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Difficulty: HardData Integration and Analytical Storage

A healthcare provider is designing a new analytical data solution on Azure to integrate and process telemetry from medical sensors and daily diagnostic reports. The design must satisfy the following technical requirements:

* Diagnostic reports: 800 GB800\text{ GB} of compressed JSON files are uploaded as a batch once every 24 hours24\text{ hours}.
* Exploratory analysis: Data analysts must be able to run ad-hoc, exploratory queries directly against the raw JSON batch files without incurring costs for idle compute resources.
* Disaster recovery: The raw analytical data must be resilient against a regional datacenter failure.
* Access control: External research partners must be granted temporary, read-only access to the raw data files. The access must be immediately revocable at any time without rotating the primary storage account keys.

Which combination of storage redundancy, analytical querying engine, and authorization mechanism should you recommend to meet these requirements?

  1. Azure Data Lake Storage Gen2 (ADLS Gen2) configured with Geo-Redundant Storage (GRS); Azure Synapse Serverless SQL pool; Shared Access Signature (SAS) tokens associated with a stored access policy.Answer
  2. B
    Azure Data Lake Storage Gen2 (ADLS Gen2) configured with Locally Redundant Storage (LRS); Azure Synapse Serverless SQL pool; Shared Access Signature (SAS) tokens associated with a stored access policy.
  3. C
    Azure Data Lake Storage Gen2 (ADLS Gen2) configured with Geo-Redundant Storage (GRS); Azure Synapse Dedicated SQL pool; Shared Access Signature (SAS) tokens associated with a stored access policy.
  4. D
    Azure Data Lake Storage Gen2 (ADLS Gen2) configured with Geo-Redundant Storage (GRS); Azure Synapse Serverless SQL pool; Ad-hoc Shared Access Signature (SAS) tokens generated with an explicit 12-month expiration window.

Answer

Azure Data Lake Storage Gen2 (ADLS Gen2) configured with Geo-Redundant Storage (GRS); Azure Synapse Serverless SQL pool; Shared Access Signature (SAS) tokens associated with a stored access policy.
The correct answer configuration combines Geo-Redundant Storage (GRS) to survive a regional disaster, Azure Synapse Serverless SQL pool to query raw JSON files cost-effectively on an ad-hoc basis, and SAS tokens linked to a stored access policy to ensure access can be immediately revoked without affecting other clients.

Step-by-Step Solution

1
Determine the storage redundancy model based on resiliency requirements.
Locally Redundant Storage (LRS) is ruled out because it does not provide protection against regional datacenter failure. Geo-Redundant Storage (GRS) is selected to ensure data is replicated to a secondary region.
The system must survive a regional datacenter failure, which requires replicating data beyond a single physical region.
2
Evaluate the compute engine trade-offs for exploratory queries on JSON data.
Azure Synapse Serverless SQL pool is selected over Dedicated SQL pool.
Since the queries are ad-hoc and exploratory, serverless SQL pools avoid provisioning persistent resources, billing only for the data processed by the queries, which aligns with minimizing idle compute costs.
3
Select the correct security and revocation mechanism for external partners.
Shared Access Signature (SAS) tokens associated with a stored access policy are chosen over ad-hoc SAS tokens.
A stored access policy allows instantaneous revocation of the SAS tokens by modifying or deleting the policy directly on the container, without disrupting other users or requiring storage account key rotation.

Key Concept

Designing cost-effective, resilient, and secure Azure analytical storage solutions by matching data query patterns with the appropriate compute tier (Serverless SQL vs Dedicated SQL), redundancy options (GRS vs LRS), and secure delegation mechanisms (Stored Access Policies).
Estimated Time:2m 30s
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Topics

Data Integration and Analytical Storage

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