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| Section | Objectives |
|---|---|
| Topic 1: Server Deployment and Configuration | - Firmware and system configuration tools (iLO, SPP) - Initial setup and provisioning |
| Topic 2: HPE Compute Architecture and Solutions | - HPE ProLiant server families overview - Compute solution positioning and use cases |
| Topic 3: Storage and Networking Integration | - SAN and direct-attached storage integration - Networking configuration and connectivity options |
| Topic 4: Solution Design and Implementation | - High availability and redundancy design - Workload sizing and architecture planning |
| Topic 5: Troubleshooting and Support | - Performance monitoring and diagnostics - Hardware and firmware troubleshooting |
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NEW QUESTION # 36
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 the customer use to achieve this goal?
Answer: A
NEW QUESTION # 37
A customer has fine-tuned a model and wants to find out whether the model is producing any toxic content. Which NVIDIA microservice best meets this need?
Answer: D
Explanation:
NeMo Evaluator is used to assess customized or fine-tuned models against evaluation benchmarks, including toxicity benchmarks. NeMo Guardrails is mainly for enforcing runtime safety controls, not evaluating whether the model produces toxic content.
NEW QUESTION # 38
At the request of an HPE Private Cloud AI customer, you have added an AWS S3 object store to HPE AI Essentials. Several HPE Private Cloud AI Users should be able to access that source when running notebooks and Spark applications. Other users should have no access.
What should you do?
Answer: B
Explanation:
A is correct. HPE AI Essentials provides platform-level identity and access management for connected data sources. After the S3 object store is configured, an administrator can use the Identity and Access Management interface to grant the required users privileges to that object-store data source. This gives the selected users access from supported tools such as notebooks and Spark applications while leaving other users without permission.
The HPE GreenLake cloud user-management page controls broader workspace or service access, but it is not the place to assign the fine-grained HPE AI Essentials data-source privilege described in this scenario.
Embedding user lists directly in the object-store connection settings is not the documented access-control workflow. Creating credential secrets manually in every user namespace is also the wrong pattern: it duplicates sensitive credentials, increases rotation overhead, and bypasses the platform's centralized authorization model.
The operational principle is separation of connection configuration from authorization. The platform stores the data-source integration, while Identity and Access Management determines who may use it. That model is more auditable and maintainable than distributing access keys.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 "Connecting to data"; HPE AI Essentials Software documentation, Identity and Access Management and user access to data.
NEW QUESTION # 39
An AI team is using Spark Applications to analyze and format data for AI fine-tuning. The team would like to automate running the applications in a series.
Which component of HPE AI Essentials can help?
Answer: D
Explanation:
B is correct. Airflow is the workflow-orchestration component in HPE AI Essentials. It is designed to define directed acyclic graphs (DAGs) whose tasks execute in a controlled sequence based on dependencies. If a team has several Spark applications that prepare, transform, and validate data before model fine-tuning, Airflow can schedule those applications as stages in a repeatable pipeline and coordinate retries, dependencies, and execution order.
Katib and Ray Tune are focused on hyperparameter optimization rather than general-purpose workflow orchestration. MLflow tracks experiments, runs, artifacts, and model lifecycle information; it is not the scheduler that executes an ordered series of Spark jobs. Airflow complements these components rather than replacing them.
The key exam distinction is between orchestration and execution engines. Spark performs distributed data processing. Airflow controls when the Spark applications run and how one stage depends on another. That separation permits the data-preparation pipeline to be automated without embedding orchestration logic inside each Spark application.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 "Using Spark" and "Airflow"; HPE AI Essentials Software Airflow documentation.
NEW QUESTION # 40
How do workloads running within HPE AI Essentials authenticate each other?
Answer: D
NEW QUESTION # 41
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