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| Section | Objectives |
|---|---|
| Compute and Virtualization Solutions | - HPE Compute Platforms
|
| HPC and High-Performance Infrastructure | - HPC Networking
|
| Operations, Security, and Troubleshooting | - Monitoring and Management
|
| Design and Implement HPE Private Cloud AI Solutions | - HPE Private Cloud AI Architecture
|
| Kubernetes and MLOps Platforms | - Kubernetes Infrastructure
|
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25. Frage
What is one innovation in NVIDIA Blackwell GPUs?
Antwort: B
Begründung:
NVIDIA Blackwell introduces a second-generation Transformer Engine with support for lower- precision AI formats such as FP4, improving LLM training and inference efficiency. The option likely says "Transformation Engine" but is referring to the Transformer Engine.
26. Frage
An HPE Private Cloud AI User wants to use KServe to run a NIM that is available in NGC. What is one prerequisite?
Antwort: A
Begründung:
A NIM from NGC requires authenticated access to pull/use the NIM artifacts. With KServe, the NGC API key must be available to the deployment as a Kubernetes secret in the namespace where the user deploys the InferenceService. NVIDIA's KServe NIM setup requires NIM access on NGC and an NGC_API_KEY.
27. Frage
Which statement about HPE Alletra storage integration with HPE compute solutions is most accurate?
Antwort: C
Begründung:
HPE Alletra is HPE's unified data storage brand spanning multiple platforms for block, file, and hybrid workloads, and it integrates with HPE GreenLake and InfoSight for AI-driven, cloud- operated management alongside compute resources, giving integrators a consistent management experience. It is not limited to block-only, edge-only, or fully siloed from compute management.
28. Frage
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?
Antwort: C
Begründung:
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.
29. Frage
You are demonstrating HPE AI Essentials to a customer. You want to show running a DAG (workflow).
Which framework or page should you access?
Antwort: B
Begründung:
D is correct. Airflow is the HPE AI Essentials framework used to define, schedule, and execute directed acyclic graphs. A DAG expresses a workflow as tasks plus dependencies, so the Airflow interface is the appropriate place to demonstrate launching and monitoring a workflow. The UI exposes DAG state, task instances, execution history, schedules, and operational details that make the orchestration behavior visible during a customer demonstration.
NVIDIA AI Enterprise is the broader enterprise AI software suite and includes components such as NIM and NeMo, but it is not the HPE AI Essentials DAG scheduler. MLflow is used for experiment tracking and model lifecycle activities. Ray is a distributed computing framework used for scalable Python and AI workloads; it does not provide the Airflow DAG-management interface requested here.
A useful demonstration can show both control and observability: trigger a DAG, inspect task dependency order, observe task status, and review logs or retries. That makes the distinction between workflow orchestration and the underlying execution frameworks clear.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 "Airflow" and the objective
"Demonstrate HPE AI Essentials"; HPE AI Essentials Software Airflow documentation.
30. Frage
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