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| Section | Objectives |
|---|---|
| Topic 1: Kubernetes and MLOps Platforms | - AI and MLOps Services
|
| Topic 2: Compute and Virtualization Solutions | - VMware and vSphere Integration
|
| Topic 3: HPC and High-Performance Infrastructure | - HPC Networking
|
| Topic 4: Operations, Security, and Troubleshooting | - Identity and Access Management
|
| Topic 5: Design and Implement HPE Private Cloud AI Solutions | - AI Infrastructure Integration
|
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NEW QUESTION # 17
You are planning to demonstrate how you can use KServe to run NIMs on HPE Private Cloud AI. You have downloaded the CodeLlama-70B-Instruct model to a volume in your namespace named demo-model.
The objects that you are planning to deploy are shown below:
What change should you make to the objects?
Answer: D
Explanation:
D is correct after correcting the YAML location to spec.predictor.model.storageUri. The ServingRuntime describes the runtime container capable of serving the model; the InferenceService describes the particular model instance to deploy. Because the CodeLlama model files already reside on a persistent volume claim named demo-model, the InferenceService must tell KServe where those model artifacts are stored. A PVC URI is therefore supplied through the model's storageUri field, for example pvc://demo-model.
Option A is structurally wrong because modelFormat.name identifies the model format understood by the ServingRuntime; it is not a storage location. Option B incorrectly assigns model storage to the ServingRuntime specification. Option C changes the NIM cache directory inside the container but does not tell KServe to mount and initialize the requested model from the PVC. KServe's v1beta1 schema places storageUri alongside modelFormat and runtime under the predictor's model block.
This distinction is fundamental to reusable KServe design: a runtime can serve multiple compatible models, while each InferenceService supplies the model-specific location and deployment parameters.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 5 "Other options for serving models on HPE Private Cloud AI"; HPE AI Essentials KServe documentation; KServe InferenceService v1beta1 schema.
NEW QUESTION # 18
You are proposing HPE Private Cloud AI to a financial institution and want to demo the solution running a complex AI-enabled security application. What can help you to accelerate the development of the demo?
Answer: A
Explanation:
NVIDIA Blueprints provide sample/reference AI applications that accelerate development of complex demos and enterprise AI workflows; they are available through NVIDIA AI Enterprise/NGC resources.
NEW QUESTION # 19
Which HPE Gen11 server component is specifically responsible for enforcing that only cryptographically signed firmware images can be flashed to the system, rejecting unsigned or tampered images?
Answer: D
Explanation:
The firmware verification mechanism within HPE's Silicon Root of Trust design validates the digital signature of firmware before it is allowed to run or be flashed, rejecting compromised images. The Security Override Switch is a physical jumper for recovery scenarios, Server Configuration Lock prevents unauthorized hardware/BIOS changes, and Chassis Intrusion Detection reports physical case-open events.
NEW QUESTION # 20
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: D
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 # 21
An AI team is using Spark Applications to analyze and format data for AI fine-tuning. The team would like to automate running the applications in a series. Which component of HPE AI Essentials can help?
Answer: B
Explanation:
Apache Airflow is the workflow orchestration component in HPE AI Essentials. It can automate and sequence Spark Applications as DAG-based data pipelines.
NEW QUESTION # 22
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