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Network Appliance NetApp Certified AI Expert Sample Questions:

1. Advisors report that some queries to the chatbot are unacceptably slow, taking several seconds to respond. The MLOps team isolates the issue to the RAG retrieval step. Performance monitoring of the NetApp AFF A-Series hosting the vector database shows the following metrics during periods of high query load.
avg_read_latency: 3500 microseconds (3.5 ms)
avg_write_latency: 400 microseconds (0.4 ms)
iops_total: 15,000
cpu_utilization_storage_node: 15%
workload_profile: 95% small, random reads
Given these metrics, what is the most likely performance bottleneck?

A) The network between the application and the storage system is saturated.
B) The vector database is poorly indexed, causing inefficient, slow lookups that translate to high read latency on the storage.
C) The AFF A-Series system is underpowered and cannot handle the IOPS load.
D) The storage node CPU is the bottleneck, preventing faster data processing.

2. The architect is designing the complete, automated data pipeline from the on-premises data center to the Azure cloud for this medical imaging project. The design must prioritize security, efficiency, and reproducibility.
Which sequence of actions provides the most robust and automated solution?

A) Manually copy raw data to Azure, run a cloud-based script to anonymize it, then start training.
B) Use BlueXP classification to scan the on-prem data; use a DataOps Toolkit script to trigger a FlexClone of the source volume for anonymization; use SnapMirror to replicate the anonymized clone to Cloud Volumes ONTAP; then begin training in Azure.
C) Use SnapMirror to replicate the raw, sensitive data to Azure first, then run BlueXP classification in the cloud to find PII.
D) Use FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.

3. Which of the following platforms provides tools for model training and deployment specifically for AI workloads?

A) All of the above
B) Domino Data Labs
C) Google VertexAI
D) RunAI

4. To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

A) A custom "PII" category within BlueXP classification to identify specific medical record number formats.
B) A custom Python script that uses regular expressions to search file contents.
C) NetApp SnapLock to make the source data immutable before scanning.
D) NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
E) A manual review process where technicians visually inspect each scan for PII.

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

Solutions: