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Difficulty: HardData Integration and Analytical Storage

A logistics company is designing an analytical data solution on Azure to monitor fleet telemetry and optimize delivery routes. The solution must satisfy the following technical requirements:

* Ingestion: Telemetry streams from 30,00030,000 active delivery vehicles, peaking at 4,0004,000 events per second.
* Storage: Telemetry data is saved in Parquet format on Azure Data Lake Storage Gen2 (ADLS Gen2). Total storage volume is expected to reach 90 TB90\text{ TB}.
* Resilience: The stored telemetry data must remain available for read access in a secondary region if a primary datacenter outage occurs.
* Analytics: Data analysts will run ad-hoc SQL queries on the historical Parquet files to identify route inefficiencies. Queries are run infrequently and unpredictably during business hours, averaging 1010 to 1212 queries per day.
* Security: External logistics partners must be granted read-only access to specific folders in ADLS Gen2 for 9090 days. This access must be immediately revocable if the partnership ends early.
* Cost: The solution must minimize idle compute charges and administrative overhead.

Which combination of storage replication, analytical query service, and access security should you recommend?

  1. Azure Data Lake Storage Gen2 with Read-Access Geo-Redundant Storage (RA-GRS), Azure Synapse Analytics serverless SQL pool, and a Shared Access Signature (SAS) associated with a Stored Access Policy.Answer
  2. B
    Azure Data Lake Storage Gen2 with Locally Redundant Storage (LRS), Azure Synapse Analytics serverless SQL pool, and a Shared Access Signature (SAS) associated with a Stored Access Policy.
  3. C
    Azure Data Lake Storage Gen2 with Read-Access Geo-Redundant Storage (RA-GRS), Azure Synapse Analytics dedicated SQL pool, and a Shared Access Signature (SAS) associated with a Stored Access Policy.
  4. D
    Azure Data Lake Storage Gen2 with Read-Access Geo-Redundant Storage (RA-GRS), Azure Synapse Analytics serverless SQL pool, and an ad-hoc Shared Access Signature (SAS) with a 90-day validity period set directly in the token URI.

Answer

Azure Data Lake Storage Gen2 with Read-Access Geo-Redundant Storage (RA-GRS), Azure Synapse Analytics serverless SQL pool, and a Shared Access Signature (SAS) associated with a Stored Access Policy.
The correct architecture combines Read-Access Geo-Redundant Storage (RA-GRS) to satisfy the regional resilience requirement, Azure Synapse Analytics serverless SQL pool to query the Parquet files directly with zero idle compute costs, and a Shared Access Signature (SAS) backed by a Stored Access Policy to allow immediate revocation of external partner access.

Step-by-Step Solution

1
Analyze the storage and resilience requirements.
The requirement states that telemetry data must remain available in a secondary region during a primary datacenter outage, ruling out Locally Redundant Storage (LRS). Read-Access Geo-Redundant Storage (RA-GRS) or Geo-Zone-Redundant Storage (GZRS) is required.
LRS keeps three copies of data within a single physical location, offering no protection against a regional datacenter disaster.
2
Evaluate the analytical query model.
Since queries are ad-hoc, run only 1010 to 1212 times daily, and query Parquet files directly, Azure Synapse Analytics serverless SQL pools should be used instead of dedicated SQL pools.
A serverless SQL pool uses a pay-per-query model (billing based on data processed) and has zero idle compute costs, whereas dedicated SQL pools charge for continuous provisioned compute resource and require importing data.
3
Determine the access security strategy.
A Shared Access Signature (SAS) tied to a Stored Access Policy is selected to grant external partners read-only access for 9090 days.
An ad-hoc SAS token cannot be revoked individually before expiration without rotating the master storage account keys. Associating the SAS with a Stored Access Policy enables immediate revocation by modifying or deleting the policy.

Key Concept

Integrating cost-effective analytical querying of raw files in a data lake with geo-redundant resilience and revocable access security.
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Topics

Data Integration and Analytical Storage

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