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Network Appliance NetApp Certified AI Expert Sample Questions:

1. A new team of external auditors requires read-only access to the raw financial product documentation stored in the StorageGRID data lake. The data is in a bucket named 'prod-docs'.
The security team must ensure the auditors can list and read objects but cannot write, delete, or modify any data. Which is the most appropriate method to grant this specific access?

A) Configure an NFS export policy on the 'prod-docs' bucket and provide the auditors with the mount path.
B) Create a new StorageGRID tenant account for the auditors with full S3 access.
C) Provide the auditors with the root access keys for the StorageGRID system.
D) Create a new group and user within StorageGRID, and attach an S3 group policy that explicitly allows only 's3:GetObject' and 's3:ListBucket' actions on the 'prod-docs' bucket.

2. An AI team is planning two separate projects. The architect needs to provision the appropriate infrastructure for each.
| | Project A | Project B|
| -- | | - |
| Goal | Build a novel image recognition model from scratch.
| Adapt an existing, pre- trained LLM to understand company-specific jargon. |
| Input Data | 10 million new, unlabeled images. | A 50 GB text corpus of internal documents. |
| Required Compute | Very High (Weeks of multi-GPU training) | Moderate (Hours of single-GPU training) | Which two statements accurately describe the infrastructure requirements for these projects?
(Choose two.)

A) Project B is a fine-tuning task, which leverages an existing model and requires less data and compute resources than building a model from scratch.
B) Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.
C) Project A requires only a large amount of data, while Project B requires only a large amount of code.
D) Both projects are examples of fine-tuning and have similar infrastructure needs.
E) Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.

3. A development team is building a generative AI application that must answer questions based on a constantly changing internal knowledge base of company documents. They want to provide the model with up-to-date information without altering its core weights and capabilities.
Which approach is most suitable for this requirement?

A) Fine-tuning, which retrains the model's last few layers on the new documents.
B) Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.
C) Unsupervised learning, which clusters the documents into topics for the model to browse.
D) Predictive AI, which analyzes the documents to forecast future questions.

4. An architect is designing a cost-optimized storage solution for a large AI environment that has two distinct data temperature profiles:
1. Hot Data (200 TB): Actively used training sets and models requiring all-flash performance.
2. Cold Data (2 PB): Inactive, archived datasets and old model versions that must remain online but are infrequently accessed.
The solution must automatically manage data placement based on activity, without requiring manual intervention or changes to file paths. The environment consists of an on-premises data center and a public cloud account.
Which combination of NetApp technologies provides the most complete and cost-effective solution? (Select all that apply.)

A) Deploy a NetApp AFF A-Series system on-premises to serve as the high-performance tier for the hot data.
B) Use NetApp SnapMirror to create a full copy of the 2 PB of cold data on the AFF A-Series for faster access if needed.
C) Deploy NetApp StorageGRID on-premises to serve as a cost-effective capacity tier for the cold data.
D) Deploy a single, large Cloud Volumes ONTAP instance to hold both the hot and cold data tiers.
E) Use NetApp FabricPool to automatically and transparently tier cold data blocks from the AFF A- Series to the StorageGRID system.
F) Use NetApp XCP to manually script the daily movement of cold files from the AFF system to StorageGRID.

5. An AI research team is experiencing slow model training times. Their performance monitoring indicates that the GPUs are frequently idle, waiting for data. They want to implement a single technology change to create a more direct data path between their storage and GPUs.
Their current setup is as follows:
Compute: Server with NVIDIA A100 GPUs
Storage: NetApp AFF A-Series (All-Flash)
Network: 100GbE Ethernet
Data_Path: Storage -> Host CPU/Memory -> GPU Memory
Which technology should the architect recommend to specifically address this data path inefficiency?

A) GPUDirect Storage
B) A faster CPU in the server
C) NetApp SnapMirror
D) NetApp FabricPool

Solutions: