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Network Appliance NetApp Certified AI Expert Sample Questions:

1. An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

A) A new LLM trained from scratch on the legal documents.
B) A fine-tuning pipeline to update the LLM daily.
C) A predictive AI model to classify legal documents.
D) A Retrieval-Augmented Generation (RAG) architecture.

2. 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 Jupyter Notebooks.
C) Both the data scientist and the DevOps engineer should use automated production pipelines.
D) The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.

3. An architect is designing a cost-optimized storage solution for a large AI environment that has two distinct data temperature profiles:
1. Hot Data (200 TB): Actively used training sets and models requiring all-flash performance.
2. Cold Data (2 PB): Inactive, archived datasets and old model versions that must remain online but are infrequently accessed.
The solution must automatically manage data placement based on activity, without requiring manual intervention or changes to file paths. The environment consists of an on-premises data center and a public cloud account.
Which combination of NetApp technologies provides the most complete and cost-effective solution? (Select all that apply.)

A) Deploy a NetApp AFF A-Series system on-premises to serve as the high-performance tier for the hot data.
B) Use NetApp FabricPool to automatically and transparently tier cold data blocks from the AFF A- Series to the StorageGRID system.
C) Use NetApp XCP to manually script the daily movement of cold files from the AFF system to StorageGRID.
D) Deploy a single, large Cloud Volumes ONTAP instance to hold both the hot and cold data tiers.
E) Use NetApp SnapMirror to create a full copy of the 2 PB of cold data on the AFF A-Series for faster access if needed.
F) Deploy NetApp StorageGRID on-premises to serve as a cost-effective capacity tier for the cold data.

4. A pharmaceutical company is creating a "digital twin" of its manufacturing process. This involves running complex simulations (an HPC workload) that generate massive datasets.
The company wants to use this data immediately for two other purposes:
1. Analytics: Business analysts need to run complex queries on the simulation output using tools like Spark.
2. AI Training: Data scientists need to use the same output as a training set for a predictive maintenance model.
The company wants to avoid creating separate data silos for each workload.
Which two NetApp technologies are best suited for building a unified data lake that can efficiently serve all three workloads (HPC, Analytics, AI)? (Choose 2.)

A) NetApp ONTAP with FlexGroup volumes to provide high-throughput, parallel NFS access for the HPC and AI training workloads.
B) NetApp SnapCenter to create application-consistent backups of the data.
C) NetApp Keystone to provide a flexible, pay-as-you-go consumption model for the infrastructure.
D) NetApp Autonomous Ransomware Protection to secure the data from modification.
E) NetApp StorageGRID to provide a scalable, S3-native object store that integrates directly with modern analytics platforms like Spark.

5. An MLOps team is using the BlueXP Workload Factory to automate the deployment of a Jupyter notebook environment for a new data scientist. The deployment job fails. The team reviews the job details in the BlueXP UI.
Job ID: 78a1c-2b3d-4e5f
Service: BlueXP Workload Factory
Action: Deploy JupyterHub on Kubernetes
Status: FAILED
Error Message: "Failed to create PersistentVolumeClaim 'jupyter-pvc-user01'.
User 'service-account- bluexp' does not have permission to 'create' resource
'persistentvolumeclaims' in cluster 'k8s-prod- cluster-01'."
What is the most direct way to resolve this issue?

A) Manually create the PersistentVolumeClaim using 'kubectl'.
B) Modify the Kubernetes RBAC (Role-Based Access Control) configuration to grant the 'service- account-bluexp' the necessary permissions to create PVCs in the target cluster.
C) Restart the BlueXP Connector to refresh its permissions.
D) Increase the storage quota for the 'jupyter-pvc-user01' PVC.

Solutions: