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Network Appliance NetApp Certified AI Expert Sample Questions:

1. A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

A) The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
B) The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
C) Both the data scientist and the DevOps engineer should use automated production pipelines.
D) Both the data scientist and the DevOps engineer should use Jupyter Notebooks.

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

3. Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)

A) Scheduling GPU-intensive tasks during off-peak hours
B) Utilizing CPU-based workloads for all tasks
C) Using larger batch sizes
D) Balancing compute and storage workloads efficiently

4. A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?

A) The network latency is too high for an edge deployment.
B) The model was trained using supervised learning, which is inefficient for fraud detection.
C) The storage tier is too slow, causing the GPUs to wait for data.
D) The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.

5. A data scientist is using the NetApp DataOps Toolkit for Python to automate the creation of a new, writable volume for an experiment. The script is intended to clone an existing dataset volume. When the script is executed, it fails with an error.
The relevant portion of the Python script is:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="dataset-v1-pvc",
new_pvc_name="experiment-clone-pvc",
namespace="ds-team-1"
)
The script produces the following error in the terminal:
'Error: Failed to clone PVC. Source PVC 'dataset-v1-pvc' not found in namespace 'ds-team-1'.' What is the most likely cause of this error?

A) The Python script is missing the necessary import statement for the toolkit.
B) The NetApp DataOps Toolkit does not support cloning volumes.
C) The Kubernetes cluster does not have NetApp Trident installed.
D) The source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.

Solutions: