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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. The company's compliance officer mandates that all raw simulation data in the StorageGRID data lake must be protected from modification or deletion for a period of seven years. Which two StorageGRID features should be implemented to meet this strict, long-term data immutability requirement? (Choose 2.)

A) Configure an Information Lifecycle Management (ILM) rule that applies to the bucket and sets a retention period of seven years.
B) Encrypt all objects in the bucket using server-side encryption (SSE).
C) Enable Autonomous Ransomware Protection (ARP) on the StorageGRID system.
D) Create a new S3 bucket with Object Lock enabled in Compliance mode.
E) Use NetApp SnapMirror to replicate the data to a second StorageGRID system.

2. A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?

A) Generative learning
B) Reinforcement learning
C) Unsupervised learning
D) Supervised learning

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

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

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

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) Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
D) Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
E) Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.

Solutions: