NCA-AIIO Practice Questions: Data Management and Storage Domain

Published: June 12, 2025 | 5 min read

Test your NCA-AIIO knowledge with 5 practice questions from the Data Management and Storage domain. Includes detailed explanations and answers.

NCA-AIIO Practice Questions

Master Data Management and Storage

Storage Foundation: Data management builds on hardware architecture knowledge. Complete our Hardware and System Architecture practice questions first, then review our Complete NCA-AIIO Study Guide for comprehensive context.

Master Data Management and Storage with practice questions covering distributed storage systems, data pipeline optimization, backup strategies, and storage architectures for AI workloads.

Performance Connection

Storage performance directly impacts AI training efficiency. After mastering these concepts, advance to our Performance Optimization and Monitoring practice questions to understand storage bottleneck identification and optimization strategies.

Question 1: Distributed Storage Systems

When designing a distributed storage system for large-scale AI training datasets, which storage pattern provides the best balance of performance, scalability, and fault tolerance?

A) Single centralized NAS

B) Distributed parallel file system with data striping

C) Local storage only

D) Cloud storage with internet access

Show Answer & Explanation

Correct Answer: B

Explanation: Distributed parallel file systems with data striping (like Lustre or GPFS) provide high aggregate bandwidth through parallel access, built-in redundancy, and can scale to petabyte capacity. This storage architecture connects to the hardware concepts covered in our Hardware and System Architecture practice questions.

Question 2: Data Pipeline Optimization

In an AI training pipeline, what is the most effective strategy to minimize data loading bottlenecks when training with large datasets?

A) Load entire dataset into GPU memory at startup

B) Implement asynchronous data loading with prefetching

C) Process data sequentially without caching

D) Compress all data during training

Show Answer & Explanation

Correct Answer: B

Explanation: Asynchronous data loading with prefetching allows the data loader to prepare the next batch while the GPU processes the current batch, maximizing GPU utilization. This optimization technique is fundamental to the performance concepts covered in our Performance Optimization and Monitoring practice questions.

Question 3: Backup and Recovery Strategies

For mission-critical AI model training checkpoints and datasets, which backup strategy provides the best protection against data loss while minimizing storage costs?

A) Daily full backups only

B) 3-2-1 backup strategy with incremental backups

C) Local RAID arrays only

D) Cloud storage without local copies

Show Answer & Explanation

Correct Answer: B

Explanation: The 3-2-1 strategy (3 copies, 2 different media types, 1 offsite) with incremental backups provides comprehensive protection while minimizing storage costs through efficient space utilization. This disaster recovery approach is essential for the security practices covered in our Security and Compliance practice questions.

Question 4: Storage Tiering

In a tiered storage architecture for AI workloads, how should data be distributed across different storage tiers to optimize both performance and cost?

A) All data on fastest tier

B) Hot data on SSD, warm data on HDD, cold data on object storage

C) Random distribution across tiers

D) All data on cheapest tier

Show Answer & Explanation

Correct Answer: B

Explanation: Tiered storage places frequently accessed (hot) data on fast SSDs, moderately accessed (warm) data on HDDs, and rarely accessed (cold) data on cost-effective object storage. This strategy optimizes both performance and cost, which relates to the deployment considerations in our Deployment and Operations practice questions.

Question 5: Data Compression and Encoding

When storing large datasets for AI training, which data format and compression strategy typically provides the best balance of storage efficiency and read performance?

A) Uncompressed CSV files

B) Parquet with Snappy compression

C) Maximum compression regardless of format

D) Plain text with no optimization

Show Answer & Explanation

Correct Answer: B

Explanation: Parquet with Snappy compression provides excellent storage efficiency through columnar format and fast compression/decompression, while enabling selective column reading for analytics workloads. This optimization strategy connects to the troubleshooting techniques covered in our Troubleshooting and Maintenance practice questions.

Data Management Learning Progression

Build comprehensive data management expertise with these related domains:

• Foundation: Hardware and System Architecture Practice Questions (storage architecture prerequisite)

• Next: Performance Optimization and Monitoring Practice Questions (storage performance)

• Related: Security and Compliance Practice Questions (data security)

• Overview: Return to Complete Study Guide

Master Data Management for AI Infrastructure

Access unlimited practice questions covering storage systems, data pipelines, and optimization strategies.