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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Workshops and HPE AI Essentials Platform | 15% | - Customer engagement and assessment - Platform capabilities and deployment |
| Topic 2: Integration with Third-Party Platforms | 15% | - Red Hat OpenShift and Kubernetes integration - VMware integration |
| Topic 3: Troubleshooting HPE Private Cloud AI | 10% | - Issue diagnosis and resolution - Performance tuning and optimization |
| Topic 4: High Performance Computing (HPC) Design and Management | 10% | - Architecture and scaling - Management and monitoring |
| Topic 5: NVIDIA AI Enterprise and Model Serving | 10% | - Software deployment and management - Model serving architecture and optimization |
| Topic 6: HPE ProLiant Gen12 for AI | 20% | - AI-optimized compute design - Server architecture and components - GPU integration and configuration |
| Topic 7: HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
| Topic 8: Solution Optimization and Security | 5% | - Efficiency and resilience - Security best practices |
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NEW QUESTION # 50
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: B
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 # 51
An HPE Private Cloud AI customer has a model deployed with HPE Machine Learning Inference Software (MLIS). You have helped the customer further fine-tune that model. Now the customer would like to test the new model by directing 10% of traffic to it. What should you do?
Answer: D
Explanation:
MLIS supports canary rollout for a new model version by assigning a traffic percentage, such as
10%, to the canary while the existing model continues receiving the remaining traffic.
NEW QUESTION # 52
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: A
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 # 53
What is one benefit of HPE Slingshot for HPC workloads?
Answer: D
Explanation:
B is correct. HPE Slingshot uses fine-grained adaptive routing to make efficient use of available fabric paths.
For traffic that can tolerate reordering, the network can make routing decisions at packet granularity, steering packets away from congested links and distributing high-bandwidth MPI or storage traffic across the fabric.
This is particularly useful in HPC environments where compute collectives and Lustre I/O can create dynamic hotspots that are difficult to predict with static routing.
Option A is false because Slingshot is an Ethernet-based HPC interconnect, not InfiniBand NDR. Option C incorrectly characterizes its congestion management as primarily DiffServ-based; Slingshot uses specialized congestion-control and telemetry mechanisms designed for HPC traffic behavior. Option D overstates a topology requirement. HPC applications do not require every fabric to be constructed as a mathematically non- blocking network, and Slingshot's value includes maintaining performance through adaptive routing and congestion management even under variable traffic.
The design goal is sustained application throughput, not merely peak link speed. Efficient path use keeps compute nodes from idling behind network congestion.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 10 "HPE HPC networking solutions"; HPE Slingshot QuickSpecs and adaptive-routing documentation.
NEW QUESTION # 54
What is one way that NVIDIA Spectrum-X meets the needs for AI networks?
Answer: B
Explanation:
D is correct. NVIDIA Spectrum-X is engineered for Ethernet-based AI fabrics where synchronized GPU traffic can create short, extremely large bursts. Its congestion-management architecture uses high-frequency telemetry and flow metering to identify the sources contributing to congestion and react with much finer control than conventional static Ethernet behavior. This is especially valuable for RDMA/RoCE AI traffic, where packet loss, tail latency, and uneven use of parallel paths can materially reduce GPU utilization.
Option A reverses the relationship between ECN and PFC. Spectrum-X does not simply replace ECN with Priority Flow Control; modern AI Ethernet designs combine congestion signaling and control mechanisms to avoid relying on broad pause behavior. Option B is also inaccurate because static ECMP hashing is not the defining fine-grained balancing mechanism; Spectrum-X uses adaptive routing and telemetry-aware decisions. Option C describes dedicated per-port buffering as the basis for losslessness, which is not the principal Spectrum-X innovation.
The exam objective is to understand why an AI network differs from ordinary data-center Ethernet: the network must react rapidly to elephant flows and collective-communication bursts while keeping accelerated nodes productive.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, AI/HPC networking objectives; NVIDIA Spectrum-X Ethernet networking documentation, congestion control and telemetry.
NEW QUESTION # 55
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