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Network Appliance NetApp Certified AI Expert Sample Questions:

1. 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 remain in a "Pending" state until a PersistentVolume is manually created.
B) The PVC will fail because a 'storageClassName' is not explicitly defined.
C) Trident will create a 1 Gi volume, as this is the default size for all PVCs.
D) Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.

2. 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) Increase the number of network ports connected to the storage controller.
B) Enable QoS maximums on the training volume to limit its IOPS.
C) Replace the NFS protocol with iSCSI for all training data access.
D) Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
E) Use NetApp FlexCache to create a local cache of the training data on each compute node.

3. What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

A) To exclusively use cloud-native AI services, eliminating the need for any on-premises hardware.
B) To simplify network management by using a single, global virtual network spanning the on- premises and cloud environments.
C) To reduce the overall cost of storage by keeping all data in the public cloud.
D) To leverage the powerful, elastic GPU compute resources available in the public cloud for model training while keeping sensitive source data on-premises to meet security and HIPAA compliance requirements.

4. The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?

A) Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.
B) Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.
C) Implement a FabricPool policy to tier the data to a NetApp ASA system.
D) Enable deduplication and compression on all StorageGRID storage nodes.

5. A development team is building a generative AI application that must answer questions based on a constantly changing internal knowledge base of company documents. They want to provide the model with up-to-date information without altering its core weights and capabilities.
Which approach is most suitable for this requirement?

A) Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.
B) Predictive AI, which analyzes the documents to forecast future questions.
C) Fine-tuning, which retrains the model's last few layers on the new documents.
D) Unsupervised learning, which clusters the documents into topics for the model to browse.

Solutions: