Exam AZ-305 topic 2 question 36 discussion

Actual exam question from Microsoft's AZ-305

Question #: 36
Topic #: 2

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You are designing a data pipeline that will integrate large amounts of data from multiple on-premises Microsoft SQL Server databases into an analytics platform in Azure. The pipeline will include the following actions:

• Database updates will be exported periodically into a staging area in Azure Blob storage.
• Data from the blob storage will be cleansed and transformed by using a highly parallelized load process.
• The transformed data will be loaded to a data warehouse.
• Each batch of updates will be used to refresh an online analytical processing (OLAP) model in a managed serving layer.
• The managed serving layer will be used by thousands of end users.

You need to implement the data warehouse and serving layers.

What should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

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Suggested Answer:

by RJalal at Oct. 7, 2023, 12:43 p.m.

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BShelat

Highly Voted 1 year, 5 months ago

Trick to remember: Synapse Analytics - massive parallel processing Analysis Services - OLAP

upvoted 42 times

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ArunS005

Highly Voted 1 year, 8 months ago

The selected answer is correct. Data Warehouse: Azure Synapse Analytics (formerly SQL Data Warehouse) Azure Synapse Analytics is a massively parallel processing (MPP) data warehouse that can handle large amounts of data and provides a scalable solution for analytics. Managed Serving Layer: Azure Analysis Services Azure Analysis Services provides a fully managed platform-as-a-service (PaaS) solution for online analytical processing (OLAP) and data modeling. It is suitable for serving analytical models to thousands of end users.

upvoted 13 times

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Aikendrum

Most Recent 3 weeks, 2 days ago

1. Data Warehouse: Azure Synapse Analytics (Dedicated SQL Pool) ✅ Highly scalable for large data loads. ✅ Optimized for structured data and complex queries from multiple SQL Server sources. ✅ Supports parallelized ingestion for fast processing and transformations. ✅ Integrates with Azure Blob Storage and Azure Data Factory for smooth ETL workflows. 2. Serving Layer: Azure Analysis Services or Power BI Premium ✅ Supports OLAP models with efficient aggregation for thousands of users. ✅ Optimized for interactive analytics and complex multidimensional queries. ✅ Provides in-memory caching for real-time insights. ✅ Direct integration with Azure Synapse for performance optimization. Final Architecture Summary: Ingest data into Blob Storage ➝ Process and transform using Azure Data Factory ➝ Load into Synapse Dedicated SQL Pool ➝ Refresh OLAP model in Azure Analysis Services or Power BI Premium ➝ Expose data to end users efficiently

upvoted 1 times

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[Removed]

6 months, 3 weeks ago

CORRECT

upvoted 1 times

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SIJUTHOMASP

7 months, 1 week ago

Why not Apache Spark pool?

upvoted 3 times

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23169fd

11 months, 2 weeks ago

The given answer is correct. Azure Synapse Analytics dedicated SQL pool: Provides a scalable, high-performance data warehouse solution that can efficiently handle large amounts of data and supports complex queries. Azure Analysis Services: Offers powerful analytical processing (OLAP) capabilities, enabling fast and interactive data analysis for thousands of end users.

upvoted 1 times

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RJalal

1 year, 8 months ago

Here's how the pipeline would work: Periodically export database updates to Azure Blob storage. Use Azure Data Factory to cleanse and transform the data from Blob storage. Load the transformed data into your Azure Synapse Analytics data warehouse. Use Azure Analysis Services to create and manage OLAP models based on the data in your data warehouse. End users can connect to Azure Analysis Services to query and analyze the data.

upvoted 11 times

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RJalal

1 year, 8 months ago

correct answer

upvoted 1 times

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