An MLOps engineer is troubleshooting a failed Kubeflow pipeline step. The step was designed to create a clone of a dataset for a training job using the NetApp DataOps Toolkit. The pod logs for the failed pipeline step show the following:
Traceback (most recent call last):
File "create_clone.py", line 15, in <module>
clone_pvc(source_pvc_name="training-data-v2", new_pvc_name="train-job-34a-data") NameError: name 'clone_pvc' is not defined The engineer reviews the Python script for the pipeline step:
# create_clone.py
import os
from netapp_dataops.k8s import create_pvc
# Other code
print("Cloning source dataset for training run...")
clone_pvc(
source_pvc_name="training-data-v2",
new_pvc_name="train-job-34a-data"
)
print("Clone created successfully.")
What is the cause of the error?

• A. The Python script is attempting to use the 'clone_pvc' function, but it was not imported from the
  'netapp_dataops.k8s' library.
• B. The NetApp DataOps Toolkit is not installed in the container image used for this pipeline step.
• C. The source PVC 'training-data-v2' does not exist.
• D. The Kubernetes cluster is not running NetApp Trident.

A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

• A. NetApp SnapMirror
• B. NetApp FlexCache
• C. NetApp FabricPool
• D. NetApp XCP
• E. NetApp StorageGRID

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

What is the primary role of NetApp Trident in a Kubernetes environment designed for AI workloads?

• A. To act as a dynamic storage orchestrator, provisioning persistent storage from NetApp backends on- demand for containerized applications.
• B. To directly accelerate GPU computations using specialized drivers.
• C. To provide a web-based IDE, like Jupyter, for data scientists to develop models.
• D. To function as a container runtime interface for executing AI models.