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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| Topic 2: Integration with Third-Party Platforms | 15% | - VMware integration - Red Hat OpenShift and Kubernetes integration |
| Topic 3: High Performance Computing (HPC) Design and Management | 10% | - Management and monitoring - Architecture and scaling |
| Topic 4: HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
| Topic 5: Troubleshooting HPE Private Cloud AI | 10% | - Issue diagnosis and resolution - Performance tuning and optimization |
| Topic 6: NVIDIA AI Enterprise and Model Serving | 10% | - Software deployment and management - Model serving architecture and optimization |
| Topic 7: AI Workshops and HPE AI Essentials Platform | 15% | - Platform capabilities and deployment - Customer engagement and assessment |
| Topic 8: HPE ProLiant Gen12 for AI | 20% | - AI-optimized compute design - GPU integration and configuration - Server architecture and components |
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NEW QUESTION # 70
What is one innovation in NVIDIA Blackwell GPUs?
Answer: A
Explanation:
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.
NEW QUESTION # 71
A customer has HPE ProLiant DL Gen12 servers managed by HPE Compute Ops Management and acting as VMware vSphere ESXi hosts. How can you handle updating the servers' firmware and OS together?
Answer: A
NEW QUESTION # 72
A hospital wants to fine-tune an LLM on their own dataset. However, the data needs to have sensitive information redacted. Duplicate records also need to be cleaned up. What can help the customer use to achieve this goal?
Answer: C
Explanation:
NeMo Curator is used to prepare datasets for LLM training and fine-tuning, including data cleaning, filtering, sensitive data redaction, and deduplication. MLflow tracks experiments, NeMo Customizer fine-tunes models, and Katib handles hyperparameter tuning.
NEW QUESTION # 73
Which correctly characterizes HPE ProLiant Compute XD685 servers?
Answer: A
Explanation:
B is correct. HPE ProLiant Compute XD685 is a purpose-built accelerated system for large AI training and tuning workloads. Current HPE configurations support eight NVIDIA Blackwell-family GPUs, including B200 and Blackwell Ultra options, in a dense direct-liquid-cooled 5U chassis. The platform also uses HPE Integrated Lights-Out (iLO) for secure server-level management, giving it the familiar HPE management and security foundation expected in enterprise deployments.
Option A is incorrect in processor count, GPU generation, and management characterization. The XD685 is not a four-processor/four-GPU Lovelace design with a choice between unrelated management controllers.
Option C describes a multi-node scale-out chassis architecture that does not match the XD685; the 5U system is a single accelerated server platform with eight GPUs. Option D similarly invents an eight-processor, 8 TB scale-up design.
The exam objective is not merely to memorize a model name. The XD685 is positioned where workload requirements demand very high GPU density, large-model training capability, fast accelerator interconnects, and enterprise serviceability.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 1 "HPE solutions for AI"; HPE ProLiant Compute XD685 product specifications and current HPE Blackwell platform materials.
NEW QUESTION # 74
An HPE Private Cloud AI User wants to use KServe to run a NIM that is available in NGC.
What is one prerequisite?
Answer: C
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 # 75
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