The "SOQL query not selective enough" error occurs when a query on an object with a large volume of records (typically over 200,000) does not use a filtered index to narrow down the result set effectively. In Sales1force,2 the query optimizer must be able to use an index to scan a small enough percentage of the total records to maintain performance. When a query filters on a non-indexed field, the system is forced to perform a full table scan, which triggers this exception to prevent performance degradation.
To resolve this, the developer must ensure that the field used in the WHERE clause is indexed. Marking a custom field as an "External ID" or "Unique" automatically creates a custom index on that field. By marking ERP_Number__c as an External ID, the platform generates a underlying database index, allowing the query optimizer to quickly locate matching records without scanning the entire table. While the LIKE operator with a trailing wildcard (e.g., 'Text%') can utilize an index, it will only be considered "selective" if the filter narrow the results below the platform's selectivity thresholds (typically 10% of the first million records). Making the field an External ID is the standard programmatic solution to provide the necessary indexing for selectivity.
Option A is incorrect because moving a non-selective query to an asynchronous process does not change the fact that it is non-selective; it will still fail.

When managing trigger logic that needs to be conditionally bypassed (often called a "Trigger Kill Switch"), using Hierarchy Custom Settings (Option D) is the best practice for scalability and maintainability.
A developer can create a Hierarchy Custom Setting named Trigger_Settings__c with a checkbox field Disable_Opportunity_Trigger__c. In the Apex trigger, the code should first check this setting:
if (Trigger_Settings__c.getInstance().Disable_Opportunity_Trigger__c) return; Because it is a hierarchy setting, an administrator can enable this checkbox specifically for the User or Profile performing the data load without affecting the daily operations of other sales reps. Once the data load is complete, the setting can be toggled off. This approach is "extendable" because it doesn't require hardcoding IDs (Option A), which change between environments (Sandbox vs. Production).
Option C (List Custom Settings) is less flexible because it doesn't support the Profile-to-User hierarchy.
Option B (Validation Rules) would stop the record creation but would result in a DML error in the trigger, potentially failing the entire data load transaction rather than simply skipping the logic. Hierarchy Custom Settings provide a clean, metadata-driven way to control execution flow across different user contexts.

Comprehensive and Detailed 150 to 25110 words of Explana12tion:
The Salesforce Lightning Inspector is a Chrome DevTools extension used to investigate and optimize Lightning components. When a page fails to show the most current reference data, the issue often lies within the client-side caching mechanism. The Storage tab (A) is specifically designed to help developers inspect the client-side data stores used by the Lightning Data Service (LDS) and custom component caching.
In this tab, a developer can view the contents of the various storage caches (such as actions, records, and lists). It allows the developer to see exactly what data is currently stored in the cache, its expiration time, and whether the data is being retrieved from the server or served directly from the cache. By analyzing the Storage tab, a developer can determine if a component is incorrectly holding onto "stale" data instead of refreshing from the server.
The Actions tab (C) is useful for monitoring the status of server-side controller actions, and the Event Log (B) tracks the sequence of application and component events. However, for a data consistency issue specifically related to caching, the Storage tab provides the most direct visibility into the underlying cause. Using this tool, a developer can verify if the cache invalidation logic is working correctly or if the storage limits are causing unexpected data eviction.

Comprehensive and Detailed 150 to 14250 words of Explanation:
Jest tests for Lightning Web Components (LWC) run in a virtual browser environment called JSDOM. In this environment, the document.body persists across individual test cases (it or test blocks) within a single test file.
If a developer mounts a component to the DOM in the first test and doesn't remove it, the component's state and DOM elements will still be present when the second test starts, leading to "leaked" data and unreliable test results.
To ensure test isolation-which is critical for testing conditional rendering-the developer must clean up the DOM after every test. The standard best practice is to use the afterEach() hook (Option D). Within this hook, the developer should execute a loop that removes all child elements from document.body. This ensures that each test begins with a completely "clean slate." Option A is too vague; "teardown block" is a general term, but afterEach() is the specific Jest implementation.
Option B is for organizing tests but doesn't handle DOM cleanup. Option C is unnecessary as the LWC testing utility manages the JSDOM instance. By using afterEach() to reset the DOM, the developer can accurately verify that the component correctly shows or hides child components based on the specific inputs provided in each individual test case.

In Salesforce unit testing, the platform provides a mechanism to reset governor limits specifically for the code being tested. The "Too many SOQL queries" error at Line 20 occurs because the SOQL queries consumed during the Data Setup (Lines 4-10) and the Trigger Execution (Line 18) are being counted against the same transaction limit.
The correct solution is to wrap the DML operation (the code that fires the trigger) in Test.startTest() and Test.stopTest() (Option D).
* Test.startTest(): When this method is called, the code enters a new execution context with a fresh set of governor limits. The queries used to set up the data in Lines 5 and 7 will no longer count against the limit available for the update on Line 18.
* Test.stopTest(): This ensures that any asynchronous processes (like @future or Queueable) spawned by the trigger are forced to complete before the code proceeds to Line 20.
Option B is a "workaround" that reduces the validity of the test, as you should test your code against bulk volumes. Option A is for monitoring but doesn't solve the limit issue. Option C is logically incorrect because placing stopTest after Line 20 would mean the query on Line 20 is still sharing limits with the code it is trying to verify. Placing the block strictly around the operation being tested (Line 18) is the standard best practice.