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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Solution Design for Enterprise Workloads | 30% | - Design for AI, HPC and mission-critical workloads - Requirements analysis and environment assessment - Scalability, availability and performance optimization |
| Topic 2: Implementation and Lifecycle Management | 15% | - Deployment planning and validation - Upgrade, maintenance and monitoring |
| Topic 3: Integration with Data Center Infrastructure | 20% | - HPE OneView management and automation - Networking and storage integration - Security, compliance and data protection |
| Topic 4: HPE Compute Architecture and Technologies | 25% | - Virtualization and container integration - HPE ProLiant, Apollo and Superdome Flex platforms - Compute component selection: processors, memory, storage, accelerators |
| Topic 5: Customer Success and Solution Optimization | 10% | - Troubleshooting and continuous improvement - Solution demonstration and value justification |
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NEW QUESTION # 57
A VMware customer wants firmware and driver updates for ProLiant hosts to be orchestrated by vSphere Lifecycle Manager together with ESXi updates.
What must be deployed on the hosts?
Answer: D
Explanation:
vSphere Lifecycle Manager delegates firmware remediation to a registered hardware support manager. iSUT running on the ESXi hosts, together with the HPE hardware support manager provided by OneView or Compute Ops Management, enables coordinated firmware and driver remediation in a single workflow.
NEW QUESTION # 58
An automotive engineering team runs inference workloads for traffic simu-lation and computer-vision models with KServe on HPE Private Cloud AI.
The team also uses GPU-enabled notebooks on HPE Private Cloud AI to test new models. They need GPU usage for the different models to adjust automatically according to current demand, while ensuring the notebooks never keep inferencing workloads from obtaining the GPUs they require.
Which two features support this functionality?
Choose two
Answer: A,C
Explanation:
The requirements involve two complementary capabilities: automatically adjusting inference-serving capacity and prioritizing GPU allocation between competing workloads.
KServe autoscaling, represented by option B, enables inference deployments to adjust their replica counts according to configured scaling policies and demand. This helps the environment allocate inference-serving resources efficiently as traffic changes.
HPE AI Essentials provides model deployment and autoscaling configuration capabilities through its integrated inference-serving infrastructure. Administrators can define supported scaling thresholds, replica limits, and resource requirements.
HPE AI Essentials scheduler priorities, represented by option D, establish the relative importance of different workload categories. Production inference workloads can be assigned higher priority than experimental notebooks.
When GPU resources become constrained, the scheduling framework uses these priority settings to determine allocation and preemption decisions. Higher-priority inference workloads can therefore take precedence over lower-priority notebook workloads, subject to the scheduler's supported preemption behavior.
Option A is not the primary mechanism for demand-based inference scaling or GPU priority management.
Option C concerns controlled application-version rollouts and traffic distribution rather than automatic resource allocation.
The combined use of KServe autoscaling and scheduler priorities addresses both the changing inference demand and the requirement to prioritize production workloads.
Official HPE Reference: HPE AI Essentials Software - GPU Resource Management; GPU Scheduling Workload Scenarios; MLIS Object Model Reference Schema - Deployment AutoScaling.
HPE AI Essentials Software 1.7.x Documentation
NEW QUESTION # 59
A customer wants to understand more about how HPE AI Essentials secures communications between pods running on HPE AI Essentials.
Which correctly describes how these pods authenticate each other when establishing TLS communications?
Answer: A
Explanation:
HPE AI Essentials uses SPIRE/SPIFFE identities for workload authentication. Pods obtain SVIDs from the built-in SPIRE server, which are then used to establish authenticated TLS communication between workloads.
NEW QUESTION # 60
Which component included with HPE Private Cloud AI can customers use to automate a data pipeline based on a sequence of Spark applications?
Answer: A
Explanation:
Apache Airflow is the workflow orchestration framework included with HPE Private Cloud AI that enables organizations to automate data pipelines consisting of multiple dependent processing tasks.
Airflow represents workflows as Directed Acyclic Graphs (DAGs). Each DAG defines individual tasks, their execution dependencies, and the conditions under which subsequent tasks can execute.
For a pipeline containing multiple Apache Spark applications, administrators can create an Airflow DAG that submits each application in the required sequence.
For example, the first Spark application might ingest raw data from object storage, the second might perform cleansing and transformation, and the third might generate analytical datasets or features for machine learning.
Airflow coordinates these tasks, monitors execution, handles supported retries, and supports scheduled or manually triggered workflows.
HPE AI Essentials integrates Airflow with the Spark Operator and provides documented examples of DAGs that submit Spark applications. The platform also supports loading workflow definitions from Git repositories.
Option B is incorrect because HPE Machine Learning Inferencing Software focuses on model deployment and inference management.
Option C provides distributed computing capabilities but is not the primary workflow orchestration component described.
Option D supports experiment tracking and machine learning lifecycle management rather than sequencing Spark processing jobs.
Therefore, Apache Airflow is the appropriate component for automating a Spark-based data pipeline.
Official HPE Reference: HPE AI Essentials Software - Using DAGs to Submit Spark Applications; Financial Time Series Workflow; HPE AI Essentials: Core Concepts - Data Engineering and Analytics Tools.
HPE AI Essentials Software 1.12.x Documentation
NEW QUESTION # 61
You need to demonstrate HPE Private Cloud AI running a complex AI application, such as a video search app, for a customer.
What is one useful resource for assembling this demonstration more quickly?
Answer: D
Explanation:
NVIDIA AI Blueprints provide reusable application architectures, implementation resources, and workflows that accelerate the development and demonstration of complex enterprise AI applications.
These blueprints combine multiple AI components into structured reference implementations, reducing the amount of custom development required to demonstrate a complete application.
For a video search application, the architecture may incorporate video ingestion, multimodal processing, embedding generation, vector-based retrieval, and large language model inference.
NVIDIA provides a Video Search and Summarization Blueprint designed for applications that analyze video content and respond to natural-language queries about recorded events.
HPE Private Cloud AI integrates NVIDIA AI Enterprise capabilities and supports deployment workflows built around NVIDIA NIM microservices and associated blueprints.
This allows the solution architect to demonstrate a realistic AI application without building every orchestration component, model service, and integration mechanism from scratch.
Option A focuses specifically on Apache Airflow orchestration examples rather than complete AI applications.
Option B may contain useful platform tutorials but is not the primary reference resource for rapidly assembling the described complex use case.
Option C concerns the CUDA software ecosystem rather than packaged, end-to-end AI application architectures.
NVIDIA AI Blueprints therefore offer the most direct route to a comprehensive customer demonstration.
Official HPE Reference: HPE Private Cloud AI - NVIDIA AI Blueprints; HPE AI Essentials 1.11.x - NVIDIA Blueprints; HPE NVIDIA Building AI Agents with Multimodal Models - Video Search and Summarization.
support.hpe.com
NEW QUESTION # 62
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