Data-Architect Exam Dumps - Salesforce Certified Data Architect (SP25)

Leveraging a “tier”-based approach to classify the record storage need is a true statement with respect to a data archiving strategy for UC. This approach involves defining different tiers of data based on their usage, value, and retention policies, and then applying appropriate storage and archiving solutions for each tier.

According to the official Salesforce guide1, using Bulk API serial mode for data migration can help avoid locking issues by processing batches in a single thread. This mode ensures that batches are processed in the order they are received and that only one batch is processed at a time. This reduces the risk of lock contention and deadlocks, especially when updating parent and child records in a lookup relationship. Option B is the correct answer because it suggests using Bulk API serial mode for data migration. Option A is incorrect because using Bulk API parallel mode for data migration can cause locking issues by processing batches in multiple threads. This mode does not guarantee the order of batch processing and can result in concurrent updates to the same records, which can lead to lock contention and deadlocks. Option C is incorrect because inserting the order in another custom object and using Batch Apex to move the records to Service_Order__c object adds unnecessary complexity and overhead to the data migration process. Option D is incorrect because changing the lookup configuration to “Clear the value of this field” when lookup record is deleted does not address the locking issues caused by data migration, but rather by record deletion.

Both C and D are valid methods to automatically archive and delete inactive Account data that is older than 3 years1. You can use an ETL tool or the Data Loader to schedule jobs to export and delete data based on certain criteria3. Option A is not recommended because the Force.com Workbench is a web-based tool that does not support scheduling or automation. Option B is not suitable because the weekly export file from the Salesforce UI does not delete data from Salesforce.

The data classification feature allows customers to classify their data in Salesforce based on two policies:

Data sensitivity level: This policy defines how sensitive the data is and what level of protection it requires. For example, high sensitivity data may require encryption or masking.

Compliance categorization policy: This policy defines how the data is regulated by various laws and standards. For example, GDPR or PCI DSS.

The correct answer is B, add custom indexes on frequently searched account and contact object fields. Custom indexes are a way to improve the performance of your queries and reports by creating indexes on specific fields that are often used in filters or joins. By adding custom indexes on frequently searched account and contact object fields, you can speed up the response time for reports and list views. Loading only the data that users are permitted to access, limiting data loading to the 2000 most recent records, or creating a skinny table are also possible solutions, but they are either not feasible, not scalable, or not supported by Salesforce.

Enabling granular locking (option A) is the best recommendation to mitigate these errors, as it allows finer control over how records are locked during automated or manual processes, and reduces the chances of lock contention or deadlock. Removing SOQL statements from Apex Loops (option B) is a good practice for improving performance and avoiding governor limits, but it does not directly address the lock errors issue. Enabling sharing recalculations (option C) is also not relevant for this issue, as it is used to update sharing rules and recalculate access for records. Asking Salesforce support for additional CPU power (option D) is also not a viable solution, as it does not solve the root cause of the lock errors.

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 object model to configure within SF for migrating individual customers (B2C) data from legacy systems is to leverage person account object in Salesforce. Person accounts are a special type of accounts that store information about individual people by combining certain account and contact fields into a single record4. Person accounts are useful for B2C scenarios where there is no need to associate a company name with a contact. Person accounts also support standard Salesforce features and functionality, such as leads, campaigns, reports, dashboards, etc5. Leverage custom person account object in SF is not a good option because there is no such thing as a custom person account object. Leverage custom account and contact object in SF is not a good option because it would require creating and maintaining additional objects and fields that may not be necessary or compatible with standard Salesforce features. Leverage standard account and contact object in SF is not a good option because it would require filling in dummy values for the account name field, which is mandatory for standard accounts.