Microsoft Certified: Azure Fundamentals (AZ-900) Practice Exam 2026 - Free AZ-900 Practice Questions and Study Guide

The main use case for Azure Data Lake Storage is indeed centered around large-scale analytics of big data. Azure Data Lake Storage is specifically designed to handle vast amounts of unstructured and structured data, making it an optimal choice for big data analytics and processing. By leveraging its capabilities, organizations can ingest, store, and analyze large datasets efficiently, facilitating advanced analytics and data processing scenarios that traditional storage solutions might struggle with.

Its architecture supports high-throughput and low-latency capabilities, allowing for the storage of raw data in its native format. Data scientists and analysts can then access this data to perform complex queries, machine learning, and data exploration, enabling insights that drive decision-making.

While other options like organizing data with SQL databases, high-speed transactional processing, and hosting web applications are important functions of various Azure services, they do not align with the primary purpose of Azure Data Lake Storage, which is tailored for scalable data storage and analytics.