HP HPE0-J82 real exam prep : HPE Storage Architect

A Storage Architect is evaluating an HPE GreenLake for File Storage deployment supporting a genomics research environment.
Requirements:
12 PB dataset
Linux compute nodes
POSIX semantics
Multi-GB/s throughput
Minimal application modification
Which THREE statements accurately support the recommended architecture? (Choose 3.)

• A. Fibre Channel storage is mandatory for all workloads exceeding one petabyte
• B. NFS over RDMA can provide POSIX-compliant access while reducing traditional networking overhead
• C. Existing applications can continue utilizing standard file system interfaces
• D. RDMA-capable networking can improve throughput efficiency and reduce CPU consumption
• E. Object storage APIs are required for POSIX-compliant applications

A customer is evaluating HPE GreenLake for File Storage for an AI training platform.
Application Requirements:
Linux compute cluster
15 PB of unstructured training data
POSIX compliance
Multi-GB/s throughput per node
Minimal application modifications
Which THREE architectural statements are accurate? (Choose 3.)

• A. Existing applications can continue utilizing standard file APIs without requiring object-storage rewrites
• B. Fibre Channel is mandatory for AI environments larger than 10 PB
• C. SMB provides superior performance for Linux-based AI training workloads
• D. RDMA-capable networking can improve throughput efficiency and reduce CPU overhead
• E. NFS over RDMA can preserve POSIX semantics while reducing traditional TCP/IP overhead

An Infrastructure Capacity Planner is analyzing the capacity forecast dashboard for a heavily utilized HPE Alletra Storage MP array via Data Ops Manager.
[Data Ops Manager - Capacity Forecast Dashboard]
Volume Group: VMFS_DevTest_Clones
Logical Capacity Limit: 100 TB
Logical Consumed: 95 TB (95% Full)
Physical Capacity Consumed: 15 TB
Current Data Reduction Ratio: 6.3:1
Predicted Time to Physical Exhaustion: > 24 Months
Predicted Time to Logical Exhaustion: 5 Days
Which immediate, zero-cost operational action must the planner take based on this dashboard analysis to prevent an application outage?

• A. Immediately execute an array-wide space reclamation (UNMAP) command to physically erase the
  95 TB of logically consumed blocks
• B. Generate an emergency purchase order for a new NVMe expansion shelf to mitigate the impending 5-day exhaustion window
• C. Disable inline deduplication on the DevTest clones to relieve the metadata overhead and artificially extend the logical exhaustion date
• D. Edit the volume group properties in Data Ops Manager to increase the Logical Capacity Limit from
  100 TB to 150 TB

A Customer Success Manager is reviewing a Data Ops Manager dashboard with a client. The client is confused because the dashboard shows a massive discrepancy between two capacity metrics on their HPE Alletra array.
[Data Ops Manager - Capacity Overview]
Total Logical Space Provisioned: 150 TB
Total Physical Space Consumed: 25 TB
The client asks: "If I have provisioned 150 TB of volumes to my VMware hosts, how is it physically possible that the array only shows 25 TB of actual disk space being used?" Which core architectural feature of modern HPE storage arrays natively creates this mathematical discrepancy, and how should the Customer Success Manager explain it?

• A. The array uses Thin Provisioning and Inline Data Reduction (Deduplication and Compression); the hosts believe they have 150 TB of space (Logical), but because the data is highly compressible, deduplicated, and largely empty (zeroes), it only occupies 25 TB of actual flash media (Physical)
• B. The VMware ESXi hosts have crashed and temporarily lost their Fibre Channel zoning, causing the array to logically unmount the volumes and report the physical space as unconsumed
• C. The Data Ops Manager only displays physical capacity consumed by the active array controller (Node 0); the remaining 125 TB is safely stored on the standby controller (Node 1) and will not appear in the dashboard until a failover occurs
• D. The array utilizes background automated tiering, moving 125 TB of cold data to an external S3 object bucket that is not tracked by the local Data Ops Manager dashboard

A Storage Administrator is restructuring the QoS policies for a multi-tenant service provider environment hosted on an HPE Primera array. The architect wants to ensure strict performance SLAs are met for all premium tenants while preventing the array from becoming oversubscribed.
[Proposed QoS Policy Configuration]
Total Array Capacity Limit: 100,000 IOPS (Based on sizing)
Tenant A (Premium): Min IOPS = 40,000
Tenant B (Premium): Min IOPS = 40,000
Tenant C (Premium): Min IOPS = 30,000
Tenant D (Standard): Min IOPS = NONE, Max IOPS = 10,000
Which TWO architectural anti-patterns will result from implementing this specific QoS configuration? (Choose 2.)

• A. The combined Minimum IOPS guarantees (110,000) mathematically exceed the physical capabilities of the array (100,000), creating an impossible SLA scenario during peak contention
• B. During periods of heavy utilization, the array controllers will be forced to arbitrary throttle one or more Premium tenants, causing them to breach their guaranteed 40,000 IOPS SLAs
• C. Tenant D's configuration violates multi-tenancy design because Standard tier workloads must strictly utilize fixed latency targets rather than IOPS caps
• D. Setting the Max IOPS limit for Tenant D to exactly 10,000 will permanently disable the array's automated tiering engine for that specific volume set
• E. The configuration will fail validation because QoS policies are restricted to managing bandwidth (MB/s) and cannot be applied directly to IOPS metrics on HPE Primera arrays