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| Section | Objectives |
|---|---|
| HPE Storage Portfolio Overview | - HPE storage product families and positioning
|
| Data Protection and Availability | - Backup, replication, and disaster recovery
|
| Storage Management and Operations | - Storage provisioning and lifecycle management
|
| Storage Architecture and Design | - Core storage architecture concepts
|
| SAN and Connectivity Technologies | - Fibre Channel and iSCSI networking
|
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NEW QUESTION # 92
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?
Answer: B
NEW QUESTION # 93
A customer recently purchased multiple HPE StoreOnce appliances. They are experiencing slower performance than expected when performing backups to the new solution.
What should the customer do to improve performance?
Answer: B
Explanation:
Running multiple HPE StoreOnce backup jobs or streams in parallel generally increases aggregate ingest throughput compared with processing a single sequential data stream. StoreOnce appliances are designed for concurrent backup operations across supported Catalyst, VTL, and NAS configurations. A lone stream may fail to utilize available CPU, disk and network resources even when the appliance appears underperforming.
Dividing large backup workloads into multiple sessions or scheduling appropriate concurrent jobs helps use those resources more effectively. The number of sessions must remain within product limits, however; too much parallelism creates contention and can reduce throughput. Validating deduplication settings is worthwhile but not the direct optimization asked about. Matching unrelated allocation sizes is not a standard blanket fix, and spreading every backup protocol into entirely separate time windows may sacrifice available concurrency. A structured performance assessment should compare source-server throughput, target session counts, network saturation and StoreOnce resource utilization. The recommended general tuning action is to increase parallel backup activity in a controlled way.
Study Guide Reference: StoreOnce ingest performance, concurrent backup streams and throughput optimization.
Official reference(s): StoreOnce QuickSpecs
NEW QUESTION # 94
You are using the HPE ProLiant SSD Selector Tool. The customer requires a common protocol for authenticating and attesting to drives in an HPE server or storage system.
Which drive type should be chosen?
Answer: C
Explanation:
Security Protocol and Data Model (SPDM) is a DMTF standard for authenticating hardware components and obtaining cryptographically protected measurements of their security state. An SPDM requester, such as platform firmware or a management controller, communicates with an SPDM-capable responder, such as a supported SSD, to verify identity and request attestation measurements. The mechanism supports certificate- based authentication, integrity validation, and secure-session establishment. Its value is a common protocol across device categories rather than reliance on a proprietary storage-drive exchange. A Trusted Platform Module provides hardware-rooted cryptographic functions but is not itself the requested drive attestation protocol. Self-Encrypting Drives protect data at rest; a FIPS-qualified SED addresses cryptographic validation requirements. Neither designation alone provides the standardized device-to-platform exchange described in the scenario. Architects must confirm that both the host platform and selected SSD support the required SPDM version and associated authentication workflow. HPE server compatibility and firmware currency remain separate prerequisites for successful drive integration.
Study Guide Reference: Drive and platform security: SPDM, TPM, SED, FIPS and hardware attestation.
Official reference(s): SSD Selector | HPE Storage Solutions course
NEW QUESTION # 95
Which FC SAN feature restricts communications with devices in the same zone?
Answer: D
Explanation:
Peer zoning limits communication within a Fibre Channel zone through a principal-and-peer relationship.
Unlike an ordinary zone in which compatible members can generally communicate with each other, a peer zone permits the principal device to communicate with designated peer members but does not grant peer-to- peer communication merely because those peers share the zone. A common example is a storage target acting as principal with multiple host initiators assigned as peers. The hosts can reach the storage target without being given access to one another through the same zone definition. This preserves controlled initiator-to- target connectivity while potentially reducing the number of individually managed zones. Private VLANs are Ethernet segmentation features; fabric segmentation separates broader fabric domains, and ' private zoning ' is not the named FC peer-zone mechanism required by the question. Proper design also requires consistent WWPN identification, separate fabrics for redundancy, and verification that switch software supports the chosen zoning capability.
Study Guide Reference: Fabric zoning: principal/peer relationships, target/initiator segmentation.
Official reference(s): SAN Design Reference Guide
NEW QUESTION # 96
When examining the latency analysis information in HPE GreenLake Data Ops Manager, what will help a customer determine what might be causing latency issues?
Answer: A
Explanation:
Latency hotspot analysis identifies volumes or related storage objects that consistently display abnormal response times compared with comparable objects. In HPE GreenLake Data Ops Manager, hotspot visualizations help administrators isolate elevated latency within a selected observation period and distinguish recurring outliers from system-wide averages. This is valuable because averages can conceal short periods of severe degradation. A repeated hotspot may indicate queueing, workload contention, limited performance headroom, or an upstream host or networking problem. It is an investigation signal rather than proof of a single bottleneck. Workload drift describes changing I/O behavior, not necessarily the movement of a volume within a datastore. Lengthening time aggregation can sometimes obscure intermittent latency events. Simply knowing that averaging loses detail does not identify the specific Data Ops Manager diagnostic tool. The recommended practice is to inspect hotspots alongside reads, writes, throughput, IOPS, time of day and host metrics. Correlating these measurements supports a defensible diagnosis rather than attributing every delay to the array.
Study Guide Reference: Data Ops Manager latency hotspots, workload outliers and diagnostics.
Official reference(s): Workload Planner documentation
NEW QUESTION # 97
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