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HP HPE7-S01 Exam Syllabus Topics:

HP Advanced HPE Compute Architect Solutions Written Sample Questions:

1. You are discussing HPE Private Cloud AI with a customer. The customer states that they need an easier way to visualize their analyses of data across multiple types of databases, such as SQL Server and MongoDB.
What should you explain?

A) The customer can add these databases as data sources in HPE AI Essentials. Users can then reference those sources in Knowledge Bases to run AI-based analytics and visualizations on them.
B) The customer can add these databases as data sources in HPE AI Essentials and then use that platform's built in Superset and EzPresto frameworks to visualize the data.
C) The customer can integrate databases as data volumes on HPE AI Essentials. The customer can then use Notebook servers included with HPE AI Essentials to browse and query the data.
D) The customer can synchronize these databases with GreenLake for File Storage, which is included as part of the Private Cloud AI solution. The customer can then use AI built into that solution to enrich the data with metadata and analyze it.

2. 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) 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.
B) While HPE Machine Learning Inference Software does not support deploying custom models, KServe, also provided by HPE Private Cloud AI, does.
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, Ray Serve, also provided by HPE Private Cloud AI, does.

3. 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) Configure the Airflow framework to reference each existing GitHub repo using individual secrets for the credentials.
C) Replicate the built-in Airflow framework to the namespace of each user with an existing GitHub repo.
D) Create a recurring Spark pipeline to copy the DAGs from each GitHub to the Shared Data Volume from which Airflow pulls the DAGs.

4. An HPE Private Cloud AI administrator is running multiple AI workloads and needs insight into which workloads are using resources.
Which HPE Private Cloud AI feature helps to deliver this visibility?

A) HPE GreenLake Cloud AI Insights
B) HPE AI Essentials GPU control panel
C) HPE Private Cloud AI system view
D) AI optimized nodes' iLO interfaces

5. A healthcare AI research team has a software stack for AI-based image analysis and LLM-based diagnostics. However, the team needs hardware that better supports this use case.
Which hardware option fulfills these technical requirements?

A) HPE Synergy 480 Gen12 with two 48-core processors
B) HPE ProLiant DL360 Gen12 with NVIDIA L4 GPUs and two 32-core processors
C) HPE ProLiant DL384 Gen12 with NVIDIA GH200 NVL2
D) HPE ProLiant DL580 Gen12 with four 32-core processors

Solutions: