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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: AI and Machine Learning Fundamentals	- AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning - Algorithm types 1. Unsupervised learning 2. Supervised learning 3. Reinforcement learning
Topic 2: AI Infrastructure and NetApp Solutions	- AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads - Converged workloads 1. AI, HPC, and analytics convergence 2. Shared infrastructure design considerations
Topic 3: AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Data ingestion and preparation 2. Model deployment and monitoring 3. Model training and validation
Topic 4: Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Digital twins 3. Autonomous systems and agents

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
Advisors report that some queries to the chatbot are unacceptably slow, taking several seconds to respond. The MLOps team isolates the issue to the RAG retrieval step. Performance monitoring of the NetApp AFF A-Series hosting the vector database shows the following metrics during periods of high query load.
avg_read_latency: 3500 microseconds (3.5 ms)
avg_write_latency: 400 microseconds (0.4 ms)
iops_total: 15,000
cpu_utilization_storage_node: 15%
workload_profile: 95% small, random reads
Given these metrics, what is the most likely performance bottleneck?

A. The AFF A-Series system is underpowered and cannot handle the IOPS load.
B. The network between the application and the storage system is saturated.
C. The storage node CPU is the bottleneck, preventing faster data processing.
D. The vector database is poorly indexed, causing inefficient, slow lookups that translate to high read latency on the storage.

Answer: D

NEW QUESTION # 24
Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

A. Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.
B. Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.
C. Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.
D. Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.

Answer: C

NEW QUESTION # 25
A distributed training job running on the AIPod fails to start. The MLOps engineer inspects the events for one of the pending training pods and sees the following message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 5m12s default-scheduler 0/4 nodes are available: 4 node(s) had no available volume zone.
The PersistentVolumeClaim (PVC) for this pod specifies a StorageClass that uses the 'ontap-nas' Trident provisioner.
he Trident logs show no errors.
What is the most likely cause of this scheduling failure?

A. The training pod's container image does not exist in the registry.
B. The 'requiredTopology' in the StorageClass is misconfigured, preventing Trident from finding a valid ONTAP backend in the same zone as the available compute nodes.
C. The Kubernetes nodes do not have the necessary NFS client utilities installed.
D. The NetApp ASA storage system is offline.

Answer: B

NEW QUESTION # 26
An AI training job is running slower than expected. The infrastructure team suspects a compute bottleneck. The job involves processing high-resolution images with a complex convolutional neural network (CNN). They review the logs from the training script.
Epoch 1/100 - 3600s - loss: 1.253 - acc: 0.54
...
CPU_Utilization_During_Epoch: 99% (all cores)
GPU_0_Utilization_During_Epoch: 8%
GPU_1_Utilization_During_Epoch: 7%
...
Epoch 2/100 - 3610s - loss: 1.102 - acc: 0.61
What is the most likely cause of the poor training performance?

A. The model is too simple and does not effectively utilize the GPU's parallel processing capabilities.
B. The training script is CPU-bound, likely performing data augmentation or preprocessing on the CPU instead of offloading it to the GPUs.
C. The storage system cannot deliver data fast enough to the compute node.
D. The network is saturated, preventing the GPUs from receiving data.

Answer: B

NEW QUESTION # 27
Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

A. Horizontally scale the cluster by adding new GPU-equipped nodes.
B. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
C. Replace all existing GPUs with the next-generation model to increase the performance of each node.
D. Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
E. Manually deploy all new application replicas to a single, new, very large "super-node".

Answer: A,D

NEW QUESTION # 28
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