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Network Appliance NetApp Certified AI Expert Sample Questions:

1. 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) Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
B) Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.
C) Replace the disks in the existing storage nodes with higher-capacity drives.
D) Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.

2. 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.

3. A healthcare organization plans to use a large dataset of patient records to train a predictive model. Before training, they must identify and segregate all records containing Personally Identifiable Information (PII) to comply with privacy regulations. The data resides on an on- premises NetApp ONTAP cluster. The organization needs an automated tool to scan the data in- place and tag files containing PII without moving the data.
The project requirements are as follows:
Task: Identify PII in a large dataset.
Data_Location: On-premises ONTAP cluster.
Constraint: Data must not be moved from its source location for scanning.
Output: Tagged files containing PII.
Which NetApp tool is designed for this specific task?

A) NetApp BlueXP classification
B) NetApp FlexCache
C) NetApp SnapMirror
D) NetApp XCP

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

5. A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

A) Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.
B) Configure QoS minimums on the training volumes to ensure they do not impact inference performance.
C) Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.
D) Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
E) Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.

Solutions: