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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI Common Challenges | 22% | - Resource Management
|
| AI Software Architectures | 18% | - Development Tools
|
| AI Lifecycle | 27% | - Model Development
|
| AI Overview | 15% | - AI Deployment Models
|
| AI Hardware Architectures | 18% | - Infrastructure Topologies
|
Network Appliance NetApp Certified AI Expert Sample Questions:
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?
- A. Replace the disks in the existing storage nodes with higher-capacity drives.
- B. Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
- C. Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
- D. Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.
Correct Answer: B 🗳️
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?
- A. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
- B. Both the data scientist and the DevOps engineer should use automated production pipelines.
- C. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
- D. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
Correct Answer: C 🗳️
An MLOps engineer is deploying a training pod that requires a high-performance volume. After applying the pod and PVC manifests, the pod remains in a 'Pending' state. The engineer runs
'kubectl describe pod training-pod-7d8c' and sees the following event:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 2m15s
default-scheduler 0/4 nodes are available: 1 node(s) had volume node affinity conflict, 3 node(s) didn't find available persistent volume to bind.
The engineer then inspects the associated PVC and sees its status is also 'Pending'.
What is the most likely cause of this issue?
- A. The 'storageClassName' specified in the PVC does not match any existing StorageClass managed by Trident.
- B. The ONTAP backend is out of available capacity to provision a new volume.
- C. The Kubernetes scheduler is malfunctioning and cannot assign pods to nodes.
- D. The Trident controller pod has crashed and needs to be restarted.
Correct Answer: A 🗳️
A media company is building a new generative AI service. The project has two main components:
1. Data Lake & Fine-Tuning: A 300 TB repository of unstructured data (videos, images, text) stored as objects will be used to fine-tune a foundational model. This process requires a scalable, cost-effective storage solution that can integrate with cloud-native data processing tools like Apache Spark.
2. Inference & RAG: The fine-tuned model will be used in a customer-facing application that leverages Retrieval-Augmented Generation (RAG). To ensure low-latency responses, the RAG component requires extremely fast lookups from a 10 TB vector database.
The company needs a solution that optimizes both cost and performance for this entire lifecycle.
Which combination of NetApp technologies provides the most appropriate solution for this scenario?
- A. Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.
- B. Use a single, large NetApp ASA system for both the object data lake and the vector database.
- C. Use NetApp E-Series for the data lake and a NetApp ASA system for the vector database.
- D. Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.
Correct Answer: A 🗳️
An AI platform team is investigating poor I/O performance for a specific workload that involves processing hundreds of thousands of small metadata files. The application is running on a Kubernetes cluster with storage provided by a NetApp ONTAP system over NFS. Performance metrics show acceptable network throughput but very high latency for metadata operations (e.g., open, stat, close).
The current storage configuration is as follows:
Storage_System: NetApp AFF A-Series
Protocol: NFSv4.1
Workload_Profile: Metadata-intensive, many small file lookups
Observed_Issue: High latency on metadata operations, slow job completion Which storage architecture would be better suited to handle this specific metadata-intensive workload?
- A. A NetApp Cloud Volumes ONTAP instance in a different region.
- B. A NetApp E-Series system running a parallel file system like BeeGFS.
- C. A NetApp StorageGRID object storage system.
- D. A NetApp SnapLock-enabled volume for data protection.
Correct Answer: B 🗳️
Marsh -
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