Salesforce-Platform-Development-Lifecycle-and-Deployment-Architect Practice Test Questions

A.   An application with many systems and inter-dependencies between components.

B.   An application with regulatory compliance requirements to be validated by outside agencies.

Explanation:

✅ A. An application with many systems and inter-dependencies between components.
This favors Waterfall because:
You typically need a large amount of upfront planning and design.
Complex integrations and dependencies are easier to manage when the architecture is fully defined before build.
Changes late in the project are expensive and risky, so a more rigid, phased approach works better.

✅ B. An application with regulatory compliance requirements to be validated by outside agencies.
Also a strong fit for Waterfall because:
Compliance-heavy projects usually require extensive documentation, formal sign-offs, and traceability.
Outside agencies often expect sequential stages (requirements → design → build → test → validate).
Requirements are usually fixed and tightly controlled, which suits a Waterfall approach.

❌ Why the others are Incorrect:
C. An application in post-production, with incremental changes made by a small team.
This is ideal for Agile:
Small, incremental changes.
Continuous feedback from users.
Ability to prioritize quick wins and adapt frequently.

D. An in-house application with a fixed team size, but an open timeline and flexible requirements.
“Flexible requirements” and an “open timeline” scream Agile, not Waterfall.
Agile thrives when scope can evolve and the team can learn and adjust as they go.

So the situations that favor Waterfall methodology are: A and B.

A.   Can store code on a local machine, or a version control system.

C.   Can create scratch orgs.

E.   Can Install application metadata from a central repository.

Explanation:

This question tests the fundamental advantages of the modern Salesforce DX (SFDX) development model over the older, org-centric model.

Why A is Correct:
This is the core concept of source-driven development. SFDX treats your source code and metadata as the source of truth, not the org. You develop locally using the SFDX CLI and an IDE like VS Code, and your work is stored in a version control system (like Git). This enables modern DevOps practices like branching, merging, code reviews, and continuous integration.

Why C is Correct:
The ability to create scratch orgs is a revolutionary feature of SFDX. Scratch orgs are temporary, fully configurable Salesforce environments that are spun up on-demand and can be discarded after use. They are:
- Disposable: Perfect for feature development and testing.
- Configurable: Can be defined by a configuration file to have specific features, settings, and sample data.
- Source-Tracked: Integrate seamlessly with the source-driven development model, allowing you to easily pull changes back to your local source.

Why E is Correct:
This refers to the ability to create and install packages. SFDX streamlines the packaging process (both unlocked and managed packages), allowing you to build your application metadata into a distributable artifact from your source code. This artifact can then be installed from a central repository (like the Package Manager) into any Salesforce org, which is essential for distributing applications and managing dependencies.

Why B is Incorrect:
Execute Anonymous is a feature for running arbitrary blocks of Apex code. It is not a deployment tool and is not a specific advantage of SFDX. In fact, using it for deployment is an anti-pattern. SFDX provides proper deployment commands like sfdx force:source:deploy and sfdx force:mdapi:deploy which are the correct, robust methods for moving metadata.

Why D is Incorrect:
Salesforce does not have a native, one-click "Deployment Rollback Tool" that reverts a deployment to a prior state. While there are strategies for handling deployment failures (e.g., having a backup change set, using source control to track the previous state, or in some cases a "Quick Deploy" for validated deployments), there is no built-in rollback feature. This is a common misconception and a key reason why thorough testing and validation in a pre-production environment are so critical.

Key Takeaway:
The key advantages of SFDX are its enablement of source-driven development, the use of scratch orgs for agile development, and a streamlined packaging and distribution model.

A.   Create two second generation managed packages with the same namespace and set the methods that should be shared with the @namespaceAccessible annotation.

Explanation:

A. Create two second generation managed packages with the same namespace and set the methods that should be shared with the @namespaceAccessible annotation.

Second-Generation Managed Packages (2GP) with Same Namespace: The ability for multiple packages to share the same namespace is a key feature of 2GP. This allows the packages to be treated as a single, modular application for internal development and distribution, while still maintaining logical separation between the Sales and Service products.
@namespaceAccessible Annotation: This annotation is designed to expose public Apex methods, variables, and properties from a class within one managed package (the Service package) so that they can be accessed by Apex code in another managed package that shares the same namespace (the Sales package).
Enforced Control: By using this annotation, the architect ensures that the Sales team can only call the specific functions that the Service team explicitly marked as intended for cross-package use, preventing access to internal or unvetted Service functions. The restriction "No other team will use these same functions" is met because namespaceAccessible limits access to only those packages within the shared namespace (which, in this architecture, are only the Sales and Service packages).

