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Network Appliance NetApp Certified AI Expert Sample Questions:

1. An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

A) During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
B) Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
C) Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
D) Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.
E) Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
F) Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.

2. 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.

3. A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

A) Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
B) Both the data scientist and the DevOps engineer should use automated production pipelines.
C) The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
D) The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).

4. An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)

A) Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.
B) Use a RAG architecture for the sales forecasting model.
C) Use NetApp XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
D) Use NetApp StorageGRID as the primary storage for the high-performance training stage.
E) Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.
F) Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.

5. An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A) They are networking protocols designed to accelerate data transfer between GPUs.
B) They are specialized storage systems designed to hold large datasets for training.
C) They are integrated development environments (IDEs) used exclusively for writing Python code.
D) They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.

Solutions: