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Network Appliance NetApp Certified AI Expert Sample Questions:

1. The data science team in Azure reports that training jobs are taking longer than expected. An analysis of the Cloud Volumes ONTAP instance in Azure shows that the instance type is undersized for the I/O demands of the training workload. The architect needs to change the Azure VM instance type for the Cloud Volumes ONTAP system to a more powerful one.
The current configuration is:
Cloud_Provider: Azure
ONTAP_System: Cloud Volumes ONTAP (Single Node)
Current_Instance_Type: Standard_DS3_v2
Target_Instance_Type: Standard_E8s_v4
What is the most direct method to perform this operation using NetApp's management tools?

A) From the BlueXP Canvas, select the Cloud Volumes ONTAP working environment and use the
"Change Instance Type" action in the Features panel.
B) Terminate the existing Cloud Volumes ONTAP instance and deploy a new one with the target instance type, then restore data from backup.
C) Use the Azure portal to manually change the VM instance type.
D) SSH to the Cloud Volumes ONTAP instance and run an ONTAP command to modify the underlying VM.

2. An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?

A) NetApp C-Series
B) NetApp StorageGRID
C) NetApp E-Series
D) NetApp ASA (All-SAN Array)

3. 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 sends an email notification to the administrator to manually reallocate the GPUs.
C) It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
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.

4. Which AI technology is used to generate new, never-before-seen content such as images or text?

A) Predictive AI
B) Generative AI
C) Supervised AI
D) Reinforcement AI

5. Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

A) Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.
B) Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.
C) Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.
D) Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.

Solutions: