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| Section | Objectives |
|---|
| Topic 1: AI Infrastructure and NetApp Solutions | - AI-ready data infrastructure
- 1. High-performance storage for AI workloads
- 2. Data pipeline optimization for AI/ML workloads
- Converged workloads
- 1. Shared infrastructure design considerations
- 2. AI, HPC, and analytics convergence
|
| Topic 2: AI and Machine Learning Fundamentals | - Algorithm types
- 1. Supervised learning
- 2. Unsupervised learning
- 3. Reinforcement learning
- AI, ML, DL concepts
- 1. Training, inference, and prediction workflows
- 2. Differences between AI, machine learning, and deep learning
|
| Topic 3: Industry Use Cases | - AI applications across industries
- 1. Digital twins
- 2. Healthcare AI
- 3. Autonomous systems and agents
|
| Topic 4: AI Lifecycle and Deployment | - End-to-end AI lifecycle
- 1. Data ingestion and preparation
- 2. Model training and validation
- 3. Model deployment and monitoring
- Operational challenges
- 1. Scalability and performance optimization
- 2. Data governance and management
|
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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q74-Q79):
NEW QUESTION # 74
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 automated production pipelines.
- B. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
- C. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
- D. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
Answer: D
NEW QUESTION # 75
An automotive company runs crash simulations on a dedicated High-Performance Computing (HPC) cluster and trains computer vision models on a separate AI cluster. Data scientists are complaining about the long delays required to move terabytes of simulation output data from the HPC storage to the AI cluster's storage before they can begin training.
The current data flow is as follows:
HPC Cluster -> --Manual Copy (NFS)--> -> AI Cluster
An architect has been asked to redesign the infrastructure to eliminate this data movement bottleneck.
Which architectural change would be most effective?
- A. Upgrade the network connection between the two storage systems to 200GbE.
- B. Install faster CPUs in the AI cluster's storage controllers.
- C. Implement a converged data infrastructure where both the HPC and AI clusters access a single, high- performance data lake built on NetApp storage.
- D. Use NetApp XCP to perform the data copy, as it is faster than a standard NFS copy.
Answer: C
NEW QUESTION # 76
A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?
- A. NetApp SnapMirror
- B. NetApp XCP
- C. NetApp FabricPool
- D. NetApp FlexCache
Answer: A
NEW QUESTION # 77
An organization's AI platform team needs to provide two distinct tiers of storage for their data scientists on a single Kubernetes cluster:
1. 'gold-tier': Extremely low-latency storage for active model training, using an all-flash NetApp ASA system.
2. 'bronze-tier': Cost-effective, high-capacity storage for data staging and archiving, using a NetApp StorageGRID system.
How should the platform team configure NetApp Trident to meet these requirements? (Select all that apply.)
- A. Configure the 'gold-tier' StorageClass to reference the ASA backend.
- B. Create a single StorageClass named 'multi-tier-storage' that references both backends.
- C. Create two distinct StorageClasses, 'gold-tier' and 'bronze-tier'.
- D. Create a single Trident backend that points to both the ASA and StorageGRID systems simultaneously.
- E. Configure the 'bronze-tier' StorageClass to reference the StorageGRID backend.
- F. Create two separate Trident backend configurations, one for the ASA ('backend-asa.yaml') and one for StorageGRID ('backend-sgrid.yaml').
Answer: A,C,E,F
NEW QUESTION # 78
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)
- A. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
- B. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
- C. Use a standard 10GbE network for all connectivity to reduce costs.
- D. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
- E. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
- F. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
Answer: A,B,E,F
NEW QUESTION # 79
......
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