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Network Appliance NetApp Certified AI Expert Sample Questions:

1. Which of the following platforms provides tools for model training and deployment specifically for AI workloads?

A) RunAI
B) All of the above
C) Google VertexAI
D) Domino Data Labs

2. To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

A) NetApp SnapLock to make the source data immutable before scanning.
B) NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
C) A custom "PII" category within BlueXP classification to identify specific medical record number formats.
D) A custom Python script that uses regular expressions to search file contents.
E) A manual review process where technicians visually inspect each scan for PII.

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

4. The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?

A) Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.
B) Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
C) Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.
D) Replace the disks in the existing storage nodes with higher-capacity drives.

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

Solutions: