PK Chunking can resolve query timeout when running a bulk query on an object with more than 10 million records. PK Chunking is a feature of the Bulk API that splits a query into multiple batches based on the record IDs (primary keys) of the queried object. This can improve the query performance and avoid timeouts by reducing the number of records processed in each batch.

Creating an aggregate custom object that summarizes the monthly and yearly values into the required format for the required reports (option D) is the fastest and most effective way for a data architect to solve the time-out issue, as it reduces the amount of data that needs to be queried and processed by the reports. Creating custom fields on opportunity and copying data from order into those custom fields (option A) is not a good solution, as it may create data redundancy and inconsistency, and it does not address the large volume of data. Extracting opportunity and order data from Salesforce and using a third-party reporting tool (option B) is also not a good solution, as it may introduce additional complexity and cost, and it does not leverage the native reporting features of Salesforce. Creating a skinny table in Salesforce and copying order and opportunity fields into it (option C) is also not a good solution, as it may not support all the reporting requirements, and it does not reduce the number of records.

According to the Salesforce documentation 1 , the query plan tool is a tool that can be enabled and used in the developer console to analyze the performance of a SOQL query. The query plan tool shows the cost, cardinality, sObject type, and relative cost of each query plan that Salesforce considers for a query. The relative cost indicates how expensive a query plan is compared to the Force.com query optimizer threshold. A query plan with a relative cost above 1.0 is likely to cause a time-out error.
To identify why the query is taking too long, a data architect should use the query plan tool in the developer console (option B). This way, the data architect can see which query plan is chosen by Salesforce and how it affects the performance of the query. The data architect can also use the query plan tool to optimize the query by adding indexes, filters, or limits to reduce the cost and improve the efficiency of the query.
Using Splunk to query the system logs looking for transaction time and CPU usage (option A) is not a good solution, as it can be complex, costly, and difficult to integrate with Salesforce. It may also not provide enough information or insights to identify and optimize the query performance. Using Salesforce's query optimizer to analyze the query in the developer console (option C) is also not a good solution, as it is not a separate tool that can be used in the developer console. The query optimizer is a feature that runs automatically when a SOQL query is executed and chooses the best query plan based on various factors 2 .
Opening a ticket with Salesforce support to retrieve transaction logs to be analyzed for processing time (option D) is also not a good solution, as it can be time-consuming, dependent, and inefficient. It may also not provide enough information or insights to identify and optimize the query performance.

Ask support to create a skinny table for Application with the necessary reporting fields. This is an option that can improve report performance by creating a custom table that contains frequently used fields and copies their values from standard or custom objects. Skinny tables are kept in sync with their source tables and are used in place of them for reports and queries4. Add custom indexes to the Date fields used for filtering the report. This is another option that can improve report performance by creating custom indexes on fields that are often used as filters or join conditions in reports and queries. Custom indexes can speed up data retrieval and reduce query execution time