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Network Appliance NetApp Certified AI Expert Sample Questions:

1. The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

A) It automatically terminates all low-priority jobs to free up the entire cluster.
B) It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
C) It sends an email notification to the administrator to manually reallocate the GPUs.
D) It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.

2. 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) Both projects are examples of fine-tuning and have similar infrastructure needs.
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 B is a fine-tuning task, which leverages an existing model and requires less data and compute resources than building a model from scratch.
E) Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.

3. The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?

A) Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.
B) Enable deduplication and compression on all StorageGRID storage nodes.
C) Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.
D) Implement a FabricPool policy to tier the data to a NetApp ASA system.

4. A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?

A) NetApp FlexCache
B) NetApp SnapMirror
C) NetApp XCP
D) NetApp FabricPool

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

Solutions: