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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI Lifecycle | 27% | - Data Preparation
|
| AI Overview | 15% | - Training vs. Inferencing vs. Predictions
|
| AI Software Architectures | 18% | - MLOps and LLMOps Ecosystems
|
| AI Common Challenges | 22% | - Traceability and Optimization
|
| AI Hardware Architectures | 18% | - NetApp Architectures
|
Network Appliance NetApp Certified AI Expert Sample Questions:
1. An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?
A) Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
B) Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
C) Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
D) Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
E) Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
2. 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 data lake, a data warehouse, a data pipeline, and a data mart.
B) A pre-trained model, a validation set, an inference engine, and a cloud provider.
C) A project manager, a data scientist, a software engineer, and a budget.
D) Data, code, compute, and time.
3. 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) Both the data scientist and the DevOps engineer should use automated production pipelines.
C) The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
D) Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
4. The firm wants to extend the "Advisor Assistant" to include a new batch processing feature. Every night, the system must analyze every client portfolio against a set of 50 different risk models and generate a compliance report. This is a highly parallel, read-intensive workload. The architect must design a data workflow that is efficient and does not impact the production chatbot environment. Which sequence of actions and technologies provides the most effective solution?
A) Create a full physical copy of the client portfolio database to a separate volume, mount it to the compute nodes, and run the analysis.
B) Use NetApp SnapMirror to replicate the portfolio database volume to the DR site, and run the analysis jobs there.
C) Create a NetApp Snapshot of the portfolio database volume, create a FlexClone from that snapshot, mount the FlexClone to the analysis pods, and run the batch job.
D) Run the analysis job directly against the production portfolio database during off-peak hours.
5. Which AI technology is used to generate new, never-before-seen content such as images or text?
A) Reinforcement AI
B) Predictive AI
C) Supervised AI
D) Generative AI
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: D |
