Network Appliance NetApp Certified AI Expert NS0-901

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Network Appliance NetApp Certified AI Expert Sample Questions:

1. An online retail company's recommendation engine, which provides real-time product suggestions to users, is experiencing unacceptable latency. The inference application is running on a correctly-sized edge server, but user requests are taking over 500ms to process. An architect reviews the data access pattern and infrastructure diagram.
Application_Location: Edge Server (In-store)
Data_Source_Location: Core Data Center (On-premises ONTAP)
Data_Required_for_Inference: User profile data, product catalog vectors Network_Path: Edge -> WAN -> Core Data Center Observed_Latency: 550ms What is the most likely cause of the high inference latency?

A) Every inference request requires a high-latency round trip over the WAN to fetch data from the core data center.
B) The on-premises ONTAP system is not configured for high-throughput.
C) The model is too large to fit into the edge server's memory.
D) The edge server has insufficient CPU resources to run the model.

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

3. An architect is explaining different data storage paradigms to a new data engineering team. The team needs to understand where to store different types of data for their AI projects. Which statement accurately describes a key difference between a data lake and a data warehouse?

A) A data lake is designed to store vast quantities of raw, multi-format data (structured, semi- structured, and unstructured), while a data warehouse is typically used for storing structured and filtered data that has already been processed for a specific purpose.
B) A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.
C) A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.
D) A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.

4. 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) Scheduling GPU-intensive tasks during off-peak hours
C) Using larger batch sizes
D) Balancing compute and storage workloads efficiently

5. A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

A) The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
B) Both the data scientist and the DevOps engineer should use automated production pipelines.
C) Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
D) The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.

Solutions: