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| Section | Weight | Objectives |
|---|---|---|
| NetApp AI Solutions and Architecture | 25% | - Data management and data pipeline design - ONTAP integration with AI frameworks - NetApp AI-ready infrastructure components - Storage architectures for AI workloads - Scalability and performance optimization for AI |
| AI Lifecycle | 27% | - AI governance, ethics, and compliance - AI lifecycle stages: design, training, deployment, monitoring - Predictive vs generative AI - Model training, inference, and optimization - Data preparation and management for AI |
| Cloud and Hybrid Cloud AI Deployment | 18% | - Cloud-native AI solutions and integration - NetApp cloud data services for AI - Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures |
| Security, Reliability, and Operations | 15% | - High availability and data protection - Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments - Cost management and efficiency |
| AI Overview | 15% | - AI, machine learning, and deep learning concepts - AI deployment models: on-premises, cloud, edge - Algorithm types: supervised, unsupervised, reinforcement learning - AI industry use cases and applications - Convergence of AI, high-performance computing, and analytics |
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NEW QUESTION # 29
The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?
Answer: C
NEW QUESTION # 30
An MLOps engineer is troubleshooting a failed Kubeflow pipeline step. The step was designed to create a clone of a dataset for a training job using the NetApp DataOps Toolkit. The pod logs for the failed pipeline step show the following:
Traceback (most recent call last):
File "create_clone.py", line 15, in <module>
clone_pvc(source_pvc_name="training-data-v2", new_pvc_name="train-job-34a-data") NameError: name 'clone_pvc' is not defined The engineer reviews the Python script for the pipeline step:
# create_clone.py
import os
from netapp_dataops.k8s import create_pvc
# Other code
print("Cloning source dataset for training run...")
clone_pvc(
source_pvc_name="training-data-v2",
new_pvc_name="train-job-34a-data"
)
print("Clone created successfully.")
What is the cause of the error?
Answer: A
NEW QUESTION # 31
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)
Answer: A,B,C,E
NEW QUESTION # 32
Which AI technology is used to generate new, never-before-seen content such as images or text?
Answer: D
NEW QUESTION # 33
The HPC cluster generates simulation data at an extremely high rate, requiring a storage system that can handle massively parallel writes from hundreds of compute nodes simultaneously. Which storage system and file protocol combination is the most appropriate choice for the HPC cluster's high-performance scratch space?
Answer: C
NEW QUESTION # 34
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