relationship could be formulated as the following rule.

Cereal implies milk with 85% confidence

This application of association modeling is called market-basket analysis. It is valuable for direct

marketing, sales promotions, and for discovering business trends. Market-basket analysis can also be

used effectively for store layout, catalog design, and cross-sell.

Association Algorithm

Oracle Data Mining uses the Apriori algorithm to calculate association rules for items in frequent itemsets.

References:

Correct Answer: D

Explanation:

Data mining does not reduce the amount of data in the warehouse.

Note:

Data mining (the analysis step of the knowledge discovery in databases process, or KDD), a relatively

young and interdisciplinary field of computer science is the process of discovering new patterns from large

data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and

database systems. The overall goal of the data mining process is to extract knowledge from a data set in a

human-understandable structure and besides the raw analysis step involves database and data

management aspects, data preprocessing, model and inference considerations, interestingness metrics,

complexity considerations, post-processing of found structure, visualization and online updating.