PDII Exam Dumps - Salesforce Developers Questions and Answers

The current bottleneck is caused by the need to resolve Salesforce Internal IDs ($001...$) for the Account lookup before orders can be imported. This can be completely bypassed by using External IDs (Option D) and the Upsert operation (Option C).

External IDs: By marking a unique field from the OMS (like OMS_Account_Number__c) as an External ID in Salesforce, the developer creates a "foreign key" that Salesforce understands.

Upsert Wizard: When using the Data Loader's Upsert wizard, the user is asked which field should be used to match the parent Account. By selecting the OMS_Account_Number__c external ID, the Data Loader allows the user to map the orders directly to accounts using the OMS number found in the CSV file.

Salesforce will automatically find the correct Account ID in the background. This eliminates the "export-and-VLOOKUP" step entirely. Option A (Insert) would still require internal IDs. Option B (sorting) helps with performance in massive loads but doesn't solve the manual ID matching problem. Using Upsert with External IDs is the platform-recommended way to streamline recurring data integrations.

This requirement calls for "Transactional Atomicity," meaning either both database operations (Account creation and Contact creation) succeed, or neither is committed to the database. In Apex, each DML statement normally acts as its own individual transaction unless managed by Savepoints.

The correct approach is to use Database.setSavepoint() and Database.rollback() within a try-catch block (Option A). The developer sets a savepoint immediately before the Account is inserted. If the Account is created successfully but the subsequent Contact insertion fails (due to a validation rule, trigger error, or system exception), the code enters the catch block. Within the catch block, the developer executes Database.rollback(sp), which reverts the database to the state it was in before the Account was ever inserted.

Option B is technically similar but "A" provides the standard programmatic pattern name. Option C (allOrNone=false) only applies to a single list of records in one DML call and cannot link the success of an Account to a Contact. Option D (manual deletion) is an unreliable "cleanup" strategy that fails if the system crashes or if there are secondary side effects from the initial insertion. Using Savepoints ensures the platform handles the rollback safely and completely.

In Visualforce, the "View State" is a hidden form field that maintains the state of the page (and the controller's variables) across postbacks to the server. If the View State becomes too large, it can slow down page loads and eventually hit the Salesforce View State limit (170KB), causing the page to crash.

The transient keyword is used to declare instance variables in Apex controllers that should not be saved in the View State. When a variable is marked as transient, its value is discarded after the request finishes and is not transmitted back to the client. This effectively reduces the size of the View State. It is commonly used for data that is needed only for the duration of the current request (like a large list of records displayed in a read-only table) and can be easily requeried or recalculated if needed.

Handling date and time formatting across different countries is a common requirement in global Salesforce orgs. For example, a user in the US expects MM/DD/YYYY, while a user in the UK expects DD/MM/YYYY.

The Date.format() method (Option D) is specifically designed to handle this localization automatically. When called on a Date instance, it converts the value into a String based on the locale settings of the logged-in user. This is the most efficient and localized approach as it requires no manual mapping of date patterns.

Option A (Date.parse) is the inverse; it converts a localized String into a Date object. Option C (String.valueOf) returns a standard, non-localized format (YYYY-MM-DD), which is used for system logging or API payloads but is often confusing for end-users. Option B (String.format) is used for variable substitution within a string (e.g., "Hello {0}") and does not inherently handle date localization. By using the native .format() method, developers ensure a consistent and familiar user experience for employees worldwide.

The requirement is to perform a callout when a record is inserted. Because Salesforce prohibits synchronous callouts after DML operations in the same transaction, this must be handled asynchronously.

Queueable Apex (Option C) is the optimal choice for this integration. When an Account is inserted, a trigger captures the ID and enqueues a Queueable job. Queueable is superior to Future methods (Option D) because it supports complex data types (not just primitives), allows for job chaining, and provides a Job ID that can be used for monitoring via AsyncApexJob.

Before-Save Flows (Option A) cannot directly perform callouts, and while they can trigger asynchronous paths, the programmatic control offered by a Trigger-to-Queueable pattern is more robust for complex third-party integrations. Batch Apex (Option B) is designed for bulk processing of existing records and is "overkill" for a real-time, record-by-record trigger requirement. By using a Trigger with Queueable, the developer ensures the address validation happens nearly in real-time without blocking the user's initial save transaction or hitting concurrent request limits.

For storing application configurations and integration settings that need to be easily modified by administrators and deployed across environments, Custom Metadata Types are the preferred solution. Unlike Custom Settings (Options A and D), records within a Custom Metadata Type are considered metadata rather than data. This is a critical distinction for the development lifecycle because these records can be included in Change Sets or deployment packages. This eliminates the manual overhead and risk associated with re-creating configuration records in production after a sandbox deployment.

