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| Section | Weight | Objectives |
|---|---|---|
| HPE ProLiant Gen12 for AI | 20% | - GPU integration and configuration - Server architecture and components - AI-optimized compute design |
| Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| Integration with Third-Party Platforms | 15% | - Red Hat OpenShift and Kubernetes integration - VMware integration |
| High Performance Computing (HPC) Design and Management | 10% | - Architecture and scaling - Management and monitoring |
| HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
| NVIDIA AI Enterprise and Model Serving | 10% | - Model serving architecture and optimization - Software deployment and management |
| AI Workshops and HPE AI Essentials Platform | 15% | - Customer engagement and assessment - Platform capabilities and deployment |
| Troubleshooting HPE Private Cloud AI | 10% | - Issue diagnosis and resolution - Performance tuning and optimization |
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NEW QUESTION # 65
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: D
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 # 66
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: C
Explanation:
Apache Airflow is the workflow orchestration component in HPE AI Essentials. It can automate and sequence Spark Applications as DAG-based data pipelines.
NEW QUESTION # 67
An HPE Private Cloud AI customer wants to prevent Airflow from consuming too many resources on the cluster.
What is one approach you can take to fulfill this request?
Answer: D
Explanation:
D is correct. Airflow pools provide a direct mechanism for controlling task concurrency. Each pool has a defined number of slots, and tasks assigned to that pool consume slots while they execute. Reducing the number of slots in the default pool therefore limits the number of tasks that can run simultaneously and places an upper bound on how aggressively Airflow can consume cluster resources across concurrent DAG activity.
Option A is not the supported control in this scenario; the Airflow UI does not serve as the general editor for executor-pod CPU and memory sizing. Option B is the opposite of the stated objective because enabling autoscaling can increase executor capacity in response to demand. Option C suggests changing framework values for executor resources, but HPE's Airflow integration historically constrains those settings and the exam-tested method for limiting aggregate workload pressure is pool concurrency.
This is a scheduling-level safeguard rather than a Kubernetes quota. In a production environment, pool slots can be combined with platform quotas and workload resource requests to create layered control over resource consumption.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 "Airflow" and Module 7 resource troubleshooting; HPE AI Essentials Airflow documentation, pool slots and maximum simultaneous jobs.
NEW QUESTION # 68
A customer needs to deploy a NIM LLM with HPE Machine Learning Inference Software running on HPE Private Cloud AI (AI Essentials 1.6.1). What is one option for the image for serving the model?
Answer: C
Explanation:
For AI Essentials 1.6.1, NIM LLM serving images are handled as prepackaged/curated images in the HPE Private Cloud AI registry. Direct dynamic NGC delivery is associated with later AI Essentials releases, not 1.6.1.
NEW QUESTION # 69
A customer wants to manage the full lifecycle of their HPE ProLiant Gen11 servers - including firmware updates, monitoring, and remote console access - from a single cloud-based console without deploying an on-premises management appliance. Which HPE solution should the integrator recommend?
Answer: B
NEW QUESTION # 70
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