Network Appliance NetApp Certified AI Expert Sample Questions:

1. 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 stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.
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 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.

2. The AI training jobs on the AIPod are performing below expectations. The NVIDIA DGX servers' GPUs show low utilization. A performance analysis reveals that the bottleneck is not the storage system itself, but the network path between the storage and the compute nodes.
The current network configuration is as follows:
Network_Fabric: 100GbE Standard Ethernet
Protocol: NFS over TCP/IP
Data_Path: Storage -> Host CPU -> GPU Memory
Which network architecture enhancement would provide the most significant performance improvement by reducing latency and CPU overhead?

A) Isolate the storage traffic on a separate VLAN from the management traffic.
B) Implement RDMA over Converged Ethernet (RoCE) and configure GPUDirect Storage.
C) Upgrade the network switches to a model with a larger packet buffer.
D) Add more 100GbE network ports to the storage controllers.

3. A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?

A) Generative learning
B) Supervised learning
C) Unsupervised learning
D) Reinforcement learning

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

5. The data scientists report that their Kubernetes-based data preparation jobs are failing. The pods are stuck in a 'Pending' state.
An MLOps engineer runs 'kubectl describe pvc data-prep-pvc-01' and sees the following event:
Type Reason Age From Message
- - - -
Warning ProvisioningFailed 2m15s trident-orchestrator-7b... failed to provision volume with StorageClass "bronze-tier": backend unavailable: no healthy backend with satisfying attributes for storage class "bronze-tier" The engineer checks the Trident backend configurations and finds no backend associated with the "bronze-tier" StorageClass.
What is the root cause of the failure?

A) The StorageGRID data lake is offline, preventing the creation of new volumes.
B) The PersistentVolumeClaim is referencing a StorageClass that has no configured or healthy Trident backend to provision storage from.
C) The Kubernetes scheduler is unable to find a node with enough resources for the pods.
D) The data preparation pods do not have the correct security permissions.

Solutions: