Free Practice Questions for Salesforce Data-Architect Exam

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

The correct answer is B, data export wizard. The data export wizard is a tool that allows you to export your data in CSV format, schedule the export process to run once per week, and configure the export settings through the Salesforce UI. The data export wizard can handle up to 51 million records per export. The bulk API, third-party ETL tools, and data loader are also tools that can export data, but they are not as simple or user-friendly as the data export wizard.

 Event Monitoring is a feature that allows you to track user actions, such as logins, logouts, downloads, etc., in your Salesforce org. You can use Event Monitoring to monitor performance, usage, security, and compliance

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.

Creating a unique global customer ID for each customer and storing that in all systems for referential identity (option A), investing in a data duplicate tool to de-dupe and merge duplicate records across all systems (option C), and creating a customer master database external to Salesforce as a system of truth and syncing the customer data with all systems (option E) are the three options that a data architect should perform to resolve the issues with the customer data. Option A ensures that each customer can be uniquely identified across different systems, option C eliminates duplicate records and improves data quality, and option E provides a consistent and reliable source of customer data for all systems. Using Salesforce CDC to sync customer data across all systems (option B) is not a good option, as it does not address the duplication or inconsistency issues. Duplicating customer records across the system and providing a two-way sync of data between the systems (option D) is also not a good option, as it may create more confusion and conflicts with customer data.

Integrating Salesforce with ERP and making ERP the system of truth (option B) and implementing a de-dupe solution and establishing account ownership in Salesforce (option D) are the two solutions that a data architect should recommend to resolve the complaints. Option B ensures that account data is consistent and accurate across both systems, while option D prevents duplicate records and clarifies who owns each account in Salesforce. Building a nightly batch job to de-dupe data and merge account records (option A) is not a good solution, as it does not address the root cause of the duplication and may result in data loss or conflicts. Building a nightly sync job from ERP to Salesforce (option C) is also not sufficient, as it does not prevent duplication or establish ownership in Salesforce.

The correct answer is A, enable defer sharing calculations, and suspend sharing rule calculations. Defer sharing calculations and suspend sharing rule calculations are features that allow you to temporarily disable the automatic recalculation of sharing rules when you load large volumes of data. This can improve the performance and speed of your data load process by avoiding unnecessary system calculations. Loading the data through data loader, leveraging the bulk API, or enabling granular locking are also options that can help with data load times, but they do not directly address the system calculations issue.