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Network Appliance NetApp Certified AI Expert Sample Questions:

1. An organization wants to provide its data science team with a secure, on-demand method for using a powerful generative AI model with their private, sensitive corporate data. The solution must ensure that the private data is never exposed to the public internet or the public LLM API endpoint.
The architect is designing a solution using BlueXP.
Which two components are essential for building this secure solution? (Choose 2.)

A) A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
B) A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
C) A direct VPN connection from each data scientist's laptop to the public LLM provider.
D) A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
E) The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.

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

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

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

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

5. The firm's CFO is concerned about the rising costs of the on-premises AI infrastructure. A storage utilization report shows that of the 200 TB of data on the high-performance AFF A-Series, 150 TB consists of inactive, older versions of product documents that are rarely accessed but must be kept online for regulatory reasons.
The current storage landscape is:
- Performance Tier: NetApp AFF A-Series (200 TB used)
- Capacity Tier: NetApp StorageGRID (1.5 PB used)
What is the most effective and automated solution to reduce the storage cost of the performance tier without impacting data accessibility?

A) Manually identify and delete the 150 TB of inactive data from the AFF A-Series.
B) Implement NetApp FabricPool to automatically tier the inactive data blocks from the AFF A-Series to the StorageGRID system.
C) Purchase an additional, larger AFF A-Series system to gain better storage efficiency through deduplication.
D) Use NetApp SnapMirror to replicate the entire 200 TB volume to the StorageGRID system and then delete the source.

Solutions: