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

Explanation:

A. Multiple triggers on the same object
Very likely.
In older orgs built by different vendors over many years, it’s common to find more than one trigger per object, each handling a different “piece” of logic. This is considered an anti-pattern because:

It’s hard to predict order of execution.
Logic becomes scattered and difficult to debug.
It violates the common best practice of one trigger per object that delegates to handler classes.

Salesforce reference: Apex Trigger Framework / Best Practice – “One trigger per object, logic in handler classes.”

D. Duplicated logic across Visualforce pages and Apex classes
Also very likely.

With many teams contributing over 8+ years, you often see:

Copy-paste Apex and controller logic across multiple Visualforce controllers.
Similar validation, querying, or transformation logic repeated across classes.
No central service layer or common utility classes.

This leads to maintenance pain and inconsistent behavior when only some copies get updated.

E. Custom-built JSON and String manipulation classes that are no longer required
Very plausible in an 8-year-old org.

Earlier in Salesforce history, teams often wrote:

Custom JSON serializers/deserializers.
Custom String utilities (trimming, padding, searching, etc.).

Over time, Salesforce added rich built-in JSON (e.g., JSON.serialize, JSON.deserialize) and String methods, plus features like JSON.deserializeUntyped, JSONGenerator, etc. Those older custom utilities may:

Be obsolete, but still clutter the codebase.
Increase confusion about which utilities to use.

Why not the others?

B. Multiple unit test failures would be encountered.
Not necessarily. To deploy changes, Salesforce requires tests to pass (and 75% org-wide coverage). Even if the code is ugly, as long as deployments have been happening, key tests probably pass. Failing tests can exist, but it’s not something you would expect by default just because the org is old.

C. Broken functionality due to Salesforce upgrades.
Salesforce is strongly backward compatible. While behavior changes can sometimes surface edge issues, it is not common for standard upgrades to directly break existing, supported patterns of custom code. So this is less likely than architectural/code-smell issues like A, D, and E.

Explanation:

This question tests the fundamental knowledge of the core components of a modern Application Lifecycle Management (ALM) toolchain. A robust ALM methodology requires tools for tracking work, managing code changes, and automating the build and deployment processes.

Why B is Correct (Version Control Repository):
This is the non-negotiable foundation of any professional software development lifecycle. A version control system (like Git) is used to:

Track all changes to code and metadata.
Maintain a complete history of who changed what and why.
Enable branching and merging strategies, allowing for parallel development (e.g., feature branches, release branches).
Act as the single source of truth for all project artifacts. Without version control, collaboration, rollback, and auditability are nearly impossible.

Why D is Correct (Continuous Integration Tool):
Continuous Integration (CI) is a practice and a tool that automates the process of building and testing code every time a developer commits a change to the version control repository. A CI tool (like Jenkins, Copado, Azure DevOps, Salesforce CLI) is critical for:

Running automated tests to immediately catch regressions.
Validating that code from different developers integrates correctly.
Packaging code for deployment. This automation enforces quality gates and is essential for a repeatable and reliable deployment process.

Why E is Correct (Issue Tracking Tool):
An issue or work item tracking tool (like Jira, Azure Boards, or Salesforce Agile Accelerator) is essential for managing the application lifecycle from a business and project management perspective. It is used to:

Capture requirements (e.g., user stories).
Track bugs and defects.
Manage the development workflow (e.g., To Do, In Progress, Done).
Provide traceability between a business requirement and the code that implements it. This tool is the central hub for planning and communication.

Why A is Incorrect (Database Management Systems):
While a DBMS is critical for the application itself, it is not a tool for managing the lifecycle. The lifecycle tools interact with the Salesforce platform (the "database" in a broader sense) via APIs, but recommending a specific DBMS is not part of defining an ALM methodology for Salesforce.

Why C is Incorrect (Middleware):
Middleware is used for application integration, such as connecting Salesforce to other external systems (e.g., using MuleSoft). It is a tool for the solution architecture, not for managing the development, testing, and deployment lifecycle of the Salesforce application itself.

Key Takeaway:
The core ALM toolchain for a disciplined development process consists of:

Version Control for source code management.
CI Tool for build and test automation.
Issue Tracker for work management and traceability.

These three tools work together to provide governance, automation, and visibility throughout the application lifecycle.

Explanation:

Change sets are notorious for failing during deployment even after they have been successfully uploaded, especially in large or complex orgs. The most common causes of delays are missing dependencies that were not automatically detected and included in the change set (e.g., profiles, permission sets, list views, custom labels, remote site settings, etc.).
Running Validate (not just Upload) before the actual deployment does the following:

Executes all Apex tests in the target org.
Performs a full dry-run of the deployment.
Surfaces all missing dependencies and errors ahead of time.
Allows the team to add the missing components or fix issues while the change set is still in the sandbox, preventing last-minute surprises and deployment-queue delays in production.

Why the Other Options Are Incorrect

A. Change set performance is independent of included components
This statement is completely false and contradicts well-documented Salesforce behavior. Deployment time and success rate are heavily influenced by the number, size, and type of components in a change set. For example, including full Profile or Permission Set deployments (especially in orgs with hundreds of users and objects) can take hours and frequently fails due to hidden dependencies or size limits. Large numbers of custom fields, sharing rules, Apex classes, or reports can also dramatically slow down or break the deployment. Salesforce itself warns that change sets with too many components or certain component types (e.g., profiles) are prone to timeouts and errors. Claiming performance is “independent” of what’s included is the opposite of reality.

B. Manually create new custom objects and new custom fields
A Technical Architect would never recommend manually recreating metadata directly in production as a way to speed up deployments. Doing so completely bypasses version control, automated testing, code review, governance, and audit trails — all of which are mandatory for any enterprise org. It also creates discrepancies between environments, making future deployments even harder (you now have metadata in production that doesn’t exist in sandboxes or source control). This is considered one of the worst anti-patterns in Salesforce development and is explicitly called out in the Application Lifecycle and Development Models Trailhead modules as something to avoid at all costs.

C. Manually apply the field type changes
Manually changing field types (e.g., Text → Picklist, Number → Text (Length), or anything that involves data transformation) directly in production is extremely high-risk and often irreversible. Many field-type changes cause data truncation or loss, break existing integrations, reports, formulas, and Apex code, and can even lock records. Salesforce restricts many field-type changes in production precisely because they are dangerous. The correct process is to make the change in a sandbox, test thoroughly (including data migration if needed), and deploy via a proper ALM process — never to do it by hand in production as a “workaround” for change set issues.

Reference:
Trailhead and Salesforce Help both explicitly recommend validating change sets before deploying to production to “avoid surprises and reduce deployment time.”
Salesforce DevOps documentation now discourages heavy reliance on change sets in favor of unlocked packages or Metadata API, but when change sets must be used, validation is the key mitigation step.

Explanation:

A. Introduce manual code review before deployment to the testing sandbox.
Explanation: A manual code review (often performed via a Pull Request/Merge Request approval process) is a quality gate enforced by developers and architects. By requiring a review before merging code into the main branch and deploying it to a shared testing sandbox, you ensure that another pair of eyes checks for:
Logic Errors: Issues that static analysis might miss.
Adherence to Best Practices: Trigger framework usage, proper bulkification, and readable code structure.
Architectural Alignment: Compliance with the overall design.
Why before the testing sandbox? This is the crucial point. In CI, quality checks should happen as early as possible ("Shift Left"). Checking the code before it is integrated into the shared sandbox prevents bad code from ever contaminating the test environment and ensures the code being tested is of high quality.

D. Introduce static code analysis before deployment to the testing sandbox.
Explanation: Static Code Analysis (SCA) is a crucial, automated quality gate in any CI pipeline. Tools like Salesforce Code Analyzer, PMD, or SonarQube scan the code (Apex, Visualforce, LWC, etc.) without executing it to check for:
Security Vulnerabilities (e.g., SOQL injection).
Code Smells (e.g., excessive complexity, duplicated logic).
Anti-Patterns (e.g., hardcoding IDs).
Enforcement: The CI tool can be configured to fail the build if the SCA result exceeds a defined severity threshold. This enforces the quality policy, preventing low-quality code from being deployed to any environment. Introducing this before deployment to the testing sandbox is the earliest and most effective place to catch these technical flaws.

❌ Incorrect Answers and Explanations
B. Introduce manual code review before deployment to the production org.
Explanation: While a final review before Production is good practice, it is too late for an enforcement step aimed at improving CI quality. The goal of Continuous Integration is to validate code quality early and frequently. If poor-quality code has already been deployed to and tested in multiple sandboxes, identifying a quality issue at the Production stage creates maximum friction and deployment delays. The review should be done at the start of the pipeline (A).

C. Increase the size of the testing team assigned to the project.
Explanation: Increasing the size of the testing team focuses on improving the quality of functional testing (finding defects in how the application works), not the quality of the underlying code itself (fixing the root cause: decreased code quality). Defects caused by poor code (e.g., non-bulkified Apex, security flaws) are best addressed by developers using automated tools (D) and peer review (A) in the CI phase, not by adding more manual testers.

📚 References
The Salesforce Development Lifecycle and Deployment Architect exam strongly aligns with DevOps best practices, which emphasize "Shift Left" quality gates.

Static Code Analysis (D) as a CI Quality Gate:
Salesforce Developers, Salesforce Code Analyzer
Relevant Concept: SCA tools are designed to be integrated into the CI/CD pipeline and execute automatically to identify code quality issues and security vulnerabilities, ensuring the code base adheres to standards before promotion.

Code Reviews (A) as an Early Quality Gate:
Salesforce Developers, Streamlining Development: Best Practices for Salesforce DevOps and Continuous Integration (Focus on Pull Request/Merge Request practices)
Relevant Concept: Code reviews serve as a manual enforcement of best practices and logic integrity, and by integrating this review with the merge to the main branch (which triggers the CI deployment to the testing sandbox), it becomes an essential early quality gate.

Explanation:

This question addresses the critical need for traceability and version control across complex, multi-environment development lifecycles, especially for a "large transformation journey." The key requirement is to track which version of each feature is in which environment.

Why B is Correct:
A specialized DevOps tool (such as Copado, Autorabit, Flosum, Gearset, or Azure DevOps with the Salesforce Extension) is designed explicitly for this purpose. These tools provide:

Integrated Version Control: They tightly couple a version control system (like Git) with Salesforce metadata, ensuring every change is tracked and versioned.

Environment Management: They provide a clear, visual dashboard showing exactly which commit or user story (feature) has been deployed to each environment (e.g., Dev, UAT, Staging, Production).

Audit Trail: They maintain a complete history of who deployed what, when, and why, linking deployments back to specific user stories or bug fixes.

Automation & Compliance: They automate the promotion of changes through the environments, enforcing governance and reducing human error. For a large project, this is the only scalable and reliable solution.

Why A is Incorrect:
Using an Excel sheet is a manual, error-prone, and non-scalable approach. It relies on human discipline to update the document for every single change, which is unsustainable in a large team. It becomes outdated quickly, provides no automation, and offers no integration with the actual deployment process or version control. It is the antithesis of a robust DevOps practice.

Why C is Incorrect:
Change Sets are a deployment mechanism within Salesforce orgs, but they are a poor tool for tracking and versioning. They do not provide:

- A history of what was in a past deployment.
- A clear link between a metadata component and the feature/bug it belongs to.
- An easy way to see the differences between environments.
- Integration with a version control system. Change Sets are siloed and lack the overarching lifecycle management view required for a large project.

Why D is Incorrect:
Salesforce DX (SFDX) CLI commands are a powerful execution tool for deployments, but they are not, by themselves, a tracking or management solution. While SFDX enables a source-driven development model (which is excellent), the question is specifically about tracking which version is in which environment. This requires a layer of orchestration and visualization on top of the CLI commands, which is precisely what the specialized tools in option B provide.

Key Takeaway:
For a large-scale Salesforce project, the Deployment Architect must recommend an integrated DevOps platform. These tools provide the necessary "single source of truth" for the entire application lifecycle, connecting version control, environment status, and deployment automation to give full visibility and control over what is where.

Explanation:

A. An easy way to deploy related components ✅
Change sets are good for simple, admin-driven releases where you:

- Select related components (objects, fields, validation rules, page layouts, etc.)
- Bundle them into a single unit
- Deploy them to a directly connected org (e.g., sandbox → production)

This fits the scenario of simple configuration changes and enhancements in Sales Cloud.

C. A simple and declarative method for deployment ✅
Change sets are:

- Point-and-click, no code or CLI required
- Available directly in Salesforce Setup
- Suitable for admins or less technical users

So they provide a declarative deployment mechanism, which is exactly what the question is targeting.

Why not the others?

B. The ability to deploy a very large number of components easily ❌
Change sets are not great for large, complex deployments. They can be:

- Slow and tedious to build (manual selection)
- Hard to manage when the number of components grows

D. The ability to track changes to components ❌
Change sets do not offer true versioning or detailed change tracking. For that, you’d use version control (Git) or specialized DevOps tools, not change sets.

