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| Section | Objectives |
|---|---|
| Design and Implement HPE Private Cloud AI Solutions | - HPE Private Cloud AI Architecture
|
| Compute and Virtualization Solutions | - HPE Compute Platforms
|
| Operations, Security, and Troubleshooting | - Performance Optimization and Troubleshooting
|
| Kubernetes and MLOps Platforms | - AI and MLOps Services
|
| HPC and High-Performance Infrastructure | - HPC Networking
|
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NEW QUESTION # 53
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: A
Explanation:
A is correct. HPE Machine Learning Inference Software supports canary rollout behavior for a deployment when a new model version is introduced. A canary allows the current production version and the candidate version to run concurrently while a defined percentage of inference traffic is directed to each. Setting the new version to 10% gives the customer a controlled production test while retaining 90% of traffic on the established version.
Option B invents a "Canal" routing service and unnecessarily creates a separate deployment. Option C confuses autoscaling with traffic splitting; autoscaling changes replica capacity, not the percentage of requests routed to different model versions. Option D also misstates the model: traffic distribution is part of the versioned canary rollout rather than an arbitrary routing rule between two independently configured model versions in the existing deployment.
The canary mechanism reduces change risk because model quality, latency, error rate, and resource consumption can be compared before full promotion. If the candidate is satisfactory, its traffic share can be increased; if not, the rollout can be stopped or rolled back.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 5 "Serving models with HPE Machine Learning Inference Software"; HPE AI Essentials MLIS documentation, "Initiate Canary Rollout."
NEW QUESTION # 54
What is one way that NVIDIA Spectrum-X meets the needs for AI networks?
Answer: C
Explanation:
NVIDIA Spectrum-X uses telemetry-based congestion control with high-frequency telemetry probes and flow metering to protect AI workloads and maintain predictable performance. It improves on traditional ECMP with adaptive routing.
NEW QUESTION # 55
An HPE Private Cloud AI User wants to use KServe to run a NIM that is available in NGC.
What is one prerequisite?
Answer: D
Explanation:
D is correct. A NIM image hosted in NVIDIA NGC is protected by NVIDIA registry authentication. To allow KServe in a user namespace to pull that image, the user needs a valid NGC API key and a Kubernetes secret containing the corresponding registry credentials in the namespace where the InferenceService will run. The workload can then reference that secret for the image pull or other required NGC access.
Option A describes registering NGC with HPE Machine Learning Inference Software. That is relevant when MLIS is the serving mechanism, but the scenario explicitly uses KServe. Option B is not how NVIDIA AI Enterprise is made available to HPE AI Essentials; it is not imported as a user Helm chart. Option C is also invalid because KServe does not require special "decorators" in application code to launch a NIM.
Namespace placement matters because Kubernetes secrets are namespace-scoped. A secret created elsewhere is not automatically available to the user's KServe deployment.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 5 "Other options for serving models on HPE Private Cloud AI" and NVIDIA AI Enterprise; HPE AI Essentials KServe guidance; NVIDIA NGC authentication documentation.
NEW QUESTION # 56
A customer needs to deploy a NIM LLM with HPE Machine Learning Inference Software running on HPE Private Cloud AI (AI Essentials 1.6.1). What is one option for the image for serving the model?
Answer: B
Explanation:
For AI Essentials 1.6.1, NIM LLM serving images are handled as prepackaged/curated images in the HPE Private Cloud AI registry. Direct dynamic NGC delivery is associated with later AI Essentials releases, not 1.6.1.
NEW QUESTION # 57
An HPE Private Cloud AI customer wants to eliminate the certificate errors that show when users access HPE AI Essentials. The customer has created a certificate signed by their CA to replace the current certificate. The customer has given you the certificate file, private key file, and CA certificate file.
How should you install this certificate?
Answer: A
Explanation:
D is correct for a customer-supplied, already-issued certificate. HPE AI Essentials presents its external TLS identity through the Istio ingress gateway, and the relevant certificate material is held in the ingress-cert secret in the istio-system namespace. When performing the manual replacement workflow, the administrator replaces the existing ingress certificate secret with one populated from the supplied certificate, private key, and CA chain according to HPE's documented format.
Option A is incorrect because the certificate is not installed on a single UI virtual machine; HPE AI Essentials is a Kubernetes-based platform and ingress TLS is handled at the cluster ingress layer. Option B confuses certificate issuance with certificate installation. A ClusterIssuer is appropriate when cert-manager should request and renew a certificate, but the customer has already supplied the signed certificate and key. Option C incorrectly places TLS management in Keycloak.
Rev. 26.21 updated the certificate procedures for HPE AI Essentials 1.9 and later, so administrators should follow the version-specific HPE commands and secret format rather than an older generic Kubernetes recipe.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 3 "Replacing the Istio ingress gateway certificate"; HPE AI Essentials documentation, manual certificate replacement and ingress-cert access.
NEW QUESTION # 58
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