Exam DP-900 topic 1 question 35 discussion

If you to think logically, Sentence one is yes - You can reduce transfer sensitive data with transformations on data source. Sentence two is No - ELT use the compute resource from destination system. And for last Yes. ELT minimizes time with transformations are going realizaded on target data destination systems.

Y/N/Y ETL: ...data transformation that takes place usually involves various operations, such as filtering... ELT: ...This approach skips the data copy step present in ETL, which can be a time consuming operation for large data sets... Load and Trasformation are on the same target (https://docs.microsoft.com/en-us/azure/architecture/data-guide/relational-data/etl)

Sentence one, Yes - ETL transforms the data before loading it into the destination system, which allows for filtering or masking sensitive data before transfer. Sentence two, No - In ELT, data is first loaded into the destination system and then transformations occur within the destination system itself, typically using its computing resources. Sentence three, Yes - ELT loads data first and then transforms it in the destination system, allowing for faster data ingestion, especially when dealing with large datasets.

Yes: ETL can help transform or mask sensitive data before it is loaded into the destination system, reducing the risk of transferring sensitive data in its raw form. However, this depends on the specific transformation steps applied. No: In ELT, data is first loaded into the destination (often a data warehouse or data lake), and the transformation typically happens using compute resources within the destination system (e.g., Azure SQL Data Warehouse, Azure Synapse, or Data Lake). So, the compute resource is typically within or closely tied to the destination system. Yes: ELT minimizes the time to copy large volumes of data because the transformation step occurs after the data is already loaded into the destination system, which means the loading phase doesn't need to wait for transformation logic.

Y Y Y 100% chat-o Here are the correct answers for the statements in the image: Extract, transform, and load (ETL) can reduce the transfer of sensitive data to destination systems. Answer: Yes Explanation: ETL processes can help by transforming and filtering sensitive data before loading it to the destination system, reducing the transfer of unneeded sensitive information. Extract, load, and transform (ELT) transforms data by using a compute resource independent of the source system and destination system. Answer: Yes Explanation: ELT transforms data after loading it into the destination system, often using compute resources within the destination system, which can be independent of the source system. Extract, load, and transform (ELT) minimizes the time it takes to copy large volumes of data to destination systems. Answer: Yes Explanation: ELT can speed up the data transfer process because it first loads the data into the destination system and then transforms it, minimizing the time for copying large data volumes.

I believe the correct answer is Yes, No, Yes.

yes, no, yes

Sì No Sì

ETL can redact and remove sensitive information before putting it into the data warehouse or cloud server. so i think no1 is yes

https://www.striim.com/blog/etl-vs-elt-differences/

https://aws.amazon.com/pt/what-is/etl/

The answer for 2 is NO. The reason being, it says source and destination, those are the keywords. In ELT, the source for transformation is inside DW itself and the destination is also inside DW. So, the compute is same. Answer is NO. The answer for 1 - Yes, as transformation allows fileting data before you put sensitive information in a data warehouse or any destination. This could be really beneficial in for example, banking sector. The answer for 3 - Yes, and the reason is in ETL processes, one of the bottleneck was staging server compute which took a lot of time. Whereas in ELT process, the transformation takes inside DW, which supports massive parallel computing and it removes that bottleneck of slow compute.

it should be NO,NO,Yes

Yes, No, Yes

Yes No Yes

no yes yes

Instead of using a separate transformation engine, the processing capabilities of the target data store are used to transform data. https://learn.microsoft.com/en-us/azure/architecture/data-guide/relational-data/etl the answer : NO / NO / YES