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Network Appliance NS0-901 Exam Syllabus Topics:

SectionObjectives
Industry Use Cases- AI applications across industries
  • 1. Autonomous systems and agents
    • 2. Digital twins
      • 3. Healthcare AI
        AI Lifecycle and Deployment- End-to-end AI lifecycle
        • 1. Data ingestion and preparation
          • 2. Model training and validation
            • 3. Model deployment and monitoring
              - Operational challenges
              • 1. Data governance and management
                • 2. Scalability and performance optimization
                  AI and Machine Learning Fundamentals- Algorithm types
                  • 1. Unsupervised learning
                    • 2. Reinforcement learning
                      • 3. Supervised learning
                        - AI, ML, DL concepts
                        • 1. Training, inference, and prediction workflows
                          • 2. Differences between AI, machine learning, and deep learning
                            AI Infrastructure and NetApp Solutions- Converged workloads
                            • 1. Shared infrastructure design considerations
                              • 2. AI, HPC, and analytics convergence
                                - AI-ready data infrastructure
                                • 1. High-performance storage for AI workloads
                                  • 2. Data pipeline optimization for AI/ML workloads

                                    Network Appliance NetApp Certified AI Expert Sample Questions:

                                    Question 1

                                    To comply with the security mandate, the architect must design a process to prevent client PII from the portfolio database from ever being included in the context sent to the LLM. Which two actions are required to build a robust and automated solution for this? (Choose 2.)

                                    A. Move the entire client portfolio database to an encrypted, air-gapped system.
                                    B. Implement a data preparation step in the RAG pipeline that programmatically masks or excludes any data fields tagged as PII before the context is constructed.
                                    C. Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
                                    D. Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.
                                    E. Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.


                                    Question 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. Supervised learning
                                    B. Reinforcement learning
                                    C. Generative learning
                                    D. Unsupervised learning


                                    Question 3

                                    Which of the following applications use AI in the healthcare industry? (Choose two)

                                    A. Predicting patient outcomes
                                    B. Automating financial reporting
                                    C. Diagnosing diseases
                                    D. Managing hospital supply chains


                                    Question 4

                                    An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
                                    1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
                                    2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
                                    The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
                                    Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

                                    A. Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.
                                    B. Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.
                                    C. Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
                                    D. Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
                                    E. Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
                                    F. Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.


                                    Question 5

                                    The data scientists report that their Kubernetes-based data preparation jobs are failing. The pods are stuck in a 'Pending' state.
                                    An MLOps engineer runs 'kubectl describe pvc data-prep-pvc-01' and sees the following event:
                                    Type Reason Age From Message
                                    - - - -
                                    Warning ProvisioningFailed 2m15s trident-orchestrator-7b... failed to provision volume with StorageClass "bronze-tier": backend unavailable: no healthy backend with satisfying attributes for storage class "bronze-tier" The engineer checks the Trident backend configurations and finds no backend associated with the "bronze-tier" StorageClass.
                                    What is the root cause of the failure?

                                    A. The data preparation pods do not have the correct security permissions.
                                    B. The StorageGRID data lake is offline, preventing the creation of new volumes.
                                    C. The PersistentVolumeClaim is referencing a StorageClass that has no configured or healthy Trident backend to provision storage from.
                                    D. The Kubernetes scheduler is unable to find a node with enough resources for the pods.


                                    Solutions:

                                    Question 1
                                    Answer: B,E
                                    Question 2
                                    Answer: B
                                    Question 3
                                    Answer: A,C
                                    Question 4
                                    Answer: A,D
                                    Question 5
                                    Answer: C

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