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Network Appliance NetApp Certified AI Expert Sample Questions:

1. A healthcare organization plans to use a large dataset of patient records to train a predictive model. Before training, they must identify and segregate all records containing Personally Identifiable Information (PII) to comply with privacy regulations. The data resides on an on- premises NetApp ONTAP cluster. The organization needs an automated tool to scan the data in- place and tag files containing PII without moving the data.
The project requirements are as follows:
Task: Identify PII in a large dataset.
Data_Location: On-premises ONTAP cluster.
Constraint: Data must not be moved from its source location for scanning.
Output: Tagged files containing PII.
Which NetApp tool is designed for this specific task?

A) NetApp SnapMirror
B) NetApp XCP
C) NetApp BlueXP classification
D) NetApp FlexCache

2. Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

A) Parallel file systems
B) Object-based storage
C) POSIX-based file systems
D) File-based systems

3. An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable.
Users at the edge are reporting slow model loading times.
An architect reviews the data access logs from an edge site:
Timestamp: 2025-07-11T15:30:00Z
Event: Model_Load_Request
Model_Path: nfs://core-filer.example.com/vol/models/latest_model.pkl
Source_IP: 192.168.100.15 (Edge Server)
Destination_IP: 10.1.1.50 (Core Filer)
Status: SUCCESS
Duration: 3600s (60 minutes)
What is the most likely cause of the slow model loading times at the edge?

A) The core ONTAP filer is using slow, capacity-based disks.
B) The NFS version used between the core and edge is outdated.
C) The model file is being transferred over a slow, high-latency WAN link for every load request.
D) The edge server does not have enough RAM to cache the model effectively.

4. Which of the following are typical requirements for inferencing in AI models? (Choose two)

A) Cloud-based inference only
B) Loading the model into memory
C) Pre-trained models
D) Extensive training data

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

Solutions: