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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

Question 1

An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process. Which of the following are the four essential requirements for model generation?

A. A project manager, a data scientist, a software engineer, and a budget.
B. Data, code, compute, and time.
C. A data lake, a data warehouse, a data pipeline, and a data mart.
D. A pre-trained model, a validation set, an inference engine, and a cloud provider.

Question 2

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 XCP
D. NetApp SnapMirror

Question 3

What is the primary role of NetApp Trident in a Kubernetes environment designed for AI workloads?

A. To act as a dynamic storage orchestrator, provisioning persistent storage from NetApp backends on- demand for containerized applications.
B. To directly accelerate GPU computations using specialized drivers.
C. To provide a web-based IDE, like Jupyter, for data scientists to develop models.
D. To function as a container runtime interface for executing AI models.

Question 4

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

Question 5

An AI team is planning two separate projects. The architect needs to provision the appropriate infrastructure for each.
| | Project A | Project B|
| -- | | - |
| Goal | Build a novel image recognition model from scratch.
| Adapt an existing, pre- trained LLM to understand company-specific jargon. |
| Input Data | 10 million new, unlabeled images. | A 50 GB text corpus of internal documents. |
| Required Compute | Very High (Weeks of multi-GPU training) | Moderate (Hours of single-GPU training) | Which two statements accurately describe the infrastructure requirements for these projects?
(Choose two.)

A. Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.
B. Project B is a fine-tuning task, which leverages an existing model and requires less data and compute resources than building a model from scratch.
C. Project A requires only a large amount of data, while Project B requires only a large amount of code.
D. Both projects are examples of fine-tuning and have similar infrastructure needs.
E. Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.

Solutions: