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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q103-Q108):

NEW QUESTION # 103
Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

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

Answer: A

NEW QUESTION # 104
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 automatically encrypt data in-flight between the GPU servers and the storage system.
C. 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.
D. They enable the use of lower-cost Ethernet switches in place of InfiniBand fabrics without any performance degradation.

Answer: C

NEW QUESTION # 105
To comply with the security mandate, the architect must design a process to prevent client PII from the portfolio database from ever being included in the context sent to the LLM. Which two actions are required to build a robust and automated solution for this? (Choose 2.)

A. Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.
B. Implement a data preparation step in the RAG pipeline that programmatically masks or excludes any data fields tagged as PII before the context is constructed.
C. Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.
D. Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
E. Move the entire client portfolio database to an encrypted, air-gapped system.

Answer: A,B

NEW QUESTION # 106
An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.
avg_gpu_utilization: 25%
avg_cpu_iowait_percent: 65%
avg_network_bandwidth_util: 95% (on a 10GbE network)
storage_array_latency: <1ms
Given these metrics, what is the most likely bottleneck causing the low GPU utilization?

A. The GPUs are faulty and cannot process data at their rated speed.
B. The network connecting the compute nodes and storage is saturated and has become the primary bottleneck.
C. The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.
D. The storage array is too slow and cannot serve data quickly enough.

Answer: B

NEW QUESTION # 107
An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?

A. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
B. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
C. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
D. Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
E. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.

Answer: D

NEW QUESTION # 108
......

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Accurate Network Appliance NS0-901 Exam Dumps With 100% Success Rate

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Certification NS0-901 Dump - NS0-901 Reliable Test Notes

Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q103-Q108):

Free PDF Quiz First-grade Network Appliance NS0-901 - NetApp Certified AI Expert Exam Testking Learning Materials

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