NetApp Certified AI Expert Exam (NS0-901) Free Practice Test

Question 1

A research institute is designing an infrastructure to support its entire AI drug discovery pipeline.
The pipeline has two distinct workload requirements:
1. Training: A team of data scientists needs to train several large transformer models simultaneously using a 500 TB dataset of genomic sequences. This process requires maximum data throughput to keep the GPUs saturated.
2. Inference: Once trained, the models are deployed to an internal web portal where researchers submit individual protein sequences for analysis. These queries must return results with the lowest possible latency.
Which infrastructure design best satisfies both requirements? (Choose 2.)

A. Implement NetApp FlexCache on smaller nodes at the network edge to serve the inference requests.

B. Configure QoS minimums on the training volumes to ensure they do not impact inference performance.

C. Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.

D. Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.

E. Use a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.

Correct Answer: A,D

Question 2

What is the primary role of NetApp Trident in a Kubernetes environment designed for AI workloads?

A. To function as a container runtime interface for executing AI models.

B. To act as a dynamic storage orchestrator, provisioning persistent storage from NetApp backends on- demand for containerized applications.

C. To provide a web-based IDE, like Jupyter, for data scientists to develop models.

D. To directly accelerate GPU computations using specialized drivers.

Correct Answer: B

Question 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. Unsupervised learning, which clusters the documents into topics for the model to browse.

C. Predictive AI, which analyzes the documents to forecast future questions.

D. Fine-tuning, which retrains the model's last few layers on the new documents.

Correct Answer: A

Question 4

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 orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.

C. They are specialized storage systems designed to hold large datasets for training.

D. They are integrated development environments (IDEs) used exclusively for writing Python code.

Correct Answer: B

Question 5

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 a RAG architecture for the sales forecasting model.

C. Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.

D. Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.

E. Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.

F. Use NetApp StorageGRID as the primary storage for the high-performance training stage.

Correct Answer: A,C,E

Question 6

The "Advisor Assistant" application, running as a pod in Kubernetes, suddenly cannot access its data on the AFF A-Series. The application logs show "connection timed out" errors. The network team provides a firewall log snippet for the traffic between the application pod and the storage system's NFS data LIF.
TIME | SRC_IP | DST_IP | PROTO | DST_PORT | ACTION
-|--||-|-|-
2025-07-11T16:01:10Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 111 | BLOCKED 2025-07-
11T16:01:12Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 2049 | BLOCKED What is the most likely cause of the connectivity failure?

A. A network firewall between the pod's subnet and the storage subnet is blocking the required NFS ports.

B. The application pod has incorrect mount options configured.

C. The AFF A-Series does not support the NFS protocol.

D. The ONTAP system's data LIF is offline.

Correct Answer: A

Question 7

Which AI technology is used to generate new, never-before-seen content such as images or text?

A. Predictive AI

B. Generative AI

C. Supervised AI

D. Reinforcement AI

Correct Answer: B

Question 8

The company decides to establish a disaster recovery (DR) site in a secondary data center for the entire Digital Twin platform. The DR plan must protect the HPC data, the AI training data, and the central data lake.
The DR requirements are:
- RPO: 4 hours for all data.
- RTO: 24 hours for the entire platform.
- Process: The failover and failback process should be as automated as possible.
Which combination of technologies provides the most comprehensive DR solution?

A. Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.

B. Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.

C. Use NetApp SnapMirror for the ASA, StorageGRID's built-in replication for the data lake, and a host- based tool like 'rsync' for the E-Series/BeeGFS data, all orchestrated via BlueXP disaster recovery.

D. Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.

Correct Answer: C

Question 9

Which of the following applications use AI in the healthcare industry? (Choose two)

A. Automating financial reporting

B. Diagnosing diseases

C. Predicting patient outcomes

D. Managing hospital supply chains

Correct Answer: B,C

Question 10

A. Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.

B. Both projects are examples of fine-tuning and have similar infrastructure needs.

C. Project A requires only a large amount of data, while Project B requires only a large amount of code.

D. Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.

E. 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.

Correct Answer: A,E

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