Network Appliance NetApp Certified AI Expert Sample Questions:

1. What is the primary difference between C-Series and A-Series storage for AI workloads?

A) A-Series provides GPU-based acceleration, while C-Series only supports CPU-based workloads.
B) C-Series supports both file and object-based storage, while A-Series is optimized for object storage.
C) A-Series is for smaller-scale AI workloads, while C-Series supports large-scale, high-performance AI applications.
D) C-Series is designed for high-performance workloads, while A-Series focuses on cost-efficiency for general storage.

2. An AI platform team is investigating poor I/O performance for a specific workload that involves processing hundreds of thousands of small metadata files. The application is running on a Kubernetes cluster with storage provided by a NetApp ONTAP system over NFS. Performance metrics show acceptable network throughput but very high latency for metadata operations (e.g., open, stat, close).
The current storage configuration is as follows:
Storage_System: NetApp AFF A-Series
Protocol: NFSv4.1
Workload_Profile: Metadata-intensive, many small file lookups
Observed_Issue: High latency on metadata operations, slow job completion Which storage architecture would be better suited to handle this specific metadata-intensive workload?

A) A NetApp Cloud Volumes ONTAP instance in a different region.
B) A NetApp E-Series system running a parallel file system like BeeGFS.
C) A NetApp SnapLock-enabled volume for data protection.
D) A NetApp StorageGRID object storage system.

3. 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 FabricPool
B) NetApp SnapMirror
C) NetApp XCP
D) NetApp FlexCache

4. 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 a single, large Cloud Volumes ONTAP instance in a public cloud to handle both workloads to simplify management.
D) Use NetApp StorageGRID as the primary storage for both the training and low-latency inference workloads.
E) Deploy a large NetApp ASA cluster with GPUDirect Storage enabled for the training environment.

5. What is the primary architectural advantage of using a NetApp AIPod with NVIDIA DGX servers for the AI training cluster, as described in the scenario?

A) It is a reference architecture that is pre-validated by NetApp and NVIDIA to eliminate design complexity and ensure predictable performance for AI workloads.
B) It is designed for small-scale, departmental AI projects and cannot be scaled.
C) It prioritizes CPU performance over GPU performance for traditional machine learning algorithms.
D) It exclusively uses object storage, which simplifies access for data scientists using S3-native tools.

Solutions: