To answer this question, you need to understand the concept of partitioning methods in parallel processing.

The lowest CPU cost partitioning method for parallel stage to parallel stage is the "Same" method.

The "Same" partitioning method ensures that the same data is sent to all parallel stages. This means that the data is not divided or partitioned in any way, and each parallel stage receives the entire dataset.

Let's go through each option to understand why it is correct or incorrect:

Option A) Range - This option is incorrect because the "Range" partitioning method divides the data into ranges or intervals and assigns each range to a different parallel stage. This can increase the CPU cost as it requires additional computation to determine the ranges and assign data to each stage.

Option B) Modulus - This option is incorrect because the "Modulus" partitioning method uses a modulo operation to assign data to different parallel stages based on the remainder of a division. This can also increase the CPU cost as it requires additional computation to determine the modulo and assign data to each stage.

Option C) Entire - This option is incorrect because the "Entire" partitioning method sends the entire dataset to a single parallel stage. This can lead to a higher CPU cost as it requires a single stage to process the entire dataset.

Option D) Same - This option is correct because the "Same" partitioning method sends the same data to all parallel stages. This eliminates the need for data division or computation to determine ranges or modulo, resulting in the lowest CPU cost.

The correct answer is D) Same. This option is correct because it eliminates the need for data division or computation, resulting in the lowest CPU cost.

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To answer this question, you need to understand the different import options available for importing metadata from a data modeling tool such as Erwin or Rational Data Architect. Let's go through each option to understand why it is correct or incorrect:

Option A) Import using bridges - This option is correct because importing metadata from a data modeling tool can be done using bridges. Bridges are connectors that provide a seamless integration between different tools and platforms, allowing for the exchange of data and metadata.

Option B) Import a table definition using the Connector wizard - This option is incorrect because the Connector wizard is typically used for importing data from different sources, not for importing metadata from data modeling tools.

Option C) Import DataStage components - This option is incorrect because DataStage components are specific to IBM InfoSphere DataStage, a data integration tool, and not related to importing metadata from data modeling tools.

Option D) Import a table definition using the Plug-In Meta Data facility - This option is incorrect because the Plug-In Meta Data facility is a general term and does not specifically refer to importing metadata from data modeling tools.

The correct answer is A) Import using bridges. This option is correct because it allows for the import of metadata from data modeling tools like Erwin or Rational Data Architect using dedicated connectors or bridges.

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• Wrong explanation