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Network Appliance NetApp Certified AI Expert Sample Questions:

1. A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?

A) NetApp SnapMirror
B) NetApp FlexClone
C) NetApp XCP
D) NetApp FabricPool

2. A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)

A) Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
B) Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
C) Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
D) Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
E) Use a standard 10GbE network for all connectivity to reduce costs.
F) Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.

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

4. A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?

A) The model was trained using supervised learning, which is inefficient for fraud detection.
B) The network latency is too high for an edge deployment.
C) The storage tier is too slow, causing the GPUs to wait for data.
D) The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.

5. An AI architect is planning the resource allocation for a new project. The primary task is to process millions of unlabeled customer reviews to identify naturally occurring groups or themes without any prior guidance.
The project requirements are summarized below:
Task: Discover hidden patterns in text data
Input_Data: 10 million unlabeled text reviews
Output: Clustered groups of related reviews
Supervision: None
Which type of machine learning algorithm is required for this task?

A) Unsupervised learning
B) Predictive learning
C) Supervised learning
D) Reinforcement learning

Solutions: