Network Appliance NS0-901 real exam prep : NetApp Certified AI Expert Exam

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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. 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 automatically terminates all low-priority jobs to free up the entire cluster.
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 sends an email notification to the administrator to manually reallocate the GPUs.
D) It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.

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

3. An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)

A) Increase the number of network ports connected to the storage controller.
B) Use NetApp FlexCache to create a local cache of the training data on each compute node.
C) Replace the NFS protocol with iSCSI for all training data access.
D) Enable QoS maximums on the training volume to limit its IOPS.
E) Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.

4. An online retail company's recommendation engine, which provides real-time product suggestions to users, is experiencing unacceptable latency. The inference application is running on a correctly-sized edge server, but user requests are taking over 500ms to process. An architect reviews the data access pattern and infrastructure diagram.
Application_Location: Edge Server (In-store)
Data_Source_Location: Core Data Center (On-premises ONTAP)
Data_Required_for_Inference: User profile data, product catalog vectors Network_Path: Edge -> WAN -> Core Data Center Observed_Latency: 550ms What is the most likely cause of the high inference latency?

A) Every inference request requires a high-latency round trip over the WAN to fetch data from the core data center.
B) The edge server has insufficient CPU resources to run the model.
C) The on-premises ONTAP system is not configured for high-throughput.
D) The model is too large to fit into the edge server's memory.

5. 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 model was trained using supervised learning, which is inefficient for fraud detection.
B) The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.
C) The storage tier is too slow, causing the GPUs to wait for data.
D) The network latency is too high for an edge deployment.

Solutions: