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Network Appliance NetApp Certified AI Expert Sample Questions:

1. An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)

A) Use NetApp StorageGRID as the primary storage for the high-performance training stage.
B) Use NetApp XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
C) Use a RAG architecture for the sales forecasting model.
D) Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.
E) Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.
F) Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.

2. The architect is designing the complete, automated data pipeline from the on-premises data center to the Azure cloud for this medical imaging project. The design must prioritize security, efficiency, and reproducibility.
Which sequence of actions provides the most robust and automated solution?

A) Use FabricPool to automatically tier all raw data to Azure Blob storage, then mount the blob container to the training VMs.
B) Use BlueXP classification to scan the on-prem data; use a DataOps Toolkit script to trigger a FlexClone of the source volume for anonymization; use SnapMirror to replicate the anonymized clone to Cloud Volumes ONTAP; then begin training in Azure.
C) Manually copy raw data to Azure, run a cloud-based script to anonymize it, then start training.
D) Use SnapMirror to replicate the raw, sensitive data to Azure first, then run BlueXP classification in the cloud to find PII.

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

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

4. 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 AFF A-Series system is underpowered and cannot handle the IOPS load.
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 storage node CPU is the bottleneck, preventing faster data processing.

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

Solutions: