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HP HPE7-S01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Explain HPE Compute AI and HPC Solution Components and Architecture | 38% | - HPE Compute portfolio
|
| Topic 2: Manage Implementation and Startup of HPE AI and HPC Solutions | 2% | - Deployment and validation
|
| Topic 3: Design HPE Solutions for AI and HPC Workloads | 50% | - Solution architecture and design
|
| Topic 4: Demonstrate AI Solutions | 10% | - AI solution demonstration
|
HP Advanced HPE Compute Architect Solutions Written Sample Questions:
1. What is one reason to suggest a VMware vSAN OSA deployment rather than an ESA deployment?
A) The customer prioritizes obtaining the highest write performance.
B) The customer wants to use features such as compression, which are not supported with ESA.
C) The customer wants to create a vSAN cluster that provides storage for other clusters but does not host VMs.
D) The customer wants a cost-effective solution that incorporates HDDs.
2. Which statement correctly describes hardware security capabilities for HPE Cray XD systems?
A) BMC software integrates with an iLO ASIC to provide hardware root of trust capabilities.
B) The HPE Cray XD systems include a sidecar node dedicated to security functions, such as monitoring firmware and OS for compromise.
C) The HPE Cray XD systems use the same iLO ASIC and firmware as the HPE ProLiant DL line.
D) A BMC ASIC enables secure boot any root of trust capabilities similar to those in HPE iLO.
3. You are demonstrating HPE Private Cloud AI and HPE Machine Learning Inference Software to a customer. The customer says that the organization has a custom model, stored in an OpenLLM hub, which they would like to deploy with HPE Machine Learning Inference Software.
What should you explain?
A) While HPE Machine Learning Inference Software does not support deploying custom models, Ray Serve, also provided by HPE Private Cloud AI, does.
B) HPE Machine Learning Inference Software can deploy this model if the customer packages it with Bento Archive and saves it to a PVC in HPE Private Cloud AI.
C) HPE Machine Learning Inference Software supports pulling this model from the hub and deploying it.
D) While HPE Machine Learning Inference Software does not support deploying custom models, KServe, also provided by HPE Private Cloud AI, does.
4. A customer asks about the options HPE Private Cloud AI includes out of the box for hyperparameter optimization (HPO) during fine-tuning.
What is one thing that you should explain?
A) Users can run Katib experiments using the built-in Kubeflow framework, which includes several predefined trial templates.
B) Administrators must expose the built-in Kuberay head service port 10001 externally in order to support Ray Tune.
C) Administrators must install a Livy server with which users can run interactive HPO sessions.
D) Users can apply HPO to any training code by uploading the code to the MLflow UI accessible from HPE AI Essentials out of the box.
5. An HPE Private Cloud AI customer needs help planning how to use Airflow within that solution.
The customer currently has several GitHub repos in which different teams store their DAGs.
What should you advise AI administrators to do?
A) Set up one Github repo, to which they will refer the Airflow framework, with the existing GitHub repos added as submodules.
B) Create a recurring Spark pipeline to copy the DAGs from each GitHub to the Shared Data Volume from which Airflow pulls the DAGs.
C) Replicate the built-in Airflow framework to the namespace of each user with an existing GitHub repo.
D) Configure the Airflow framework to reference each existing GitHub repo using individual secrets for the credentials.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: A |
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