We're so confident of our products that we provide no hassle product exchange.
Network Appliance NS0-901 Exam Overview:
| Certification Vendor: | NetApp |
|---|---|
| Exam Name: | NetApp Certified AI Expert Exam |
| Exam Number: | NS0-901 |
| Real Exam Qty: | 60 |
| Passing Score: | 66% |
| Exam Duration: | 90 minutes |
| Exam Price: | USD 250 |
| Related Certifications: | NetApp Certified AI Expert (NCAE) |
| Available Languages: | English |
| Exam Format: | Scenario-based, Multiple Choice |
| Sample Questions: | Network Appliance NS0-901 Sample Questions |
| Exam Way: | Online or at a Pearson VUE testing center |
| Official Syllabus URL: | https://www.netapp.com/be-nl/certification-track/ai-expert/ |
Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Lifecycle | 27% | - Generative AI Concepts
|
| Topic 2: AI Software Architectures | 18% | - Development Tools
|
| Topic 3: AI Overview | 15% | - AI Convergence with HPC and Analytics
|
| Topic 4: AI Common Challenges | 22% | - Traceability and Optimization
|
| Topic 5: AI Hardware Architectures | 18% | - Networking and Storage
|
Network Appliance NetApp Certified AI Expert Sample Questions:
A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?
- A. The storage tier is too slow, causing the GPUs to wait for data.
- B. The model was trained using supervised learning, which is inefficient for fraud detection.
- C. The network latency is too high for an edge deployment.
- D. The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.
Correct Answer: D 🗳️
The company decides to establish a disaster recovery (DR) site in a secondary data center for the entire Digital Twin platform. The DR plan must protect the HPC data, the AI training data, and the central data lake.
The DR requirements are:
- RPO: 4 hours for all data.
- RTO: 24 hours for the entire platform.
- Process: The failover and failback process should be as automated as possible.
Which combination of technologies provides the most comprehensive DR solution?
- A. Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.
- B. Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.
- C. Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.
- D. Use NetApp SnapMirror for the ASA, StorageGRID's built-in replication for the data lake, and a host- based tool like 'rsync' for the E-Series/BeeGFS data, all orchestrated via BlueXP disaster recovery.
Correct Answer: D 🗳️
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)
- A. RunAI
- B. TensorFlow Extended
- C. KubeFlow
- D. Google VertexAI
Correct Answer: A,C 🗳️
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 sends an email notification to the administrator to manually reallocate the GPUs.
- B. 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.
- C. It automatically terminates all low-priority jobs to free up the entire cluster.
- D. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
Correct Answer: B 🗳️
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 NetApp ASA storage system is offline.
- C. The Kubernetes nodes do not have the necessary NFS client utilities installed.
- D. The 'requiredTopology' in the StorageClass is misconfigured, preventing Trident from finding a valid ONTAP backend in the same zone as the available compute nodes.
Correct Answer: D 🗳️
By Joa
