A financial services company is designing a new analytical data platform on Azure to query historical audit logs. The audit logs are stored in Parquet format in an Azure Data Lake Storage Gen2 account.
The platform must meet the following requirements:
- Volume: of data is currently stored, growing by per month.
- Velocity: Batch uploads occur once every week.
- Query Latency and Pattern: Data analysts run highly complex, ad-hoc exploratory queries to investigate anomalies. These queries occur irregularly, and the system may remain idle for several days.
- Resilience: The data must remain available even during a complete regional datacenter outage.
- Security: Access to the data lake must be delegated to external audit teams via Shared Access Signatures (SAS) that can be immediately revoked if security policies are violated.
You need to recommend the storage configuration and the query engine for the platform.
Which two components should you include in the recommendation? (Select TWO.)
- An Azure Synapse Analytics serverless SQL pool to query the Parquet files directly in the data lake.Cevap
- A storage account configured for Geo-zone-redundant storage (GZRS) with access granted via Shared Access Signatures (SAS) that reference a stored access policy.Cevap
- CAn Azure Synapse Analytics dedicated SQL pool to ingest the Parquet files into relational tables before running queries.
- DA storage account configured for Locally redundant storage (LRS) with access granted via Shared Access Signatures (SAS) that reference a stored access policy.
- EA storage account configured for Geo-zone-redundant storage (GZRS) with access granted via ad-hoc Shared Access Signatures (SAS) configured with a expiration date.