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Data Storage for AI Solutions Flashcards
Microsoft Azure AI Engineer Associate AI-102 Flashcards
| Front | Back |
| How can unstructured data be made usable for AI solutions | Unstructured data can be converted into structured formats using data preprocessing techniques such as tagging, categorization, or metadata enrichment. |
| How does Azure Blob Storage secure data | Azure Blob Storage secures data using encryption at rest and role-based access control (RBAC). |
| How does Azure Blob Storage support AI solutions? | Azure Blob Storage provides scalable storage for training datasets, especially unstructured data like images and videos. |
| How does managing data redundancy work in Azure Blob Storage | Azure Blob Storage offers redundancy options like LRS, GRS, and RA-GRS to ensure data durability and availability. |
| How is Azure Cosmos DB beneficial for AI solutions? | Cosmos DB enables real-time access to structured or semi-structured data for AI models requiring immediate decision-making capabilities. |
| What does structured data refer to? | Structured data refers to data organized in a defined manner, such as tables with rows and columns. |
| What is a benefit of integrating AI and cloud data storage solutions? | It enables efficient processing and scaling of massive datasets needed for AI. |
| What is a container in Azure Blob Storage | A container organizes blobs in Azure Blob Storage similar to a folder in a file system. |
| What is a key feature of Azure Cosmos DB? | A key feature is its multi-region replication and low-latency access. |
| What is Azure Blob Storage used for? | Blob Storage is used for storing unstructured data such as images, videos, and large datasets. |
| What is data sharding in Azure Cosmos DB | Data sharding is the process of distributing data across multiple servers for horizontal scaling. |
| What is HTAP in the context of Azure Cosmos DB | HTAP stands for Hybrid Transactional and Analytical Processing, allowing Cosmos DB to support real-time analytics on transactional data. |
| What is partitioning in Azure Cosmos DB? | Partitioning divides the database into smaller, more manageable subsets for scalability and performance. |
| What is the scalability advantage of Azure Blob Storage | Azure Blob Storage offers near-infinite scalability for managing large and growing amounts of data. |
| What is unstructured data? | Unstructured data is information that does not follow a predefined data model, like images, videos, or text files. |
| What role do tiers play in Azure Blob Storage | Tiers in Azure Blob Storage (hot, cool, archive) allow cost optimization based on access frequency. |
| What type of data is ideal for Azure Cosmos DB? | Azure Cosmos DB is ideal for storing highly scalable and globally distributed structured or semi-structured data. |
| Which API options does Azure Cosmos DB support | Azure Cosmos DB supports APIs such as SQL, MongoDB, Cassandra, Table, and Gremlin. |
| Why is data integration important for AI solutions? | Data integration allows combining structured and unstructured data for a comprehensive analysis and training of AI models. |
| Why is low-latency access important for AI models | Low-latency access ensures timely processing and decision-making required for real-time AI applications. |
This deck explains data storage options in Azure, including Azure Blob Storage, Cosmos DB, and integrating structured and unstructured data for AI solutions.