❌ Incorrect Answers and Explanations

B. Create two managed packages with Sales and service namespaces. Set the methods to be shared with the @salesAccessible annotation
The @salesAccessible annotation does not exist. Furthermore, if the packages had different namespaces, the only way to share functionality would be to make the methods global, which would expose them to all subscribers (including non-Sales teams), violating the requirement for restricted access.

C. Create a managed package with both products and create a code review process with an approver from each team.
Creating a single package contradicts the organizational need for separation and modularity (two different teams creating two different products). While a code review process is vital for quality, it is a manual governance control, not a technical enforcement mechanism. It doesn't prevent the Sales team's code from calling internal Service functions; it just relies on a reviewer to catch the violation.

D. Create two managed packages. Create an authentication function... and validate the token...
This is a vast, unnecessary over-engineering of the solution. Using tokens and authentication functions is appropriate for secure communication between different organizations (e.g., via APIs) but is entirely unnecessary and overly complex for enabling internal, cross-package communication within the same Salesforce org, especially when the platform provides the dedicated @namespaceAccessible feature for this exact purpose.

📚 References
Salesforce Developers, Apex Access Modifiers for Packages: The @namespaceAccessible annotation is the official mechanism for fine-grained control over which components are exposed between packages that share a common namespace.
Second-Generation Managed Packaging (2GP): The architecture relies on the concept of a Dev Hub where multiple packages can be built and grouped under a single namespace for internal modular development.

A.   Team development of an application in a Developer Edition organization. After completing development and testing, the application is Distributed via Lightning Platform AppExchange.

D.   Development of a custom application in a sandbox organization. After completing development and testing, the application is then deployed into a production organization using Metadata API.

Explanation:

Why A is a primary use case
When building an AppExchange managed package, the standard and officially supported development flow is:

Develop in a Developer Edition or packaging org
Use Metadata API retrieve() to pull the metadata into a local file system
Version control the files
Use Metadata API deploy() to push new package versions
This is exactly how the Force.com Migration Tool (Ant), Partner Packaging scripts, and most ISV build systems work. Salesforce explicitly lists this as the primary scenario for direct Metadata API calls.

Why D is a primary use case
For enterprise internal applications or customizations developed in a sandbox, the classic and still widely used pattern is:

Develop in a sandbox
Use retrieve() via Ant Migration Tool or similar to extract the metadata
Store in version control
Use deploy() to move the metadata through higher environments and finally into production
This is the original and still-official use case for the Metadata API and tools like the Ant Migration Tool.

Why B is incorrect
When using scratch orgs and Salesforce CLI (SFDX), the primary commands are sfdx force:source:pull/push or sfdx force:source:deploy. These use the Source API, not the Metadata API. Direct retrieve() and deploy() calls are not the standard or recommended approach in a modern SFDX workflow.

Why C is incorrect
While the Ant Migration Tool does use Metadata API under the hood, the question specifically asks for scenarios where developers write code to call retrieve() and deploy() directly (i.e., via Apex, Java, Node.js, etc.). Using the pre-built Ant Migration Tool is not “writing code to call the methods directly.”

References
Salesforce Metadata API Developer Guide → “Primary Use Cases”
Explicitly lists:

ISV/AppExchange package development (A)
Enterprise deployments using custom scripts (D)

ISVforce Guide → “Packaging with Metadata API”
Recommends direct retrieve/deploy calls for AppExchange partners.

Bonus Tips
Memorize: Writing code to call retrieve() and deploy() directly → AppExchange packaging (A) + enterprise custom deployments (D).
SFDX/scratch orgs = Source API, not Metadata API → never the answer here.
This exact question appears very frequently on the real Development Lifecycle and Deployment Architect exam.

A.   JSTEST

C.   WebDriver Test

Explanation:

This question tests knowledge of the specific testing frameworks provided by Salesforce for Aura components, particularly for accessibility testing. The key is knowing which test runners support the Aura Accessibility test suite.

Why A is Correct (JSTEST):
JSTEST is a Node.js-based test runner provided by Salesforce specifically for running unit tests for Aura components. It allows you to run tests from the command line, which is ideal for integrating into a CI/CD pipeline. The Aura accessibility tests can be executed within this environment to validate components in isolation.

Why C is Correct (WebDriver Test):
WebDriver is a standard for automating web browser interactions. It is used for end-to-end (E2E) testing. When running Aura component tests with WebDriver, you can invoke the Aura accessibility test suite to check for accessibility issues within the context of a fully rendered component in a browser, which can catch issues that might not appear in an isolated unit test.

Why B is Incorrect (ACCTEST):
There is no officially recognized Salesforce testing framework or environment called ACCTEST. This appears to be a distractor.

Why D is Incorrect (AuraDriver Test):
While AuraDriver is a real Salesforce library that simplifies writing WebDriver tests for Aura components, it is not a distinct test environment. It is a tool used within a WebDriver test. Therefore, the correct answer is the broader category of "WebDriver Test," which encompasses tests written using AuraDriver.

Key Takeaway:
Salesforce's Aura accessibility tests can be run in two primary contexts: the JSTEST framework for unit-level testing and the WebDriver framework for end-to-end, browser-level testing.

A.   Generate a destructivechanges.xml file and deploy the package via the Force.com Migration Tool

Explanation

Here’s why.
When you have a large list of metadata to remove and you want to:
Control what is deleted
Run it through lower environments first
Minimize impact on other work streams
Keep everything repeatable and auditable

…the Salesforce-recommended approach is to use Metadata API with a destructiveChanges.xml file (often via the Force.com Migration Tool / Ant or SFDX equivalent).

Why A is correct
You list all components to be deleted in destructiveChanges.xml.
You can:
Run the deployment in a sandbox first (dev → QA → UAT → prod).
Version-control the deletion scripts.
Coordinate timing with other teams and pipelines.
It’s automated, repeatable, trackable, and works well alongside ongoing development.

Why the others are not ideal
B. Perform deletes manually in a sandbox and then deploy a Change Set to production
Manual deletes are error-prone and hard to reproduce.
Change Sets don’t support all delete scenarios cleanly, especially at scale.
Not great for a large list of technical-debt components.

C. Assign business analysts to perform the deletes and split up the work
Completely manual, no single source of truth.
High risk of inconsistency between environments.
No proper release management or rollback strategy.

D. Delete in production and then refresh all sandboxes
Very risky: you’re changing production first.
Refreshing all sandboxes:
Disrupts current work streams.
Is limited by sandbox refresh intervals.
You lose the chance to test deletions safely in lower environments first.

So the architect should recommend:
Using destructiveChanges.xml with the Metadata API (Force.com Migration Tool) — Option A.

A.   Developers can see automated test statuses that commit on a specific user story.

D.   Developers can use the committed code's build status directly on the user story record.

Explanation:

A. Developers can see automated test statuses that commit on a specific user story.
When a developer commits code using the user story's ID (e.g., git commit -m "Fixing bug ABC-123"), the CI tool (e.g., Jenkins) triggers the automated build and testing process. The integration pushes the test results (Pass/Fail) back to the issue tracker. This provides a rapid feedback loop, allowing the developer and the team to immediately see if their recent code change broke any tests for that specific story, which is crucial for maintaining code quality.

D. Developers can use the committed code's build status directly on the user story record.
Similar to test status, the integration ensures the build status (Success/Failure) is reported directly on the user story. This establishes a single source of truth and increases transparency. Team members (Developers, Scrum Masters, Product Owners) can look at the user story in the issue tracker and instantly know if the code needed to complete that story has been successfully built and deployed to the testing environment without having to log into the CI tool.

❌ Analysis of Incorrect Options
B. Developers can collaborate and communicate effectively on specific user stories.
Collaboration and communication are benefits of using an agile issue tracker itself (its comments, attachments, and workflow features), not specifically a benefit gained by integrating it with CI/Testing tools. The integration provides automated, factual data, not a communication channel.

C. Developers can observe their team velocity on the burn chart report in the agile tool.
The Team Velocity and Burn Down/Up Charts are core reports generated by the agile issue tracker based on the status changes of the user stories (e.g., moving from "In Progress" to "Done"). While CI/CD helps the team achieve a higher velocity, the reporting mechanism for velocity is a native function of the agile tool, not a benefit directly derived from integrating the build/test status.

A.   Identify the data relevant to the new system, including dependencies, and develop a plan/scripts for verification of data integrity.

D.   Use a full sandbox environment and perform test runs of data migration scripts/processes with real data from the legacy system.

Explanation:

This question focuses on risk mitigation specifically for the complex and high-stakes process of data migration. The core risks are data corruption, loss, and poor quality, which can doom a new system from the start.

