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Network Appliance NetApp Certified AI Expert Sample Questions:

1. Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

A) Object-based storage
B) File-based systems
C) POSIX-based file systems
D) Parallel file systems

2. The AI training jobs on the AIPod are performing below expectations. The NVIDIA DGX servers' GPUs show low utilization. A performance analysis reveals that the bottleneck is not the storage system itself, but the network path between the storage and the compute nodes.
The current network configuration is as follows:
Network_Fabric: 100GbE Standard Ethernet
Protocol: NFS over TCP/IP
Data_Path: Storage -> Host CPU -> GPU Memory
Which network architecture enhancement would provide the most significant performance improvement by reducing latency and CPU overhead?

A) Upgrade the network switches to a model with a larger packet buffer.
B) Implement RDMA over Converged Ethernet (RoCE) and configure GPUDirect Storage.
C) Add more 100GbE network ports to the storage controllers.
D) Isolate the storage traffic on a separate VLAN from the management traffic.

3. Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

A) Google VertexAI
B) TensorFlow Extended
C) RunAI
D) KubeFlow

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

5. An AI research team is experiencing slow model training times. Their performance monitoring indicates that the GPUs are frequently idle, waiting for data. They want to implement a single technology change to create a more direct data path between their storage and GPUs.
Their current setup is as follows:
Compute: Server with NVIDIA A100 GPUs
Storage: NetApp AFF A-Series (All-Flash)
Network: 100GbE Ethernet
Data_Path: Storage -> Host CPU/Memory -> GPU Memory
Which technology should the architect recommend to specifically address this data path inefficiency?

A) GPUDirect Storage
B) NetApp SnapMirror
C) A faster CPU in the server
D) NetApp FabricPool

Solutions: