An enterprise is designing a new data analytics platform in Azure to process and analyze telemetry data from global manufacturing plants. The platform must meet the following requirements:
- Ingest raw telemetry data at an ingestion rate of up to with sub-second ingestion latency, resulting in an expected daily volume of .
- Store raw data in an Azure Data Lake Storage Gen2 (ADLS Gen2) account.
- Enable data analysts to perform exploratory ad-hoc queries on raw files (CSV and Parquet) with a target query latency of under for small datasets, using a serverless model to minimize operational overhead and costs.
- Provide a persistent, centralized enterprise data warehouse for complex analytical queries on historical data (volume exceeding ), supporting clustered columnstore indexes and massive parallel processing (MPP) with sub-minute query latency.
- Ensure that both the storage and analytical layers survive a primary regional outage.
- Dynamically authorize access to raw files without exposing long-lived access keys or connection strings.
Which three solutions should you recommend to meet these requirements? (Select three.)
- Use Azure Synapse serverless SQL pools to perform ad-hoc exploratory queries on the raw Parquet and CSV files in ADLS Gen2.Answer
- Deploy Azure Synapse dedicated SQL pools to serve as the enterprise data warehouse, configuring geo-redundant backups for regional disaster recovery.Answer
- Implement Microsoft Entra ID authentication combined with Azure RBAC to secure access to the ADLS Gen2 storage account.Answer
- DProvision a dedicated SQL pool in Azure Synapse Analytics to perform all ad-hoc, exploratory queries on the raw CSV files stored in ADLS Gen2.
- EGenerate Shared Access Signature (SAS) tokens with a 12-month duration to provide analysts with direct access to the ADLS Gen2 storage container.
- FConfigure the ADLS Gen2 storage account to use Locally Redundant Storage (LRS) to minimize cost while meeting regional disaster resilience requirements.