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

A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?

• A. NetApp FabricPool
• B. NetApp FlexClone
• C. NetApp XCP
• D. NetApp SnapMirror

A data scientist needs to launch a Jupyter notebook as a pod in a Kubernetes cluster. The pod requires a 50 Gi persistent volume for storing datasets and notebooks. The cluster administrator has configured a default Trident StorageClass for general-purpose use. The data scientist has the following PersistentVolumeClaim (PVC) manifest:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: jupyter-pvc
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 50Gi
When this PVC is applied to the cluster, what will be the result?

• A. The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
• B. Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
• C. The PVC will fail because a 'storageClassName' is not explicitly defined.
• D. Trident will create a 1 Gi volume, as this is the default size for all PVCs.

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

Advisors report that some queries to the chatbot are unacceptably slow, taking several seconds to respond. The MLOps team isolates the issue to the RAG retrieval step. Performance monitoring of the NetApp AFF A-Series hosting the vector database shows the following metrics during periods of high query load.
avg_read_latency: 3500 microseconds (3.5 ms)
avg_write_latency: 400 microseconds (0.4 ms)
iops_total: 15,000
cpu_utilization_storage_node: 15%
workload_profile: 95% small, random reads
Given these metrics, what is the most likely performance bottleneck?

• A. The storage node CPU is the bottleneck, preventing faster data processing.
• B. The vector database is poorly indexed, causing inefficient, slow lookups that translate to high read latency on the storage.
• C. The network between the application and the storage system is saturated.
• D. The AFF A-Series system is underpowered and cannot handle the IOPS load.