Free Salesforce PDII Practice Exam with Questions & Answers | Set: 5

To write "generic" code in Apex—meaning code that can handle different types of objects (Accounts, Contacts, Custom Objects) interchangeably—the developer should use the base sObject class. All Salesforce objects inherit from this base class.

The sObject class provides a method called .get(fieldName) (Option C). This method allows you to retrieve the value of any field by passing its string name as an argument. By casting the input records (e.g., an Account and an Opportunity) to the generic sObject type, the developer can call .get('Name') on both and compare the returned objects (typically strings). This bypasses the need to know the specific object type at compile-time and avoids complex conditional logic.

Option A is incorrect because String.replace is for string manipulation, not data retrieval. Option B is incorrect because the Metadata API is for modifying org structure, not retrieving record field values. Option D is incorrect because Schema.describe provides information about the field's definition (type, label, length), but it does not provide the actual data value stored in a specific record. Using the generic sObject.get() method is the standard, most efficient way to achieve polymorphism in Apex data handling.

Comprehensive and Detailed Explanation:

When dealing with Large Data Volumes (LDV)—specifically in the range of hundreds of thousands to millions of records—the Bulk API (Option C) is the only optimal choice.

The Bulk API is based on REST principles but is optimized for processing large sets of data asynchronously. Instead of processing records one by one or in small synchronous chunks (which would hit time limits and consume many API calls), the Bulk API allows you to upload a CSV file or JSON batch. Salesforce then processes these records in the background using parallel processing.

Options A and B (REST/SOAP) are designed for real-time, synchronous interactions with a limited number of records and would hit rate limits or timeout for millions of records. Option D is for custom logic and is subject to the same synchronous execution limits as standard REST.

Comprehens7ive and Detailed 150 8to 250 words of Explanation:

In Salesforce Apex testing, the @testSetup annotation is a powerful tool designed to improve test performance and reduce code redundancy. When a method is marked with @testSetup, it executes once before any of the individual test methods in the class run. The data created in this method is then persisted for the entire class.

The primary performance benefit comes from how Salesforce handles this data: the platform creates a "snapshot" of the database state after the setup method finishes. For every subsequent test method in the class, the system simply rolls back the database to this snapshot rather than re-executing the data creation logic. This significantly reduces the time spent on DML operations, which are often the most time-consuming part of a test suite.

Option D (Data Factories) is a best practice for code reuse, but if called inside every test method, it still results in redundant DML operations. Options A and B are incorrect; @createData is not a valid Salesforce annotation, and reducing the number of tests sacrifices code coverage and quality. By using @testSetup, a developer ensures that heavy data initialization happens only once, leading to faster execution cycles and more efficient resource usage during deployments.

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Co30mprehensive and Detailed 150 to 250 words of Explanation:

When retrieving data for display in a custom UI, formatting dates, times, and currencies to match the user's specific locale can be complex. Salesforce provides the FORMAT() function within SOQL specifically to handle this at the database layer.

When you wrap a field in the FORMAT() function, such as SELECT FORMAT(Amount), FORMAT(LastModifiedDate) FROM Opportunity, the platform automatically applies the localized formatting rules based on the user's "Language and Locale" settings. This includes applying the correct currency symbol, thousands separators, decimal points, and date/time structures (e.g., MM/DD/YYYY vs DD/MM/YYYY). For organizations with Multi-Currency enabled, FORMAT() also handles the conversion and display of currency symbols according to the record's currency code.

Using FORMAT() is significantly more efficient than creating a wrapper class (Option B) or using REGEX (Option C), as it leverages the platform's native globalization engine. Option D (FOR VIEW) is used to update the "Last Viewed" date and does not affect the visual formatting of the returned data. By using FORMAT(), the developer ensures that the Lightning component remains lightweight and consistent with the standard Salesforce user experience.

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The most fundamental best practice for writing bulk-safe (scalable) Apex triggers is to avoid performing DML operations or SOQL queries inside of loops. Salesforce enforces a limit of 150 DML statements per transaction. If a trigger iterates through a list of 200 records and performs a delete or update inside that loop, the code will fail with a governor limit exception after the 150th record.

To make a trigger bulk-safe, developers must collect all records that need to be processed into a collection (such as a List or Map) and then perform a single DML operation on that entire collection outside of the loop (Option B). This consumes only one DML statement regardless of the number of records (up to the 10,000 row limit).

Option A is incorrect because LIMIT 50000 does not help with bulk safety; it just prevents a query from exceeding the SOQL row limit. Option C (using allOrNone=false) allows for partial success of a DML operation but does not inherently make a trigger bulk-safe if the calls are still made inside a loop. Option D is incorrect because moving DML to a future method is a way to handle callouts or long-running processes, but it is not a general requirement for trigger bulkification and can lead to reaching asynchronous limits if used incorrectly.

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Source-driven development shifts th21e "source of truth" from the Salesforce Org to a Version Control System (like Git). To b22ridge the gap between local source code and the Salesforce platform, Salesforce CLI with Salesforce DX (Option C) is the required mechanism.

Salesforce DX (Developer Experience) introduced a source-centric metadata format that is more granular and easier to track in version control than the traditional Metadata API. The Salesforce CLI provides the command-line tools necessary to automate the deployment process, create scratch orgs for isolated testing, and perform "source tracking" to identify exactly which files have changed. This is the foundation of modern CI/CD (Continuous Integration/Continuous Delivery) pipelines in the Salesforce ecosystem.

In contrast, Change Sets (Option B) are org-centric and manual, making them incompatible with automated version control. Data Loader (Option A) is for record data, not metadata. Unmanaged Packages (Option D) are used for distribution but do not support the iterative, source-controlled deployment workflow required for professional project management.

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This requirement involves two specific constraints: performing a callout after a database change (DML) and handling hig9h-volume processing where the external service only accepts one rec10ord at a time. In Salesforce, you cannot perform a callout in the same transaction after a DML operation; you must use asynchronous processing.

The Queueable interface is the superior choice here over @future methods. Queueable Apex allows for "chaining" jobs. Since the external web service only accepts one record per call, a developer can write a Queueable class that processes the first record, makes the callout, and then checks if there are remaining records. If more records exist, the class can enqueue a new instance of itself to process the next record. This creates a controlled, sequential execution path that respects the external system's limits.

While @future methods can perform callouts, they cannot be chained and have no built-in way to handle a sequential "one-by-one" requirement for an unknown number of records efficiently. Platform events (Option A) are used for event-driven architectures but would still require an asynchronous consumer to handle the callout limits. Exposing a web service (Option D) is for receiving data, not sending it. Therefore, the Queueable interface provides the necessary flexibility and transaction control to manage the snapshot and individual callout requirements effectively.

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.

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