PDII Exam Dumps - Salesforce Certified Platform Developer II ( Plat-Dev-301 )

Testing asynchronous Apex (such as @future methods, Batchable, or Queueable classes) requires a specific mechanism to ensure that background tasks complete before the test verifies the results. In a standard execution, asynchronous jobs are queued and run whenever system resources are available, which means a test's assertion statements might execute before the background job has even started. To address this, Salesforce provides the Test.startTest() and Test.stopTest() methods.78

When t9he code that initiates an asynchronous process is placed between Test.startTest() and Test.stopTest(), the platform behaves di10fferently. All asynchronous calls made within this block are collected and held by the system. As soon as the 11code reaches Test.stopTest(), the test execution pauses, and the system forces all queued asynchronous jobs to ru12n immediately and synchronously within the same thread. Once Test.stopTest() finishes, the execution continues to the next line. By placing assertions immediately after Test.stopTest(), the developer is guaranteed that the asynchronous logic has finished its work. This is the only supported way to reliably test the side effects of background processes. Other options, like using System.runAs() or seeAllData=true, do not affect the timing of asynchronous execution and will likely result in failed assertions.

When using lightning-record-edit-form, server-side validation rules (Option D) typically return errors one at a time as the database engine encounters them, or as a single combined toast message that can be difficult to parse. To provide a superi1or user experience where multiple fields are validated simultaneously before the data even reaches the server, the develo2per shou3ld implement Client-side validation (Option B).

In the component's JavaScript controller, the developer can intercept the onsubmit event. By iterating through all the lightning-input-field or standard lightning-input elements, the developer can programmatically check for various conditions (e.g., custom regex patterns, conditional logic between fields, or range checks). Each field can then be marked with a custom error message using the reportValidity() or setCustomValidity() methods. This allows the UI to highlight all invalid fields at once, providing immediate, comprehensive feedback to the user. This approach reduces unnecessary server round-trips and ensures that the user can correct all issues in a single pass before successfully submitting the record.

In Salesforce, a callout cannot be made directly from an Apex trigger (Option C). This is a fundamental architectural restriction of the platform. Triggers execute as part of a database transaction. If a trigger were allowed to make a synchronous callout, the database transaction—along with all its associated row locks—would remain open while waiting for a response from the external system. This could lead to severe performance bottlenecks and transaction timeouts.

To perform a callout based on a trigger event, the developer must move the callout logic into an asynchronous context, such as a @future(callout=true) method, a Queueable class, or by publishing a Platform Event.

Option A is incorrect because while asynchronous methods allow for higher CPU time, they do not extend the maximum individual callout timeout (120 seconds). Option B is incorrect because REST is simply a protocol and does not dictate synchronicity. Option D is incorrect because the limit for callouts in a single transaction is 100, so 10 callouts would still be permissible in a synchronous context (assuming they aren't in a trigger). Consequently, the presence of a trigger is the only factor listed that programmatically mandates an asynchronous approach.

The issue described involves hitting DML limits due to a high volume of records (over 10,000) being processed in a single transaction. In Salesforce, a single synchronous transaction or a standard Scheduled Apex job has strict governor limits on the number of DML statements (150) and the total number of records processed (10,000 for DML rows).

To resolve this, the developer should implement the Database.Batchable interface. Batch Apex is specifically designed to handle large data volumes by breaking the processing into smaller, manageable chunks (batches) of records (default 200). Each batch runs within its own transaction context, resetting the governor limits for that specific batch. This prevents the "Too many DML rows" exception. While Queueable (Option D) and @future (Option C) provide asynchronous processing, they are not inherently designed to iterate over large datasets in the same robust, governor-limit-safe manner as Batch Apex, particularly when the record count exceeds the thousands.

A significant challenge in Salesforce unit testing is that system-generated records, such as those in the AccountHistory or OpportunityHistory objects, are not created during the execution of a test method, even if field history tracking is enabled in the organization. Because these records are read-only and managed by the system, a developer cannot manually insert them via DML within a test setup. Consequently, the query in the getAccountHistory method will always return an empty list in a test context, causing the System.assert to fail.

The optimal programmatic solution to this limitation is to implement a "mocking" strategy using Test.isRunningTest(). By modifying the production code within getAccountHistory, the developer can check if the code is currently being executed by a unit test. If it is, the method can return a manually constructed list of AccountHistory records (stored in memory but not inserted into the database) to satisfy the test's requirements. This allows the test to verify the logic that processes the history data without needing the system to actually generate historical records. While SeeAllData=true (Option A) would allow the test to see real data in the org, it is considered a poor practice because it makes the test dependent on the specific state of the organization's data, leading to brittle tests that may fail in different environments.12

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