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| Section | Weight | Objectives |
|---|---|---|
| Cloud and Hybrid Cloud AI Deployment | 18% | - Cloud-native AI solutions and integration - Data mobility and consistency across environments - NetApp cloud data services for AI - Hybrid and multi-cloud AI architectures |
| NetApp AI Solutions and Architecture | 25% | - Scalability and performance optimization for AI - NetApp AI-ready infrastructure components - Storage architectures for AI workloads - ONTAP integration with AI frameworks - Data management and data pipeline design |
| AI Overview | 15% | - Algorithm types: supervised, unsupervised, reinforcement learning - Convergence of AI, high-performance computing, and analytics - AI industry use cases and applications - AI deployment models: on-premises, cloud, edge - AI, machine learning, and deep learning concepts |
| Security, Reliability, and Operations | 15% | - Data security and access control for AI - High availability and data protection - Cost management and efficiency - Monitoring, logging, and troubleshooting AI environments |
| AI Lifecycle | 27% | - Model training, inference, and optimization - AI governance, ethics, and compliance - AI lifecycle stages: design, training, deployment, monitoring - Predictive vs generative AI - Data preparation and management for AI |
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90. Frage
A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?
Antwort: D
91. Frage
A data scientist needs to launch a Jupyter notebook as a pod in a Kubernetes cluster. The pod requires a 50 Gi persistent volume for storing datasets and notebooks. The cluster administrator has configured a default Trident StorageClass for general-purpose use. The data scientist has the following PersistentVolumeClaim (PVC) manifest:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: jupyter-pvc
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 50Gi
When this PVC is applied to the cluster, what will be the result?
Antwort: C
92. Frage
An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)
Antwort: B,E
93. Frage
The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?
Antwort: A
94. Frage
An AI architect is planning the resource allocation for a new project. The primary task is to process millions of unlabeled customer reviews to identify naturally occurring groups or themes without any prior guidance.
The project requirements are summarized below:
Task: Discover hidden patterns in text data
Input_Data: 10 million unlabeled text reviews
Output: Clustered groups of related reviews
Supervision: None
Which type of machine learning algorithm is required for this task?
Antwort: A
95. Frage
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