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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI Hardware Architectures | 18% | - NetApp Architectures
|
| AI Software Architectures | 18% | - Scaling and Orchestration
|
| AI Lifecycle | 27% | - Data Preparation
|
| AI Overview | 15% | - Machine Learning Fundamentals
|
| AI Common Challenges | 22% | - Resource Management
|
Network Appliance NetApp Certified AI Expert Sample Questions:
1. 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.
2. 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.
3. 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?
A) Trident will create a 1 Gi volume, as this is the default size for all PVCs.
B) The PVC will fail because a 'storageClassName' is not explicitly defined.
C) Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
D) The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
4. The data science team in Azure reports that training jobs are taking longer than expected. An analysis of the Cloud Volumes ONTAP instance in Azure shows that the instance type is undersized for the I/O demands of the training workload. The architect needs to change the Azure VM instance type for the Cloud Volumes ONTAP system to a more powerful one.
The current configuration is:
Cloud_Provider: Azure
ONTAP_System: Cloud Volumes ONTAP (Single Node)
Current_Instance_Type: Standard_DS3_v2
Target_Instance_Type: Standard_E8s_v4
What is the most direct method to perform this operation using NetApp's management tools?
A) Terminate the existing Cloud Volumes ONTAP instance and deploy a new one with the target instance type, then restore data from backup.
B) From the BlueXP Canvas, select the Cloud Volumes ONTAP working environment and use the
"Change Instance Type" action in the Features panel.
C) SSH to the Cloud Volumes ONTAP instance and run an ONTAP command to modify the underlying VM.
D) Use the Azure portal to manually change the VM instance type.
5. The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?
A) Implement a FabricPool policy to tier the data to a NetApp ASA system.
B) Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.
C) Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.
D) Enable deduplication and compression on all StorageGRID storage nodes.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: B |
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