| 1. We can easily compile and optimize the application we want to deploy for 64-bit processing. | |
|---|---|
| TRUE | FALSE |
| 1. | 2. |
| 2. Others have used this or a similar application successfully on 64-bit platforms. | |
| TRUE | FALSE |
| 1. | 2. |
| 3. Floating-point calculations comprise a large part of what the application does. | |
| TRUE | FALSE |
| 1. | 2. |
| 4. The application sometimes needs to hold more than 4 GB of memory on the processor at a given time. | |
| TRUE | FALSE |
| 1. | 2. |
| 5. Our technology staff has experience working with 64-bit platforms. | |
| TRUE | FALSE |
| 1. | 2. |
| 6. The number of users or transactions for this application could increase rapidly to reach volumes that we cant predict. | |
| TRUE | FALSE |
| 1. | 2. |
| 7. The application incorporates a security gateway that must process complex encryption algorithms instantaneously. | |
| TRUE | FALSE |
| 1. | 2. |
| 8. Multithreading isnt fast enough; we need a platform that can run queries in parallel. | |
| TRUE | FALSE |
| 1. | 2. |
| 9. Its quite likely that we will soon need a server with more than eight processors. | |
| TRUE | FALSE |
| 1. | 2. |
| 10. Our applications are highly specialized or technical. | |
| TRUE | FALSE |
| 1. | 2. |

AI success depends on whether enterprise data is ready, reachable, and close enough to the workloads that need it. In this eSpeaks episode, Dell Technologies’ Vrashank Jain explains why fragmented data, weak metadata, slow pipelines, and poor data locality can stall AI projects before models ever reach production.

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