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Network Appliance NetApp Certified AI Expert Sample Questions:

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

2. A media company is building a new generative AI service. The project has two main components:
1. Data Lake & Fine-Tuning: A 300 TB repository of unstructured data (videos, images, text) stored as objects will be used to fine-tune a foundational model. This process requires a scalable, cost-effective storage solution that can integrate with cloud-native data processing tools like Apache Spark.
2. Inference & RAG: The fine-tuned model will be used in a customer-facing application that leverages Retrieval-Augmented Generation (RAG). To ensure low-latency responses, the RAG component requires extremely fast lookups from a 10 TB vector database.
The company needs a solution that optimizes both cost and performance for this entire lifecycle.
Which combination of NetApp technologies provides the most appropriate solution for this scenario?

A) Use NetApp E-Series for the data lake and a NetApp ASA system for the vector database.
B) Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.
C) Use a single, large NetApp ASA system for both the object data lake and the vector database.
D) Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.

3. To comply with the security mandate, the architect must design a process to prevent client PII from the portfolio database from ever being included in the context sent to the LLM. Which two actions are required to build a robust and automated solution for this? (Choose 2.)

A) Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
B) Move the entire client portfolio database to an encrypted, air-gapped system.
C) Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.
D) Implement a data preparation step in the RAG pipeline that programmatically masks or excludes any data fields tagged as PII before the context is constructed.
E) Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.

4. An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.
avg_gpu_utilization: 25%
avg_cpu_iowait_percent: 65%
avg_network_bandwidth_util: 95% (on a 10GbE network)
storage_array_latency: <1ms
Given these metrics, what is the most likely bottleneck causing the low GPU utilization?

A) The storage array is too slow and cannot serve data quickly enough.
B) The GPUs are faulty and cannot process data at their rated speed.
C) The network connecting the compute nodes and storage is saturated and has become the primary bottleneck.
D) The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.

5. 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 ONTAP system's data LIF is offline.
C) The application pod has incorrect mount options configured.
D) The AFF A-Series does not support the NFS protocol.

Solutions: