Data-Architect Exam Dumps : Salesforce Certified Platform Data Architect (Plat-Arch-201)

The three questions that should be considered when designing an appropriate archiving strategy are: If reporting is necessary, can the information be aggregated into fewer, summary records? Will the data being archived need to be reported on or accessed in any way in the future? Are there any regulatory restrictions that will influence the archiving and purging plans? These questions are important because they help determine the scope, frequency, and method of archiving and purging data from Salesforce. For example, if reporting is necessary, then summary records or analytics snapshots can be used to store aggregated data and reduce the number of records that need to be archived1. If the data being archived needs to be accessed in the future, then offsite storage or external objects can be used to retain the data and make it available on demand2. If there are any regulatory restrictions, such as GDPR or HIPAA, then the archiving and purging plans need to comply with them and ensure data security and privacy

Exporting in multiple batches and using PK Chunking are two steps that can help correct the issues with exporting large volumes of Order records using Informatica Cloud Loader. Exporting in multiple batches can reduce the load on the system and avoid timeouts or errors. Using PK Chunking can split a large data set into smaller chunks based on the record IDs and enable parallel processing of each chunk.

The best solution to extract 50 million records from a custom object everyday from Salesforce org without facing query timeout issues is to use an ETL tool for extraction of records. ETL stands for extract, transform, and load, and it refers to a process of moving data from one system to another. An ETL tool is a software application that can connect to various data sources, perform data transformations, and load data into a target destination. ETL tools can handle large volumes of data efficiently and reliably, and they often provide features such as scheduling, monitoring, error handling, and logging5. Using a custom auto number and formula field and use that to chunk records while extracting data is a possible workaround, but it requires creating additional fields and writing complex queries. The REST API can extract data as it automatically chunks records by 200, but it has some limitations, such as a maximum of 50 million records per query job6. Asking SF support to increase the query timeout value is not feasible because query timeout values are not configurable