Why A is Correct:
This is the foundational strategy for any data migration. You cannot migrate what you don't understand.
Identify Relevant Data & Dependencies: This involves data profiling and mapping to determine what data to bring over, how it relates to other data, and how it maps to the new Salesforce data model. This prevents migrating obsolete or irrelevant data.
Verification Plan/Scripts: You must have an automated, repeatable way to verify that the data was migrated correctly. This means writing scripts to check record counts, validate field-level data integrity, and ensure relationships were preserved. Without this, you have no objective measure of success.

Why D is Correct:
This is the critical "practice run" strategy. A Full Sandbox provides a production-like environment with the necessary storage and configuration.
Test Runs with Real Data: Performing multiple trial migrations using real data from the legacy system is the only way to uncover hidden issues with data quality, transformation logic, performance, and volume. You can identify errors, refine scripts, and accurately estimate the time required for the final production migration.
This practice de-risks the final production cutover significantly.

Why B is Incorrect:
This describes a phased user adoption or rollout strategy, which is a valid approach for deploying the application to minimize user disruption. However, it is not a primary strategy for mitigating data migration risks. The data itself needs to be migrated completely and accurately, regardless of whether users are phased in. This option addresses a different type of risk (user adoption) rather than the core technical risk of moving the data correctly.

Why C is Incorrect:
While using a Full Sandbox is correct, the rest of this option is flawed. You cannot use a "full sandbox environment for all the systems involved." A Salesforce sandbox only contains your Salesforce org. You cannot host the legacy system inside a Salesforce sandbox. Furthermore, using "test data generation scripts" is the opposite of what you need for a data migration dry run. You need to test with the real, actual data to find the real-world problems, not with synthetic, generated data.

Key Takeaway:
To mitigate data migration risks, an architect must recommend a strategy based on thorough data analysis and mapping (Option A) and rigorous testing with real data in a production-like environment (Option D).

B.   Create development standards and train teams in those standards.

D.   Use static code analysis tool in the pipeline before manual code review.

Explanation:

To improve both code quality and team agility, the goal is to reduce the burden on the technical lead, prevent recurring issues, and shift quality left in the development lifecycle. Let’s break down the correct answers:

✅ B. Create development standards and train teams in those standards
Why it works:
Establishes clear expectations for code structure, naming, patterns, and practices
Reduces subjective review cycles and repetitive feedback
Empowers developers to self-correct before submitting code
Training ensures that all team members are aligned, reducing the volume of issues caught during review

✅ D. Use static code analysis tool in the pipeline before manual code review
Why it works:
Tools like PMD, ESLint, or CodeScan can catch:
- Syntax errors
- Code smells
- Security vulnerabilities
- Style violations
Automates early detection, reducing the manual review load
Ensures consistency and objectivity in code quality checks

❌ Why the Other Options Are Not Ideal
A. Choose the most senior developer to help the technical lead in the code review
While this may temporarily reduce the load, it doesn’t scale or solve the root cause
It still centralizes reviews among a few individuals, limiting team-wide learning and ownership

C. Skip the code review and focus on functional tests and UAT
Skipping code reviews:
- Increases technical debt
- Misses architectural, readability, and maintainability issues
Functional tests cannot catch design flaws or poor coding practices

C.   Automated browser testing

D.   Static code quality analysis

Explanation:

Here’s why these two are the best fit for a CI/CD pipeline:
✅ C. Automated browser testing
Can be run automatically on every build or on a schedule.
Validates end-to-end user flows in the actual UI (e.g., login, create opportunity, submit case).
Helps catch:
- Broken buttons/links
- Miswired flows
- Regression issues introduced by new changes
Tools: Selenium, WebDriverIO, Cypress (via API/External), etc. integrated into CI.
This is a classic part of a modern CI/CD setup.

✅ D. Static code quality analysis
Runs as part of the build pipeline before or along with tests.
Automatically checks:
- Code smells
- Security issues
- Complexity and anti-patterns
- Style and convention violations
Tools: PMD, CodeScan, SonarQube, ESLint (for JS), etc.
Helps catch defects before they ever reach deployment or UAT.
This is one of the primary quality gates in CI/CD.

❌ Why not A and B?
A. Business verification testing
Usually refers to manual or semi-manual UAT / business validation.
Typically done by business users or QA outside of the automated CI pipeline.
Hard to fully automate, and it tends to run per major release, not every build.

B. Stress testing
Important, but:
- Often expensive and time-consuming.
- Not run on every commit or build.
Typically done in performance testing cycles, not as a standard CI step.
More suited for dedicated performance test phases or scheduled runs, not day-to-day CI.

So, the two QA techniques that best fit into an automated CI/CD pipeline are:
C. Automated browser testing
D. Static code quality analysis