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Network Appliance NetApp Certified AI Expert Sample Questions:

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

2. An organization is planning to deploy a large AI infrastructure but wants to avoid a large, upfront capital expenditure. They prefer an operational expenditure (OpEx) model where they pay for storage and compute resources as they are consumed. They also need the flexibility to scale resources up or down based on project demands.
Which NetApp consumption model is specifically designed to meet these financial and operational requirements?

A) NetApp Keystone, which provides a subscription-based, pay-as-you-go service for on-premises and cloud storage.
B) A perpetual licensing model for all ONTAP software features.
C) NetApp Cloud Volumes ONTAP PAYGO licensing only.
D) A standard capital purchase of AFF and ASA systems.

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

4. 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 source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.
B) The Kubernetes cluster does not have NetApp Trident installed.
C) The NetApp DataOps Toolkit does not support cloning volumes.
D) The Python script is missing the necessary import statement for the toolkit.

5. An online retail company's recommendation engine, which provides real-time product suggestions to users, is experiencing unacceptable latency. The inference application is running on a correctly-sized edge server, but user requests are taking over 500ms to process. An architect reviews the data access pattern and infrastructure diagram.
Application_Location: Edge Server (In-store)
Data_Source_Location: Core Data Center (On-premises ONTAP)
Data_Required_for_Inference: User profile data, product catalog vectors Network_Path: Edge -> WAN -> Core Data Center Observed_Latency: 550ms What is the most likely cause of the high inference latency?

A) The model is too large to fit into the edge server's memory.
B) The on-premises ONTAP system is not configured for high-throughput.
C) Every inference request requires a high-latency round trip over the WAN to fetch data from the core data center.
D) The edge server has insufficient CPU resources to run the model.

Solutions: