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Network Appliance NetApp Certified AI Expert Sample Questions:

1. Which of the following describes the impact of generative AI in content creation?

A) Generative AI predicts customer preferences without creating new content.
B) Generative AI only processes pre-existing content.
C) Generative AI analyzes data without generating content.
D) Generative AI can create new content such as text, images, and videos.

2. A new team of external auditors requires read-only access to the raw financial product documentation stored in the StorageGRID data lake. The data is in a bucket named 'prod-docs'.
The security team must ensure the auditors can list and read objects but cannot write, delete, or modify any data. Which is the most appropriate method to grant this specific access?

A) Create a new StorageGRID tenant account for the auditors with full S3 access.
B) Provide the auditors with the root access keys for the StorageGRID system.
C) Create a new group and user within StorageGRID, and attach an S3 group policy that explicitly allows only 's3:GetObject' and 's3:ListBucket' actions on the 'prod-docs' bucket.
D) Configure an NFS export policy on the 'prod-docs' bucket and provide the auditors with the mount path.

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

4. A training job on one of the NVIDIA DGX servers is running slowly. A performance engineer runs the 'dstat' command on the server and captures the following output during the job execution.
-total-cpu-usage- -dsk/total- -net/total- paging-- system-- usr sys idl wai hiq siq| read writ| recv send| in out | int csw 15 5 70 10 0 0| 1.2G 15.0M| 1.2G 12.0M| 0 0 | 15k 35k
14 6 69 11 0 0| 1.2G 14.3M| 1.2G 11.8M| 0 0 | 14k 33k
16 5 68 11 0 0| 1.2G 16.1M| 1.2G 13.1M| 0 0 | 16k 36k
The server is connected to the NetApp ASA via a 100GbE (12.5 GB/s) network.
What is the most likely performance bottleneck based on this data?

A) The server's CPU is overloaded.
B) The storage system (ASA) is unable to provide sufficient read throughput.
C) The application is experiencing excessive memory paging.
D) The server's network interface is saturated.

5. 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.

Solutions: