A company is designing a data analytics solution on Azure to support a team of data scientists who perform infrequent, ad-hoc exploratory queries on a dataset stored in Azure Data Lake Storage Gen2. New data is loaded in batches every , adding approximately daily. The exploratory queries must execute within . The entire architecture must remain resilient to a regional disaster, ensuring that both data and analytical query capabilities can be restored or accessed in a secondary region. Additionally, all access to the data lake must be secure, easily revocable, and audit-compliant, avoiding long-lived static credentials. You need to design the solution while minimizing operational overhead and idle resource costs. Which storage and analytical query configuration should you recommend?
- AA dedicated SQL pool in Azure Synapse Analytics querying Azure Data Lake Storage Gen2 configured with Geo-Redundant Storage (GRS), using Microsoft Entra ID integration for authentication.
- BAzure Synapse Analytics serverless SQL pools querying Azure Data Lake Storage Gen2 configured with Locally Redundant Storage (LRS), using Microsoft Entra ID integration for authentication.
- Azure Synapse Analytics serverless SQL pools querying Azure Data Lake Storage Gen2 configured with Geo-Redundant Storage (GRS), using Microsoft Entra ID integration for authentication.Cevap
- DAzure Synapse Analytics serverless SQL pools querying Azure Data Lake Storage Gen2 configured with Geo-Redundant Storage (GRS), using ad-hoc Shared Access Signature (SAS) tokens with a five-year expiration date embedded in the connection configuration.