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Network Appliance NetApp Certified AI Expert Sample Questions:

1. Which of the following applications use AI in the healthcare industry? (Choose two)

A) Predicting patient outcomes
B) Automating financial reporting
C) Diagnosing diseases
D) Managing hospital supply chains

2. A distributed training job running on the AIPod fails to start. The MLOps engineer inspects the events for one of the pending training pods and sees the following message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 5m12s default-scheduler 0/4 nodes are available: 4 node(s) had no available volume zone.
The PersistentVolumeClaim (PVC) for this pod specifies a StorageClass that uses the 'ontap-nas' Trident provisioner.
he Trident logs show no errors.
What is the most likely cause of this scheduling failure?

A) The 'requiredTopology' in the StorageClass is misconfigured, preventing Trident from finding a valid ONTAP backend in the same zone as the available compute nodes.
B) The NetApp ASA storage system is offline.
C) The training pod's container image does not exist in the registry.
D) The Kubernetes nodes do not have the necessary NFS client utilities installed.

3. Which of the following are typical requirements for inferencing in AI models? (Choose two)

A) Extensive training data
B) Cloud-based inference only
C) Pre-trained models
D) Loading the model into memory

4. What is the primary architectural benefit of using technologies like RDMA (Remote Direct Memory Access) and GPUDirect Storage in a high-performance AI training cluster?

A) They reduce the power consumption of the storage array by offloading checksum calculations.
B) They allow GPUs to communicate directly with each other and with NVMe storage, bypassing the host CPU and system memory, which significantly reduces I/O latency and CPU overhead.
C) They automatically encrypt data in-flight between the GPU servers and the storage system.
D) They enable the use of lower-cost Ethernet switches in place of InfiniBand fabrics without any performance degradation.

5. A development team is building a generative AI application that must answer questions based on a constantly changing internal knowledge base of company documents. They want to provide the model with up-to-date information without altering its core weights and capabilities.
Which approach is most suitable for this requirement?

A) Fine-tuning, which retrains the model's last few layers on the new documents.
B) Unsupervised learning, which clusters the documents into topics for the model to browse.
C) Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.
D) Predictive AI, which analyzes the documents to forecast future questions.

Solutions: