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An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)
A. Enable QoS maximums on the training volume to limit its IOPS.
B. Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
C. Use NetApp FlexCache to create a local cache of the training data on each compute node.
D. Increase the number of network ports connected to the storage controller.
E. Replace the NFS protocol with iSCSI for all training data access.
Answer: B,C
NEW QUESTION # 29
Given the firm's requirements for using a private, constantly updated knowledge base and the strict mandate for data traceability, which AI architecture is the most appropriate foundation for the "Advisor Assistant" chatbot?
A. A predictive AI model trained to forecast market trends.
B. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.
C. A standalone LLM deployed in an air-gapped environment with no access to the document repository.
D. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.
Answer: B
NEW QUESTION # 30
An enterprise is planning a generative AI solution to power its internal support chatbot. The architect must choose between a RAG-based approach and fine-tuning a base model. The project stakeholders have provided a list of prioritized requirements.
| Requirement | Priority | Details
|
| | -- | |
| Factual Accuracy | Critical | Must use the latest product documentation, updated daily.
| | Brand Voice & Persona | High | Must respond in the company's specific, formal tone.
| | Development Cost | High | Limited budget for GPU compute hours for model training.
|
| Data Traceability | Critical | Must be able to cite the exact source document for each answer.
|
Which two recommendations should the architect make to best satisfy these requirements?
(Choose 2.)
A. Prioritize a RAG architecture to meet the critical requirements for factual accuracy and data traceability.
B. Use RAG exclusively, as prompt engineering alone can fully replicate a specific brand voice and persona.
C. Recommend training a new LLM from scratch to ensure both brand voice and factual accuracy are built-in.
D. Propose a hybrid approach where a base model is first lightly fine-tuned for persona, then used within a RAG system for factual grounding.
E. Prioritize fine-tuning to embed the company's brand voice and persona into the model.
Answer: A,D
NEW QUESTION # 31
A data scientist is using the NetApp DataOps Toolkit for Python to automate the creation of a new, writable volume for an experiment. The script is intended to clone an existing dataset volume. When the script is executed, it fails with an error.
The relevant portion of the Python script is:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="dataset-v1-pvc",
new_pvc_name="experiment-clone-pvc",
namespace="ds-team-1"
)
The script produces the following error in the terminal:
'Error: Failed to clone PVC. Source PVC 'dataset-v1-pvc' not found in namespace 'ds-team-1'.' What is the most likely cause of this error?
A. The Kubernetes cluster does not have NetApp Trident installed.
B. The NetApp DataOps Toolkit does not support cloning volumes.
C. The source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.
D. The Python script is missing the necessary import statement for the toolkit.
Answer: C
NEW QUESTION # 32
The firm decides to implement a disaster recovery (DR) site for the "Advisor Assistant" application in a secondary data center. The Recovery Point Objective (RPO) is 15 minutes, and the Recovery Time Objective (RTO) is 4 hours. The design must protect both the document data lake and the vector database.
The primary site contains:
- Data Lake: NetApp StorageGRID
- Vector DB: NetApp AFF A-Series
Which combination of technologies and processes provides a complete and robust DR solution?
(Select all that apply.)
A. Use NetApp FlexCache at the DR site to cache data from the primary site.
B. Rely on tape backups to be shipped to the DR site in the event of a disaster.
C. Use NetApp SnapMirror to create an asynchronous replication relationship for the AFF A-Series volume containing the vector database, with a schedule of 10 minutes.
D. Use BlueXP disaster recovery to orchestrate and automate the failover and failback workflows for the application and its data dependencies.
E. Use StorageGRID's built-in replication rules to replicate object data from the primary site's grid to a StorageGRID instance at the DR site.
F. Use the NetApp DataOps Toolkit to manually script the failover process.
Answer: C,D,E
NEW QUESTION # 33
......
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