So the best two advantages here are A and C.

Explanation:

Why A and C are correct

A. A digitization project to update an existing customer-facing process and enable quick adjustments
Correct – This screams classic Agile. The business wants to modernize a process, get it in front of users fast, gather feedback, and iterate quickly. Requirements are expected to evolve, and the ability to pivot based on real user behavior is a key success factor.

C. An environment with a heavy investment in DevOps capabilities for rapid testing and deployment
Correct – Mature CI/CD pipelines, automated testing, feature flags, sandbox refreshes, and one-click deployments are the technical enablers that make short Agile sprints (1–2 weeks) actually feasible on Salesforce. Without strong DevOps, most teams cannot deliver working increments frequently enough to call it true Agile.

Why the other two are incorrect (they actually favor Waterfall or hybrid)

B. A project to be executed by a third party, with a fixed and formal scope, budget, and timeline
Incorrect – Fixed-scope, fixed-price, fixed-timeline contracts with an external SI are the textbook definition of when Waterfall (or at best a Waterfall-with-stages approach) is used. The commercial model and legal contract usually make changes of scope extremely difficult and expensive, so everyone locks requirements up-front.

D. A project with well-defined requirements and complex interactions between front- and back-end systems
Incorrect – When requirements are already well understood and the biggest risk is technical integration complexity (e.g., SAP ↔ MuleSoft ↔ Salesforce ↔ external billing system), a sequential, big-design-up-front approach (Waterfall or disciplined phased delivery) is usually favored so that architects can finalize interfaces and data models before heavy coding starts.

Explanation:

A. Developers can see automated test statuses that commit on a specific user story.
Explanation: This integration creates a direct link between the code changes (the commit) and the work item (the user story). When a developer commits code that references a user story ID, the CI tool runs automated tests. The integration pushes the result of those tests (pass/fail status) directly back to the user story record in the agile tracker. This provides immediate, transparent feedback to everyone on the team about the quality of the code delivered for that specific feature.

D. Developers can use the committed code's build status directly on the user story record.
Explanation: Similar to the test status, the build status (e.g., successful compilation, no major errors, successful deployment to a temporary environment) is a critical piece of information generated by the CI tool. Displaying the build status directly on the user story record provides real-time visibility into the progress of the feature.
A successful build means the code is stable and ready for the next stage (manual testing or deployment).
A failed build means the developer must immediately stop and fix the issue, which is a key principle of CI (early issue detection). This real-time traceability streamlines the development workflow.

❌ Incorrect Answers and Explanations
B. Developers can collaborate and communicate effectively on specific user stories.
Explanation: While integration improves collaboration by increasing transparency, collaboration and communication are functions of the agile issue tracker itself (through comments, mentions, and shared context), not the direct result of integrating it with the CI/testing tools. The primary benefit of this specific integration is automation-driven status reporting and quality assurance (A & D).

C. Developers can observe their team velocity on the burn chart report in the agile tool.
Explanation: The burn-down or burn-up charts (which show team velocity) are generated by the agile issue tracker alone based on the status of the user stories (e.g., changing from "In Progress" to "Done"). This metric is dependent on the workflow of the agile tool and the team's updates, not the automated integration of test/build status from the CI tool.

📚 References
The benefits listed align with the core principles of DevOps and Continuous Integration/Continuous Delivery (CI/CD), which are essential topics for the Development Lifecycle and Deployment Architect exam.

DevOps Principle: Single Source of Truth & Traceability:
Integration ensures that the business requirement (the user story) is linked to the technical artifacts (the code commit) and the quality gates (the build/test results). This achieves full traceability from requirement to production deployment.

Continuous Integration Principle: Rapid Feedback Loop:
CI/CD tools provide instant feedback on code quality and stability. By pushing this feedback (status of tests and builds) directly to the agile tracker, developers get the critical information needed to fix integration issues early and quickly—a pillar of both Agile and DevOps methodologies.

Salesforce Developers, DevOps Guide:
Relevant Concept: Using tools like Salesforce DevOps Center (which connects a work item tracker to Git and a CI/CD pipeline) explicitly promotes this benefit by centralizing the flow of quality and status data onto the work item record.

Explanation:

In the Package Development Model, Salesforce supports source tracking in scratch orgs and some sandbox environments. This allows automatic detection of metadata changes made via the CLI or Setup UI. However, not all metadata types are supported by source tracking.

