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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Overview | 15% | - AI, machine learning, and deep learning concepts - Convergence of AI, high-performance computing, and analytics - AI industry use cases and applications - AI deployment models: on-premises, cloud, edge - Algorithm types: supervised, unsupervised, reinforcement learning |
| Topic 2: NetApp AI Solutions and Architecture | 25% | - Scalability and performance optimization for AI - Storage architectures for AI workloads - NetApp AI-ready infrastructure components - Data management and data pipeline design - ONTAP integration with AI frameworks |
| Topic 3: Security, Reliability, and Operations | 15% | - Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - High availability and data protection - Cost management and efficiency |
| Topic 4: AI Lifecycle | 27% | - Model training, inference, and optimization - AI governance, ethics, and compliance - Predictive vs generative AI - AI lifecycle stages: design, training, deployment, monitoring - Data preparation and management for AI |
| Topic 5: Cloud and Hybrid Cloud AI Deployment | 18% | - Hybrid and multi-cloud AI architectures - Cloud-native AI solutions and integration - Data mobility and consistency across environments - NetApp cloud data services for AI |
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NEW QUESTION # 19
A team has deployed a Retrieval-Augmented Generation (RAG) system to answer customer queries. Recently, users have complained that the answers provided by the chatbot are outdated and do not reflect the latest product updates. An architect investigates and finds the following status log from the RAG pipeline's data ingestion monitor.
Timestamp: 2025-07-11T14:00:00Z
System: RAG Pipeline Monitor
Status: WARNING
Message: Vector DB freshness check failed.
Source data appears stale.
Vector_DB_Last_Update: 2025-06-10T08:00:00Z
Knowledge_Base_Last_Modified: 2025-07-11T13:15:00Z
Data_Sync_Service: BlueXP copy and sync
Sync_Job_Status: Succeeded
Based on the log, what is the most likely cause of the outdated answers?
Answer: D
NEW QUESTION # 20
A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)
Answer: B,C
NEW QUESTION # 21
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?
Answer: D
NEW QUESTION # 22
Which AI technology is used to generate new, never-before-seen content such as images or text?
Answer: D
NEW QUESTION # 23
A financial services company is required by regulators to be able to trace any version of their deployed fraud detection model back to the exact dataset and source code commit used to train it.
The current MLOps workflow is as follows:
Code_Repository: Git (commit hash: a1b2c3d4)
Dataset_Location: /vol/prod_data/fraud_dataset_v3
Storage_System: NetApp ONTAP 9
Model_Output: /vol/models/fraud_model_v3.2
Which NetApp technology should be used to create an immutable, point-in-time, and space- efficient copy of the dataset that can be linked to the specific code commit and model version?
Answer: A
NEW QUESTION # 24
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