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| Section | Weight | Objectives |
|---|---|---|
| AI Lifecycle | 27% | - AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance - Predictive vs generative AI - Model training, inference, and optimization - Data preparation and management for AI |
| Security, Reliability, and Operations | 15% | - Monitoring, logging, and troubleshooting AI environments - Cost management and efficiency - High availability and data protection - Data security and access control for AI |
| NetApp AI Solutions and Architecture | 25% | - NetApp AI-ready infrastructure components - Scalability and performance optimization for AI - Data management and data pipeline design - ONTAP integration with AI frameworks - Storage architectures for AI workloads |
| Cloud and Hybrid Cloud AI Deployment | 18% | - Data mobility and consistency across environments - Cloud-native AI solutions and integration - NetApp cloud data services for AI - Hybrid and multi-cloud AI architectures |
| AI Overview | 15% | - AI deployment models: on-premises, cloud, edge - AI, machine learning, and deep learning concepts - AI industry use cases and applications - Algorithm types: supervised, unsupervised, reinforcement learning - Convergence of AI, high-performance computing, and analytics |
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NEW QUESTION # 68
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?
Answer: B
NEW QUESTION # 69
A data science team reports that their Jupyter notebook pod, which was previously working, is now failing to start. The pod's status is 'CrashLoopBackOff'. An MLOps engineer investigates and finds that the pod's PersistentVolumeClaim (PVC) is bound, but the pod logs show a "Permission denied" error when trying to write to its '/data' mount point.
The engineer checks the Trident backend configuration associated with the pod's StorageClass:
apiVersion: trident.netapp.io/v1
kind: TridentBackendConfig
metadata:
name: ontap-nas-eco
spec:
version: 1
storageDriverName: ontap-nas
managementLIF: 10.10.20.5
dataLIF: 10.10.20.10
svm: svm-prod-ds
exportPolicy: read-only-policy
What is the most likely cause of the "Permission denied" error?
Answer: D
NEW QUESTION # 70
What is the primary architectural advantage of using a NetApp AIPod with NVIDIA DGX servers for the AI training cluster, as described in the scenario?
Answer: D
NEW QUESTION # 71
A data scientist on the team wants to run an experiment on a new vector indexing strategy. To do this, they need a temporary, writable copy of the main vector database. They use the NetApp DataOps Toolkit for Python to automate this process.
The following script is executed:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="prod-vector-db-pvc",
new_pvc_name="exp-indexing-clone",
namespace="ds-prod"
)
Assuming the source PVC exists and the user has correct permissions, what is the primary benefit of using this method?
Answer: B
NEW QUESTION # 72
Which of the following platforms provides tools for model training and deployment specifically for AI workloads?
Answer: C
NEW QUESTION # 73
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