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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q75-Q80):

NEW QUESTION # 75
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 compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.
B. The model was trained using supervised learning, which is inefficient for fraud detection.
C. The network latency is too high for an edge deployment.
D. The storage tier is too slow, causing the GPUs to wait for data.

Answer: A

NEW QUESTION # 76
An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process. Which of the following are the four essential requirements for model generation?

A. A pre-trained model, a validation set, an inference engine, and a cloud provider.
B. A project manager, a data scientist, a software engineer, and a budget.
C. Data, code, compute, and time.
D. A data lake, a data warehouse, a data pipeline, and a data mart.

Answer: C

NEW QUESTION # 77
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. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
B. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
C. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
D. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
E. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
F. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.

Answer: C,D,E,F

NEW QUESTION # 78
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 XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
B. Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.
C. Use NetApp StorageGRID as the primary storage for the high-performance training stage.
D. Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.
E. Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.
F. Use a RAG architecture for the sales forecasting model.

Answer: A,B,E

NEW QUESTION # 79
Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

A. Horizontally scale the cluster by adding new GPU-equipped nodes.
B. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
C. Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
D. Replace all existing GPUs with the next-generation model to increase the performance of each node.
E. Manually deploy all new application replicas to a single, new, very large "super-node".

Answer: A,C

NEW QUESTION # 80
......

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Network Appliance NS0-901 PDF Questions [2026] - Make Your Aspirations Profitable

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2026 100% Free NS0-901 –Useful 100% Free New Braindumps Questions | Guaranteed NS0-901 Success

Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q75-Q80):

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