C. Changes to components not yet supported by source tracking ✅
Certain metadata types (e.g., CMS content, some standard objects, or legacy features) may not be tracked automatically.
Architects must manually track these changes to ensure they are included in the package or deployment artifacts.

❌ A. No manual tracking required. All changes are automatically tracked
Incorrect. Not all metadata types are supported by source tracking, so manual tracking is still necessary in some cases.

❌ B. All metadata changes for the release
Overly broad. Only unsupported metadata types require manual tracking, not all changes.

❌ D. Only the changes made via the Setup UI
Misleading. Changes via Setup UI can be tracked if the metadata type is supported. The key factor is metadata support, not the interface used.

Reference:
Salesforce Source Tracking Metadata Coverage

Explanation:

When there are many backlog items and stakeholders can’t agree on priority, the architect shouldn’t arbitrarily decide what’s most important. Instead, they should:

Bring stakeholders together.
Define clear, objective prioritization criteria (e.g., business value, regulatory impact, risk reduction, customer impact, effort).
Help them design a prioritization model or framework (e.g., weighted scoring, WSJF, MoSCoW) that everyone agrees to use.
Use that model to rank backlog items going forward.
This creates transparency, shared ownership, and repeatable decision-making.

Why not the others?

B. Organize a sprint planning meeting with the Scrum team.

Why it sounds tempting:
Sprint Planning is where teams pick work from the backlog, so it might feel like a place to “sort out” what gets done.

Why it’s not the right answer here:
Sprint Planning assumes the backlog is already prioritized.
In Scrum, the Product Owner (or similar role) brings a prioritized backlog to Sprint Planning. The team doesn’t argue business priority there; they estimate and select what they can commit to.
The core problem isn’t planning; it’s stakeholder conflict.
The issue in the question is that stakeholders can’t agree on priority, not that the team doesn’t know how to plan. Sprint Planning does not solve disagreement between stakeholders; it just consumes whatever prioritization already exists.
You’d just move the conflict into another meeting.
Without an agreed prioritization model, Sprint Planning will become a battlefield of opinions instead of a planning session.
So B is more about execution of already-prioritized work, not resolving how to prioritize.

C. Take over prioritization for the stakeholders.

Why it sounds tempting:
As an architect, you understand the system, dependencies, and risks. It might feel “efficient” to just decide.

Why it’s a bad idea:
Prioritization is a business decision, not a technical one.
Architects are responsible for technical integrity, scalability, and alignment with architecture principles. But what delivers the most business value should be decided by business stakeholders (or a Product Owner).
You’ll create political problems and resentment.
If the architect overrides stakeholders, you risk:
Alienating key business sponsors.
Being blamed if priorities are later considered “wrong”.
Undermining the governance model of the project.
You remove ownership from the people who should own it.
Stakeholders must be accountable for what gets delivered and when. Taking that away doesn’t solve the underlying misalignment; it just hides it.
So C is anti-pattern: the architect becomes a “shadow Product Owner,” which is not their role.

D. Allow the delivery teams to pick the best work for the business.

Why it sounds nice:
Empowered teams, autonomy, “the devs know what’s best” — this language feels agile-ish.

Why it’s not appropriate:
Developers don’t own business value.
Delivery teams are experts in how to implement, not what is most valuable to the business. They typically don’t have the full commercial, regulatory, or strategic context.
It bypasses stakeholder accountability altogether.
The fundamental problem is conflicting priorities among stakeholders. If you say “let the team decide,” you:
Ignore stakeholder input.
Potentially deliver what’s easiest/most fun technically, not what’s highest business impact.
Create misalignment and frustration when stakeholders see their requests deprioritized without their involvement.
Agile ≠ devs pick whatever they like.
In Agile, teams are autonomous in how they deliver; what to deliver is still governed by product/business priority (e.g., Product Owner, stakeholder alignment).
So D hands business prioritization to the wrong group and doesn’t resolve the conflict.

Why A is better in contrast

A. Facilitate the design of a prioritization model with the stakeholders.
This directly addresses the root problem:
There are many items.
Stakeholders disagree on what’s most important.
By creating a transparent, objective model (e.g., weighted scoring based on revenue impact, risk reduction, customer satisfaction, regulatory requirements, effort, etc.):
Stakeholders co-create the rules of prioritization.
Disputes become: “How does this score in the model?” instead of “My feature is more important than yours.”
The architect stays in their lane: facilitator and technical advisor, not business owner.