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Last Updated: Aug 22, 2026
No. of Questions: 106 Questions & Answers with Testing Engine
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: NetApp AI Solutions and Architecture | 25% | - NetApp AI-ready infrastructure components - Data management and data pipeline design - Scalability and performance optimization for AI - ONTAP integration with AI frameworks - Storage architectures for AI workloads |
| Topic 2: AI Lifecycle | 27% | - Predictive vs generative AI - Data preparation and management for AI - Model training, inference, and optimization - AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance |
| Topic 3: AI Overview | 15% | - Algorithm types: supervised, unsupervised, reinforcement learning - AI deployment models: on-premises, cloud, edge - AI, machine learning, and deep learning concepts - AI industry use cases and applications - Convergence of AI, high-performance computing, and analytics |
| Topic 4: Cloud and Hybrid Cloud AI Deployment | 18% | - NetApp cloud data services for AI - Hybrid and multi-cloud AI architectures - Data mobility and consistency across environments - Cloud-native AI solutions and integration |
| Topic 5: Security, Reliability, and Operations | 15% | - Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - Cost management and efficiency - High availability and data protection |
1. A healthcare organization plans to use a large dataset of patient records to train a predictive model. Before training, they must identify and segregate all records containing Personally Identifiable Information (PII) to comply with privacy regulations. The data resides on an on- premises NetApp ONTAP cluster. The organization needs an automated tool to scan the data in- place and tag files containing PII without moving the data.
The project requirements are as follows:
Task: Identify PII in a large dataset.
Data_Location: On-premises ONTAP cluster.
Constraint: Data must not be moved from its source location for scanning.
Output: Tagged files containing PII.
Which NetApp tool is designed for this specific task?
A) NetApp XCP
B) NetApp SnapMirror
C) NetApp FlexCache
D) NetApp BlueXP classification
2. An organization wants to provide its data science team with a secure, on-demand method for using a powerful generative AI model with their private, sensitive corporate data. The solution must ensure that the private data is never exposed to the public internet or the public LLM API endpoint.
The architect is designing a solution using BlueXP.
Which two components are essential for building this secure solution? (Choose 2.)
A) A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
B) A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
C) The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.
D) A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
E) A direct VPN connection from each data scientist's laptop to the public LLM provider.
3. A new team of external auditors requires read-only access to the raw financial product documentation stored in the StorageGRID data lake. The data is in a bucket named 'prod-docs'.
The security team must ensure the auditors can list and read objects but cannot write, delete, or modify any data. Which is the most appropriate method to grant this specific access?
A) Create a new group and user within StorageGRID, and attach an S3 group policy that explicitly allows only 's3:GetObject' and 's3:ListBucket' actions on the 'prod-docs' bucket.
B) Provide the auditors with the root access keys for the StorageGRID system.
C) Create a new StorageGRID tenant account for the auditors with full S3 access.
D) Configure an NFS export policy on the 'prod-docs' bucket and provide the auditors with the mount path.
4. A training job on one of the NVIDIA DGX servers is running slowly. A performance engineer runs the 'dstat' command on the server and captures the following output during the job execution.
-total-cpu-usage- -dsk/total- -net/total- paging-- system-- usr sys idl wai hiq siq| read writ| recv send| in out | int csw 15 5 70 10 0 0| 1.2G 15.0M| 1.2G 12.0M| 0 0 | 15k 35k
14 6 69 11 0 0| 1.2G 14.3M| 1.2G 11.8M| 0 0 | 14k 33k
16 5 68 11 0 0| 1.2G 16.1M| 1.2G 13.1M| 0 0 | 16k 36k
The server is connected to the NetApp ASA via a 100GbE (12.5 GB/s) network.
What is the most likely performance bottleneck based on this data?
A) The server's network interface is saturated.
B) The storage system (ASA) is unable to provide sufficient read throughput.
C) The application is experiencing excessive memory paging.
D) The server's CPU is overloaded.
5. Which of the following platforms provides tools for model training and deployment specifically for AI workloads?
A) Google VertexAI
B) RunAI
C) Domino Data Labs
D) All of the above
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A,C | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: D |
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