Exam AZ-305 topic 2 question 22 discussion

The second question is confusing, and I am not sure what the answer is - Can query delta lake with Serverless SQL pool but won't be able to update it. - Only Apache Spark pools support updates to Delta Lakes files. It can also be used to query long-time series as well if I understand the doc correctly... I think the answer to 2 is Apache Spark tools on that basis...

The answer is correct. Azure Synapse Analytics (also named SQL Data Warehouse) is a cloud-based analytics service that allows you to analyze large amounts of data using a combination of on-demand and provisioned resources. It offers several different options for working with data, including: - Dedicated SQL pool: It's best for big and complex tasks. - Serverless Apache Spark pool: It's best for big data analysis and machine learning tasks using Spark SQL and Spark DataFrames. - Serverless SQL pool: This is a service that automatically adjusts the amount of resources you use based on your needs. You only pay for what you use. It's best for small to medium-sized tasks and tasks that change often.

Got this on may. 02, 2025 , Score 925, I choose 1-Dedicated SQL pool 2- Serverless Apache Spark pool

1. Ingest data from Data Lake Storage into hash-distributed tables: • A dedicated SQL pool Explanation: Hash-distributed tables are a feature of dedicated SQL pools in Azure Synapse. They allow for efficient data distribution and parallel processing, which is ideal for large-scale data ingestion from Data Lake Storage. 2. Implement, query, and update data in Delta Lake: • A serverless Apache Spark pool Explanation: Serverless Apache Spark pools in Azure Synapse support Delta Lake, providing full read and write capabilities. They allow you to implement, query, and update Delta Lake tables effectively. Answer Area: 1. A dedicated SQL pool 2. A serverless Apache Spark pool

dedicated SQL pool serverless Apache Spark pool

This question appeared in the exam, August 2024, I gave this same answer for box 1 but answered Apache Spark Pool for box 2. I scored 870

From Copilot: To meet the requirements for ingesting data from Data Lake Storage into hash-distributed tables, you should recommend A Dedicated SQL pool. This option is designed for large-scale, high-performance, and secure analytics on Azure. For implementing, querying, and updating data in Delta Lake, you should recommend A serverless Apache Spark pool. This option allows you to run big data analytics and artificial intelligence workloads with Apache Spark, which is compatible with Delta Lake. These recommendations align with Azure's best practices for performance and scalability when working with Synapse and Data Lake Storage Gen2. If you need further details or assistance with the setup, feel free to ask.

Ingest data from Data Lake Storage into hash-distributed tables: A dedicated SQL pool Implement, query, and update data in Delta Lake: A serverless Apache Spark poo

Box 1: is correct => A hash-distributed table distributes table rows across the Compute nodes by using a deterministic hash function to assign each row to one distribution. Since identical values always hash to the same distribution, SQL Analytics has built-in knowledge of the row locations. In dedicated SQL pool this knowledge is used to minimize data movement during queries, which improves query performance. https://learn.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute Box 2: should be Apache Spark pools in Azure Synapse enable data engineers to modify Delta Lake files https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/query-delta-lake-format

Ingest data from Data Lake Storage into hash-distributed tables: For this requirement, I recommend using a Dedicated SQL pool in Azure Synapse. This service is designed for large-scale data processing and supports creating hash-distributed tables to optimize query performance. Implement, query, and update data in Delta Lake: For this requirement, I recommend using a Serverless Apache Spark pool in Azure Synapse. This service provides capabilities for working with Delta Lake as it offers an analytics service that can handle big data processing tasks without the need to provision or manage clusters.

Dedicated SQL Pools Purpose: Dedicated SQL pools provide massive parallel processing (MPP) capabilities ideal for handling large volumes of data. They are optimized for complex queries over large datasets and are suitable for building enterprise-level, big data analytics solutions. Spark Pools Purpose: Spark pools in Azure Synapse provide a fully managed Apache Spark environment. They are designed to handle big data processing, analytics, and machine learning tasks. Spark pools can process data in various formats and from multiple sources, including Azure Data Lake Storage.

from ChatGPT : For implementing, querying, and updating data in Delta Lake, the most suitable option among the ones you listed would be A serverless Apache Spark pool. Here's why: Integration with Delta Lake: Apache Spark is tightly integrated with Delta Lake, offering native support for reading from and writing to Delta tables. This integration ensures seamless compatibility and efficient data processing capabilities

OPTION 2: SERVERLESS APACHE SPARK POOL

Got this on Sept. 29, 2023

I had question at 24th Sep 2023

From MSFT docs: Serverless SQL pools don't support updating Delta Lake files. You can use serverless SQL pool to query the latest version of Delta Lake. Use Apache Spark pools in Synapse Analytics to update Delta Lake.

To meet the requirements of ingesting data from Data Lake Storage into hash-distributed tables and implementing query and update operations in Delta Lake, the recommended Azure Synapse pool options are as follows: Ingest Data from Data Lake Storage into Hash-Distributable Tables: A dedicated SQL pool: This option allows you to leverage the power of the dedicated SQL pool (formerly SQL Data Warehouse) in Azure Synapse to perform high-performance ingest operations into hash-distributed tables. The dedicated SQL pool is optimized for large-scale data warehousing scenarios. Implement Query and Update Data in Delta Lake: A serverless Apache Spark pool: This option allows you to use Apache Spark as a serverless processing engine within Azure Synapse. Spark provides robust support for querying and updating data in Delta Lake, which is an open-source storage layer for reliable big data processing.