Exam DP-203 topic 1 question 40 discussion

Answer should be A, because this talks about minimizing storage costs, not querying costs

C is the correct answer, as an external table with a subset of columns with parquet files would be cost-effective.

Parquet is a columnar storage format that inherently provides compression benefits. Applying Snappy compression on top of Parquet's internal compression will further reduce the storage footprint of the files in Azure Data Lake Storage Gen2. Snappy is a well-suited compression codec for analytical workloads, offering a good balance between compression ratio and decompression speed, which is important for Azure Synapse Analytics serverless SQL pool to efficiently query the data. Options B, C, and D do not directly address minimizing storage costs for the Parquet files themselves. Option B is about query access, Option C is about query efficiency but not storage, and Option D is counterproductive to storage cost minimization.

A. Utilisez la compression Snappy pour les fichiers Cette approche permet de réduire la taille des fichiers Parquet, ce qui minimise les coûts de stockage dans Azure Data Lake Storage Gen2 tout en restant compatible avec Azure Synapse Analytics.

https://learn.microsoft.com/en-us/azure/data-factory/supported-file-formats-and-compression-codecs-legacy

Using Snappy compression (Option A) is specifically designed to reduce the size of Parquet files, thereby directly minimizing storage costs.

The question is confusing but I believe it is C, because you can use CETAS to store this external table in Gen2 (this is the storage solution), from there you will query it using serverless SQL pool.

A, B and C they are all acceptable, D is just stupid But pay attention to "You need to minimize storage costs for the solution" that means snappy parquet compresson - A is correct

Use Snappy compression for the files is the only answer, which is about minimizing cost of storage. While one is using serverless SQL pool, the external tables are available, which are the only metadata...

Using Snappy compression for the Parquet files helps minimize storage costs while still maintaining good compression efficiency. Snappy is a compression library that offers a good balance between compression ratio and processing speed. By compressing the data using Snappy, you can significantly reduce the amount of storage required for your dataset. Option B, using OPENROWSET to query the Parquet files, doesn't directly impact storage costs. It's a method for querying data but doesn't address storage optimization. Option C, creating an external table with a subset of columns, may help reduce query costs by minimizing the amount of data that needs to be processed during queries. However, it doesn't directly address storage costs. Option D, storing all data as strings in the Parquet files, would likely increase storage costs rather than minimize them. Storing data as strings without appropriate compression would result in larger file sizes compared to using efficient compression algorithms like Snappy.

A. Use Snappy compression for the files.

The answer is C - Parquet has default SNAPPY compression which cannot be overwritten so why would I apply SNAPPY again?

Further information required for this question. There isn't enough information to go off as to what is being asked. The initial question is in regards to storage which would result in using the snappy compression answer. If you are asking about querying the data then this should be clearly defined in the question. If someone was to create a User Story with regards to this (As a Manager I want to store data in the data lake at the reduced cost) then you wouldn't be providing them with an External table. You would give them information on storage.

Snappy compression can reduce the size of Parquet files by up to 70%. This can save you a significant amount of money on storage costs.

Snappy

using compression

To minimize storage costs for the solution, you should use Snappy compression for the files. Snappy is a fast and efficient data compression and decompression library that can be used to compress Parquet files. This will help reduce the size of the data files and minimize storage costs in Azure Data Lake Storage Gen2. So, the correct answer is A. Use Snappy compression for the files