To answer this question, let's go through each option to understand why it is correct or incorrect:
Option A) When choosing a primary index, you should pick a column with a severely limited value set.
- This option is incorrect. When choosing a primary index, it is generally recommended to pick a column with a broad value set to evenly distribute data across AMPs and optimize performance.
Option B) If distribution is skewed, an all-AMP operation will take longer than if all AMPs were evenly utilized.
- This option is correct. In Teradata, data distribution across AMPs plays a crucial role in achieving optimal performance. If the data distribution is skewed, meaning that some AMPs have significantly more data than others, an all-AMP operation will take longer as the workload is not evenly distributed.
Option C) Selecting a NUPI for storing data provides for maximum efficiency and makes the best use of the parallel features of Teradata.
- This option is incorrect. While selecting a NUPI (Non-Unique Primary Index) can provide some benefits in certain scenarios, it does not necessarily guarantee maximum efficiency or the best use of parallel features. The choice of primary index depends on various factors such as data distribution, workload, and query patterns.
Option D) The degree of uniqueness is not critical to efficiency.
- This option is incorrect. The degree of uniqueness in a primary index is critical to efficiency in Teradata. A highly unique primary index distributes the data evenly across AMPs, allowing for efficient parallel processing. On the other hand, a low degree of uniqueness can lead to skewed data distribution and inefficient query performance.
The correct answer is B) If distribution is skewed, an all-AMP operation will take longer than if all AMPs were evenly utilized. This option is correct because skewed data distribution can lead to imbalanced workloads and slower performance in Teradata.