In this scenario, UC needs to manage settings for two different payment processors. A Custom Metadata Type can be created with fields for API endpoints, merchant IDs, and security keys. An administrator can then create and edit the specific records for the "Individual" and "Corporate" processors directly in the Setup menu. Furthermore, Custom Metadata queries are efficient and do not count against standard SOQL governor limits in many contexts. While Custom Labels (Option B) are useful for translating text, they are not intended for complex, structured configuration data. Hierarchy Custom Settings are designed for user-specific overrides, which is not applicable here. Therefore, Custom Metadata provides the most robust, deployable, and administrator-friendly way to manage external service configurations.

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In software engineering, the DRY (Don't Repeat Yourself) principle is fundamental for maintainability. If the logic for normalizing an address is identical for both Accounts and Contacts, that logic should not be duplicated across two separate trigger files.

The optimal implementation (Option D) is to create a single Trigger Helper or Utility class. This class contains a static method (e.g., AddressService.normalize(List records)) that performs the normalization logic. You then create a small trigger on the Account object and a small trigger on the Contact object, both of which simply invoke this shared helper method.

This approach offers several advantages:

Maintainability: If the company standard for addresses changes (e.g., changing "St." to "Street"), the developer only needs to update the code in one place.

Reusability: The helper method can also be called from other contexts, such as an Anonymous Apex script or a Batch job.

Readability: Triggers remain "thin" and easy to read, acting only as entry points rather than containing complex business logic.

Options B and C are technically impossible; a trigger on one object cannot "call" a trigger on another object directly. Option A is less optimal because it leads to code duplication and a higher risk of bugs when logic is updated in one trigger but forgotten in the other.

In Salesforce integration, the Upsert operation is highly powerful because it allows you to create or update records and relate them to other records using External IDs instead of Salesforce record IDs ($001...$).

When the ERP system sends an order, it may not know the 18-character Salesforce ID for the Account, but it does know the ERP_Customer_ID__c. By performing an Upsert on the Order__c object (Option A), the integration can leverage "Foreign Key" syntax in the API payload. This essentially tells Salesforce: "Create this Order and link its Account__c lookup field to the Account that has this specific ERP_Customer_ID__c."

This approach is the "Gold Standard" for integrations because:

It eliminates the need for the external system to perform a "lookup" query in Salesforce to find an ID before creating a record.

It reduces the total number of API calls, saving on governor limits.

It handles "idempotency," ensuring that if the same order is sent twice, it is updated rather than duplicated.

Options B, C, and D are less efficient. Upserting the Account (Option B) doesn't create the Order. Merging (Option C) is for deduplication. The Insert-then-Update approach (Option D) is an anti-pattern that doubles the required API calls and transaction overhead.

When the structure of a query—such as the number of filters or the specific fields in the WHERE clause—is not known at compile-time and must be determined at runtime based on user input, "Dynamic SOQL" is the required feature. Unlike static SOQL, which is written directly in the Apex code and validated during compilation, Dynamic SOQL allows a developer to construct a query as a string.14

In this scenario, the user might choose to filter by 'Industry' and 'AnnualRevenue' in one session, but only by 'BillingCity' in another. By using Database.query(queryString), the developer can programmatically append WHERE clauses to the base query string based on the selections made in the Lightning Web component. This provides the flexibility to handle the varying number of fields (up to five) without writing dozens of different static queries or complex "if-else" blocks that are difficult to maintain.

While describeSObjects() (Option A) can be used to get metadata about the fields, it doesn't execute the query. SOSL (Option C) is for text-based searching across multiple objects and is less efficient for specific field filtering on a single object. The REST API (Option B) is an external interface and not a feature of Apex used to solve internal filtering logic within an LWC controller. Therefore, Dynamic SOQL is the standard tool for building highly flexible, user-driven search interfaces on the platform.

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The lightning-record-form is a powerful, high-level component that simplifies data entry and display. However, its performance is heavily influenced by the layout-type attribute. When layout-type="Full" is used, the component fetches and renders every single field defined on the object's "Full" page layout in the Salesforce metadata. In this case, the Account object has 275 fields. Fetching and rendering such a large volume of metadata and data causes a significant performance lag, even if the user only cares about three specific fields.

To improve performance, the developer should switch from a layout-based approach to a field-based approach. By removing the layout-type attribute and adding the fields attribute (Option A), the developer can pass an array of only the specific field API names required (e.g., ['Name', 'CustomField1__c', 'CustomField2__c']). This drastically reduces the amount of data requested from the Lightning Data Service (LDS) and minimizes the DOM elements the browser needs to render. Option C is incorrect because "Partial" is not a valid value for layout-type; the only supported values are "Full" and "Compact". Option B only affects the visual spacing of the fields and does not reduce the data load. Option A is the direct and most effective way to optimize component responsiveness by limiting the data scope.