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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

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

2. An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.
avg_gpu_utilization: 25%
avg_cpu_iowait_percent: 65%
avg_network_bandwidth_util: 95% (on a 10GbE network)
storage_array_latency: <1ms
Given these metrics, what is the most likely bottleneck causing the low GPU utilization?

A) The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.
B) The network connecting the compute nodes and storage is saturated and has become the primary bottleneck.
C) The storage array is too slow and cannot serve data quickly enough.
D) The GPUs are faulty and cannot process data at their rated speed.

3. An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

A) During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
B) Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
C) Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.
D) Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
E) Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
F) Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.

4. A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

A) NetApp FlexCache
B) NetApp StorageGRID
C) NetApp XCP
D) NetApp FabricPool
E) NetApp SnapMirror

5. A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)

A) A Cisco and NetApp FlexPod for AI solution.
B) A custom-built server rack with components sourced from various different vendors.
C) A single NetApp FAS system connected directly to a single server.
D) NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.
E) A public cloud provider's general-purpose virtual machine instances.

Solutions: