Network Appliance NS0-901 Exam Answers - NS0-901 Practice Engine

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Overview	15%	- AI deployment models: on-premises, cloud, edge - Convergence of AI, high-performance computing, and analytics - Algorithm types: supervised, unsupervised, reinforcement learning - AI, machine learning, and deep learning concepts - AI industry use cases and applications
Topic 2: AI Lifecycle	27%	- AI governance, ethics, and compliance - Model training, inference, and optimization - Predictive vs generative AI - AI lifecycle stages: design, training, deployment, monitoring - Data preparation and management for AI
Topic 3: NetApp AI Solutions and Architecture	25%	- NetApp AI-ready infrastructure components - Storage architectures for AI workloads - Scalability and performance optimization for AI - ONTAP integration with AI frameworks - Data management and data pipeline design
Topic 4: Security, Reliability, and Operations	15%	- Monitoring, logging, and troubleshooting AI environments - Cost management and efficiency - Data security and access control for AI - High availability and data protection
Topic 5: 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

>> Network Appliance NS0-901 Exam Answers <<

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

A. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
B. It automatically terminates all low-priority jobs to free up the entire cluster.
C. It sends an email notification to the administrator to manually reallocate the GPUs.
D. It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.

Answer: D

NEW QUESTION # 15
An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A. They are networking protocols designed to accelerate data transfer between GPUs.
B. They are specialized storage systems designed to hold large datasets for training.
C. They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.
D. They are integrated development environments (IDEs) used exclusively for writing Python code.

Answer: C

NEW QUESTION # 16
Which of the following describes the impact of generative AI in content creation?

A. Generative AI can create new content such as text, images, and videos.
B. Generative AI analyzes data without generating content.
C. Generative AI only processes pre-existing content.
D. Generative AI predicts customer preferences without creating new content.

Answer: A

NEW QUESTION # 17
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.)

A. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
B. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
C. Use a standard 10GbE network for all connectivity to reduce costs.
D. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
E. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
F. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.

Answer: A,B,E,F

NEW QUESTION # 18
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

A. Google VertexAI
B. RunAI
C. KubeFlow
D. TensorFlow Extended

Answer: B,C

NEW QUESTION # 19
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