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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

A) Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
B) Manually deploy all new application replicas to a single, new, very large "super-node".
C) Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
D) Replace all existing GPUs with the next-generation model to increase the performance of each node.
E) Horizontally scale the cluster by adding new GPU-equipped nodes.

2. An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A) They are integrated development environments (IDEs) used exclusively for writing Python code.
B) They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.
C) They are networking protocols designed to accelerate data transfer between GPUs.
D) They are specialized storage systems designed to hold large datasets for training.

3. 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) Supervised learning
B) Reinforcement learning
C) Generative learning
D) Unsupervised learning

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

5. A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

A) The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
B) The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
C) Both the data scientist and the DevOps engineer should use automated production pipelines.
D) Both the data scientist and the DevOps engineer should use Jupyter Notebooks.

Solutions: