A telecommunications provider is designing an analytical data platform to process and quer… — Microsoft Azure Solutions Architect (AZ-305)

A telecommunications provider is designing an analytical data platform to process and query call detail record (CDR) log files. The platform must meet the following requirements:
- Ingest $50\text{ GB}$ of CDR files daily at a peak velocity of $200$ files per minute.
- Store the raw logs cost-effectively in Azure Data Lake Storage Gen2 (ADLS Gen2).
- Enable analysts to run ad-hoc, exploratory SQL queries directly on the raw files with a query latency of under $15\text{ seconds}$ .
- Orchestrate a weekly batch transformation job that aggregates the CDR data, which typically takes $25\text{ minutes}$ to run.
Which of the following Azure components should you include in the design to meet these requirements? (Select TWO.)

Azure Synapse serverless SQL pools to query the raw log files in Azure Data Lake Storage Gen2Answer
B
Azure Synapse dedicated SQL pools to query the raw log files in Azure Data Lake Storage Gen2
Azure Data Factory pipelines to orchestrate the weekly batch transformationAnswer
D
Azure Functions on a Consumption plan to run the weekly batch transformation

Answer

Azure Synapse serverless SQL pools to query the raw log files in Azure Data Lake Storage Gen2, and Azure Data Factory pipelines to orchestrate the weekly batch transformation

Azure Synapse serverless SQL pools are correct for ad-hoc exploratory queries because they allow querying unstructured or semi-structured data directly in Azure Data Lake Storage Gen2 using standard T-SQL without provisioning persistent resources. Azure Data Factory pipelines are correct for the weekly batch orchestration because they are built to manage complex data integration workflows with no runtime limits.

Step-by-Step Solution

Analyze the requirement for ad-hoc, exploratory SQL queries on raw data stored in Azure Data Lake Storage Gen2 (ADLS Gen2).

Identify that Azure Synapse serverless SQL pools are designed specifically for querying files (such as CSV, JSON, Parquet) in place without needing to provision dedicated clusters, fitting the

15

-second latency and cost-effectiveness requirements.

Dedicated SQL pools require importing data into relational tables and provisioning continuous resources, which is inefficient for ad-hoc file exploration.

Analyze the requirement for orchestrating a weekly batch transformation job that takes

25\text{ minutes}

to run.

Select Azure Data Factory (or Synapse Pipelines) as the orchestration engine, which supports long-running batch workflows without execution time limitations.

Azure Functions on a Consumption plan has a

10

-minute execution limit, which would cause the

25

-minute run to time out.

Key Concept

Selecting appropriate Azure analytical and integration services based on query patterns, operational overhead, and execution limits

Question

Answer

Step-by-Step Solution

Key Concept

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