A container hosts mixed production VMs. Both compression and deduplication are enabled. During data-reduction jobs, node CPU and memory use rises, and capacity savings are modest for some VMs. Which condition best justifies keeping both features enabled on this container?
Select an answer to reveal the explanation.
Short Explanation
Think of dedupe and compression like folding and vacuum-sealing laundry: both save space, but they don't help random, already compressed data. If your workload has duplicate blocks and compressible files, both can pay off; if not, you're just spending CPU and memory for little gain. The trap is assuming more data reduction is always better.
Full Explanation
Nutanix AOS can apply compression and deduplication to the same storage container, but each technique targets a different kind of redundancy. Deduplication removes repeated blocks, while compression reduces entropy within unique blocks. When a workload contains both duplicate data and compressible content, enabling both can improve effective capacity. When data is already compressed, encrypted, random, or highly unique, the additional CPU and memory cost of running these jobs may exceed any capacity benefit. A wrong claim is that both features should be enabled on every container regardless of workload; that ignores data entropy and resource overhead. Another wrong claim is that compression must be disabled whenever deduplication is enabled; AOS supports them together and they are not mutually exclusive. A further wrong claim is that higher replication factors block data reduction; replication factor controls fault tolerance and does not inherently disable compression or deduplication. Exam caveat: do not treat data reduction as universally beneficial—evaluate workload data patterns and node resources before enabling multiple techniques. Operational check: review container-level data reduction metrics and CPU/memory trends after enabling compression and deduplication, then compare effective capacity gains against performance impact.