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| Section | Weight | Objectives |
|---|---|---|
| HPE AI Essentials: Data Analytics and Data Science | 15% | - Analytics and machine learning tools - Data pipelines and processing |
| Solution Optimization and Security | 5% | - Efficiency and resilience - Security best practices |
| High Performance Computing (HPC) Design and Management | 10% | - Management and monitoring - Architecture and scaling |
| HPE ProLiant Gen12 for AI | 20% | - AI-optimized compute design - Server architecture and components - GPU integration and configuration |
| Integration with Third-Party Platforms | 15% | - Red Hat OpenShift and Kubernetes integration - VMware integration |
| NVIDIA AI Enterprise and Model Serving | 10% | - Model serving architecture and optimization - Software deployment and management |
| AI Workshops and HPE AI Essentials Platform | 15% | - Platform capabilities and deployment - Customer engagement and assessment |
| Troubleshooting HPE Private Cloud AI | 10% | - Issue diagnosis and resolution - Performance tuning and optimization |
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NEW QUESTION # 19
An integrator is proposing a two-node HPE SimpliVity hyperconverged solution for a remote office. Which of the following is a core benefit of HPE SimpliVity's architecture that should be highlighted?
Answer: A
Explanation:
HPE SimpliVity performs global inline deduplication and compression at the source (via the OmniStack accelerator) and includes native, VM-centric backup, restore, and DR capabilities without a separate backup appliance. It integrates directly with VMware vCenter for management (Hyper-V support is more limited/legacy), so C and D are incorrect, and A contradicts its built-in data protection design.
NEW QUESTION # 20
Which HPE offering allows an integrator to model, size, and validate a proposed HPE compute and storage solution against a customer's workload requirements before purchase?
Answer: A
Explanation:
HPE Sizer tools let partners and integrators model workload requirements (compute, storage, networking) and generate validated bill-of-materials recommendations. HPE Power Advisor specifically estimates power/cooling requirements for a given configuration, SPOCK validates compatibility rather than sizing, and Smart Update Manager handles firmware/driver updates post-purchase.
NEW QUESTION # 21
Which correctly describes an aspect of the AI software stack for HPE Cray XD systems?
Answer: D
Explanation:
D is correct. HPE Cray XD systems use a conventional layered management model: platform firmware is managed through standards-based interfaces such as Redfish, while the host operating environment is Linux- based. This separation is appropriate for HPC because firmware lifecycle, hardware telemetry, node provisioning, and operating-system management are distinct administrative functions even when they are coordinated by cluster-management tooling.
Option A overstates the role of HPE GreenLake. HPE Cray XD environments are not fundamentally managed as a multi-system HPC control plane through GreenLake. Option B misnames and mischaracterizes HPE Performance Cluster Manager (HPCM); HPCM is cluster provisioning and management software, not the AI development environment itself. Option C is false because HPE does not provide a single combined firmware- and-OS image that replaces the normal platform firmware and Linux software stack.
An integrator should understand both layers: Redfish/BMC interfaces expose node hardware management and telemetry, while Linux and HPC software provide the runtime environment for schedulers, MPI, accelerators, storage clients, and applications.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 9 "HPE Cray management" and
"HPE Cray firmware and software"; HPE Cray XD management documentation.
NEW QUESTION # 22
An HPE Private Cloud AI customer has a model deployed with HPE Machine Learning Inference Software (MLIS). You have helped the customer further fine-tune that model. Now the customer would like to test the new model by directing 10% of traffic to it. What should you do?
Answer: C
Explanation:
MLIS supports canary rollout for a new model version by assigning a traffic percentage, such as
10%, to the canary while the existing model continues receiving the remaining traffic.
NEW QUESTION # 23
An HPE Private Cloud AI customer wants to prevent Airflow from consuming too many resources on the cluster.
What is one approach you can take to fulfill this request?
Answer: B
Explanation:
D is correct. Airflow pools provide a direct mechanism for controlling task concurrency. Each pool has a defined number of slots, and tasks assigned to that pool consume slots while they execute. Reducing the number of slots in the default pool therefore limits the number of tasks that can run simultaneously and places an upper bound on how aggressively Airflow can consume cluster resources across concurrent DAG activity.
Option A is not the supported control in this scenario; the Airflow UI does not serve as the general editor for executor-pod CPU and memory sizing. Option B is the opposite of the stated objective because enabling autoscaling can increase executor capacity in response to demand. Option C suggests changing framework values for executor resources, but HPE's Airflow integration historically constrains those settings and the exam-tested method for limiting aggregate workload pressure is pool concurrency.
This is a scheduling-level safeguard rather than a Kubernetes quota. In a production environment, pool slots can be combined with platform quotas and workload resource requests to create layered control over resource consumption.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 "Airflow" and Module 7 resource troubleshooting; HPE AI Essentials Airflow documentation, pool slots and maximum simultaneous jobs.
NEW QUESTION # 24
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