PDII Exam Dumps - Salesforce Developers Questions and Answers

When code behavior differs between two environments that theoretically share the same codebase, the root cause is often the underlying platform version. Salesforce releases updates (e.g., Spring, Summer, Winter) on a staggered schedule. Sandbox instances are frequently updated to the "Preview" version of the upcoming release weeks before the Production environment.

If a sandbox is on a newer release (Option C), it may include changes to the Apex compiler, new platform governor limits, or modifications to how standard objects behave. If a test relies on a specific behavior that changed in the new release, it might fail in the sandbox while still passing in the older Production environment.

Option A (SeeAllData=true) is a poor practice and would likely cause more issues rather than resolve them. Option B is unlikely because API versions are fixed in the metadata of the class and wouldn't change spontaneously. Option D relates to execution speed and resource contention but rarely causes a functional test to pass in one environment and fail in another unless there are significant race conditions. Checking the release version is the standard first step when diagnosing environment-specific discrepancies in the absence of code changes.

To display new data in a lightning-datatable, changes are required at both the Data Retrieval (Apex) layer and the UI Definition (JavaScript) layer.

First, the SOQL query in the Apex controller must be updated to include the address fields. While MailingAddress is a compound field in Salesforce, in a SOQL query intended for a datatable, it is often best to retrieve the individual components (e.g., MailingStreet, MailingCity, MailingState).

Second, the JavaScript controller for the LWC must be updated. The columns array in the JS file defines which data properties the lightning-datatable looks for. Even if the Apex method returns the address data, the datatable will not render it unless there is a corresponding column definition with the correct fieldName property.

Option D correctly identifies this two-step process. Option B is incomplete because the UI wouldn't know to show the new data. Option C is incorrect because you don't "extend" the component to add columns; you simply pass a configuration array. Option A is inefficient as it creates extra server round-trips for data that should be fetched in a single query. By aligning the SOQL fields with the datatable column definitions, the mailing address can be displayed seamlessly.

A frequent mistake in Apex unit testing is failing to recognize that SObject variables in memory do not automatically reflect changes made to the database by triggers, flows, or other logic. When the developer saves the Account record in the test4, the database is updated (e.g., a trigger or the controller changes the OwnerId to the Queue ID). However, the variable used in the test still holds the original data that was present when it was first instantiated or inserted.

To successfully assert the change, the developer must perform a "refresh" query. This involves using a SOQL statement to retrieve the record from the database using its ID: Account updatedAcc = [SELECT OwnerId FROM Account WHERE Id = :acc.Id];. The assertion should then be performed against updatedAcc.OwnerId.

Option B correctly identifies this as the root cause. Option A is irrelevant as Queueable is an asynchronous execution pattern. Option C (seeAllData=true) is a poor practice and is not required for testing logic that modifies records. Option D is unrelated to assertion logic. Standard test isolation requires that you explicitly re-query the database to verify side effects caused by platform automation or controller logic.

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In a Lightning Web Component's configuration file (.js-meta.xml), the tag allows the developer to specify which device form factors the component supports. To restrict the component to only render on desktop devices, the developer must specify the type="Large".

The structure is:

XML

If supportedFormFactors is not specified, the component defaults to supporting both Small (phone) and Large (desktop) form factors. Options B, C, and D are incorrect syntax for defining form factor support in LWC metadata.

When a Visualforce page is slow because it attempts to load a massive dataset all at once, the bottleneck is often the time taken by the controller constructor to query and process those records before the page can even begin to render. Lazy Loading (Option B) is a strategy where the developer avoids loading all data during the initial page initialization.

Instead of querying 10,000 records in the constructor, the developer can load only the structural elements of the page first. Then, using an action method (triggered by or a window.onload script), the data is fetched in the background. This allows the user to see the page layout immediately while the data "populates" shortly after. This significantly improves the Time to First Byte (TTFB) and the perceived performance of the page.

Option A (transient) is excellent for reducing the View State size and speeding up postbacks (button clicks), but it does not inherently speed up the initial load time of a large dataset. Option C (JavaScript processing) can help but doesn't solve the data retrieval bottleneck. Option D (actionPoller) is used for periodic updates and is not an efficient way to load an initial large dataset. Lazy loading ensures that the "heavy lifting" is decoupled from the initial page load, providing a more responsive experience.

In Salesforce Apex, a Database.Savepoint allows a developer to define a point in a transaction that can be returned to if an error occurs, effectively undoing database changes. However, it is critical to understand what is—and is not—affected by a rollback. According to the platform specifications, while database records (DML operations) are reverted to their state at the time the savepoint was set, static variables are not reverted (Option B). Static variables persist across the entire transaction regardless of rollbacks, which is why they are commonly used as "recursion guards" to track if a trigger has already fired.

Other options are incorrect due to the strict "stack" nature of savepoints. Savepoints must be rolled back in reverse chronological order; rolling back to an earlier savepoint invalidates any savepoints created after it (Option A). Furthermore, a savepoint variable is only valid within the specific execution context in which it was created. You cannot pass a savepoint reference across different trigger invocations or asynchronous boundaries (Option D). Finally, while the rollback itself isn't a DML statement, the operations performed between setting a savepoint and rolling back still count toward DML and CPU governor limits (Option C). Understanding that memory state (static variables) remains unchanged during a database rollback is essential for debugging complex transactional logic.

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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.

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Salesforce enforces a strict architectural boundary regarding database transactions and external communications. When an Apex trigger executes, it is part of a synchronous database transaction. If the platform allowed a synchronous callout (a web service call) directly from a trigger, the database transaction—including all active record locks—would have to remain open while waiting for the external system to respond. This could lead to severe performance degradation and hit the "Concurrent Request" limit.

Consequently, calling a web service from an Apex trigger (Option C) must be handled asynchronously. A developer must hand off the callout logic to an @future(callout=true) method, a Queueable class, or a Platform Event. This allows the trigger to finish its work and commit the database transaction immediately, while the callout runs in its own separate background process with its own set of governor limits.

Option A is incorrect because a standard synchronous query can retrieve up to 50,000 records. Option B is incorrect because System.scheduleBatch is a synchronous call to initiate a process, even if the process itself runs later. Option D is incorrect because record updates can be handled synchronously in an after insert trigger context.

Salesforce has a strict rule: You cannot perform a synchronous web service callout after performing a DML operation in the same transaction. In the provided code, the insert operations (DML) occur first, creating "uncommitted work" in the database. When the code then tries to execute makeRestCallout, the platform throws the error to prevent a long-running callout from holding database locks open.

To resolve this, the callout must be moved to its own separate transaction. The most straightforward way to do this is to change the callout method to an @future(callout=true) method (Option B). When an @future method is called, the request is queued and executed in a new, independent transaction with its own governor limits. This decouples the database "work" from the external "callout."

Option A is incorrect because simply moving the line doesn't change the transaction context. Option C (removing savepoints) doesn't help because the DML itself is the blocker. Option D (@InvocableMethod) is used for Flow/Process Builder and doesn't inherently solve the transaction boundary issue. Using an @future method (or a Queueable class) is the standard platform-aligned solution for this error.