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| Section | Weight | Objectives |
|---|---|---|
| NetApp AI Solutions and Architecture | 25% | - ONTAP integration with AI frameworks - Data management and data pipeline design - NetApp AI-ready infrastructure components - Storage architectures for AI workloads - Scalability and performance optimization for AI |
| AI Overview | 15% | - AI deployment models: on-premises, cloud, edge - Convergence of AI, high-performance computing, and analytics - Algorithm types: supervised, unsupervised, reinforcement learning - AI, machine learning, and deep learning concepts - AI industry use cases and applications |
| Security, Reliability, and Operations | 15% | - Data security and access control for AI - High availability and data protection - Monitoring, logging, and troubleshooting AI environments - Cost management and efficiency |
| AI Lifecycle | 27% | - AI governance, ethics, and compliance - Predictive vs generative AI - Data preparation and management for AI - Model training, inference, and optimization - AI lifecycle stages: design, training, deployment, monitoring |
| Cloud and Hybrid Cloud AI Deployment | 18% | - Data mobility and consistency across environments - NetApp cloud data services for AI - Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures |
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 predictive AI model to classify legal documents.
B) A new LLM trained from scratch on the legal documents.
C) A fine-tuning pipeline to update the LLM daily.
D) A Retrieval-Augmented Generation (RAG) architecture.
2. A data scientist needs to launch a Jupyter notebook as a pod in a Kubernetes cluster. The pod requires a 50 Gi persistent volume for storing datasets and notebooks. The cluster administrator has configured a default Trident StorageClass for general-purpose use. The data scientist has the following PersistentVolumeClaim (PVC) manifest:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: jupyter-pvc
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 50Gi
When this PVC is applied to the cluster, what will be the result?
A) The PVC will fail because a 'storageClassName' is not explicitly defined.
B) Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
C) Trident will create a 1 Gi volume, as this is the default size for all PVCs.
D) The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
3. 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) Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.
B) Move the entire client portfolio database to an encrypted, air-gapped system.
C) 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.
D) Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
E) Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.
4. An AI operations team is troubleshooting why their RAG-based chatbot is providing outdated information. They have confirmed that the vector database embedding process is functioning correctly, but suspect an issue with the initial data synchronization that moves the knowledge base from an on- premises ONTAP file share to a cloud staging bucket.
They inspect the relevant BlueXP copy and sync job and find the following details:
Service: BlueXP copy and sync
Relationship_Name: KB_Sync_to_Vector_Staging
Source: nfs://ontap-cluster-1/vol_kb/docs
Destination: s3://vector-staging-bucket-89a3/latest/
Last_Sync_Status: FAILED
Last_Sync_Time: 2025-07-11T02:00:15Z
Error_Message: "Authentication error:
Unable to access source.
Check export policy on 'vol_kb'."
Based on this information, what is the most direct solution to fix the data pipeline?
A) Modify the NFS export policy on the 'vol_kb' volume on the on-premises ONTAP cluster to grant access to the BlueXP Connector.
B) Fine-tune the LLM with the latest data instead of using the RAG system.
C) Check the IAM permissions for the role associated with the S3 bucket.
D) Re-run the vector database embedding job.
5. Which storage protocols are commonly used for handling large-scale AI data? (Choose two)
A) File-based systems
B) POSIX-based file systems
C) Object-based storage
D) Parallel file systems
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: A,C | Question # 4 Answer: A | Question # 5 Answer: C,D |
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