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

Explanation:

A. The production org will be locked. Administrators cannot modify metadata during this time
When you validate or deploy a change set to production, Salesforce places a lock on metadata in the target org. This means:
No other deployments can run.
Admins cannot modify setup/metadata (e.g., no editing fields, workflows, page layouts, etc.).
This prevents conflicting configuration changes while the deployment/validation is in progress.

D. The production org will be locked. Users will still be able to Read/Write data to the org
Even though metadata is locked, business users can continue to work normally:
They can still create, edit, and delete records.
Normal day-to-day operations are not blocked.
So: metadata is locked; data is not.

Incorrect options
B. The sandbox org will be locked. Administrators cannot modify metadata
Only the target org (production, in this case) gets the metadata lock. The source sandbox (Developer Sandbox) is not locked by the deployment.

C. The production org will be locked. Users can only Read data during this time
This is too restrictive. During deployment/validation to production, users can both read and write data; they are not limited to read-only access.

Explanation:

UC’s managed package is trying to use Apex code to customize and create layouts in the subscriber (target) org. That kind of behavior (changing page layouts / metadata at runtime from Apex) relies on the Apex Metadata API.
Salesforce imposes a key restriction here:

In subscriber orgs, Apex that modifies metadata (like layouts) is only allowed when it runs from a certified managed package that has passed Salesforce Security Review.
Before the package is certified, the Apex Metadata operations will fail in the target org, even though the same code may work in the developer org or packaging org.

So during testing in the target org before security review, the Apex classes can’t access or change the org’s metadata — exactly what you’re seeing.
Now, why the other options are incorrect:

A. “Apex Settings to allow the access to metadata is not switched on.”
There is no generic “Apex Settings” toggle that enables metadata access like this. Metadata access from Apex is controlled by platform restrictions (e.g., certified managed package), not a simple org setting.

B. “UC needs to turn on Apex Settings within the custom metadata type.”
Custom Metadata Types are for storing configuration, not enabling metadata access to layouts. Nothing inside a custom metadata type can “unlock” metadata manipulation by Apex.

C. “The solution is flawed. UC should utilize the Tooling API from a web service call to modify the layouts.”
While you can use Metadata/Tooling APIs from an external integration, the question context is clearly about using Apex within a managed package (Apex Metadata API pattern). That approach is valid as long as the managed package is certified. So the solution itself isn’t inherently flawed — the issue is that the package isn’t security-reviewed yet.

Because the package has not yet passed security review, the Apex in the managed package cannot modify metadata (layouts) in the target org, which is why it fails during testing. Hence, D is the correct answer.

Explanation:

B. Execute a batch job that deletes all records created on the day.
A Partial Copy Sandbox is a valuable environment because it contains a copy of production's metadata and a sample of production's data (defined by a template), making it ideal for integration or UAT.
Daily Deletion: Since the requirement is to clear only 1GB of temporary test data created that day, the most efficient and practical solution is to delete that data programmatically.
Batch Apex: Using a Scheduled Batch Apex Job to query records created within the last 24 hours (WHERE CreatedDate >= YESTERDAY) and delete them in bulk is the standard, efficient, and scalable way to handle large-volume data deletion on the Salesforce platform, especially for cleaning up environments.

❌ Incorrect Answers and Explanations
A. Manually delete all records individually.
Manually deleting 1GB of data is impossible to do reliably every night. It is not scalable, leads to human error, and is an administrative nightmare, completely failing the need for an automated solution.

C. Create a new developer copy sandbox every night.
While a Developer Sandbox refreshes daily (Source 2.2), creating a new sandbox every night is not the best strategy for a Partial Copy Sandbox requirement.
Incorrect Sandbox Type: The Partial Copy sandbox is used because it contains sampled production data. Switching to a Developer Sandbox would mean losing this required sampled data, as Developer Sandboxes only copy metadata.
Licensing/Management: Creating new sandboxes daily is messy for licensing, environment management, and connecting external systems (which break every time the org ID changes).

D. Refresh the sandbox every night.
This is the wrong approach because of refresh limits:
Partial Copy Sandboxes can only be refreshed once every 5 days (Source 2.2), not nightly.
Even if the limit allowed it, a sandbox refresh is a long-running process that can take hours or even days, resulting in unacceptable downtime and significant delay for the next day's testing (Source 2.7).

References
This architectural decision prioritizes efficiency and adhering to platform limits.

Salesforce Sandbox Refresh Limits:
Partial Copy Sandboxes: Refresh interval is 5 days. This rules out a nightly refresh (Source 2.2).

Salesforce Large Data Volumes Best Practices:
Data Deletion: For bulk data operations, including deletion, Batch Apex or the Bulk API (often used by Batch Apex internally) are the recommended tools to ensure governor limits are not hit and the operation is completed efficiently (Source 1.5). This makes automated deletion the correct choice for clearing the daily test data.

Explanation:

The fact that tests pass in the sandbox but fail in production is almost always a sign that the test methods are depending on existing org data rather than generating their own independent test data.
Production data is often very different from sandbox data, especially if the sandbox was refreshed long ago. Tests that rely on org data (even accidentally) can behave differently when deployed.

Why D is correct
Salesforce’s best practice is:
Never rely on existing org data in test classes.
Never use SeeAllData=true unless absolutely necessary (e.g., testing Reports, Pricebooks, or Standard Objects with immutable data).
Always generate fresh test data within the test method or use test utility classes.
This ensures the tests behave the same way in all environments (dev sandbox, test sandbox, production).

Why the other options are incorrect
A. Create test data in production before deploying the test classes
You should never modify production data just to make tests pass.
Test classes should be self-contained and autonomous.

B. Set SeeAllData to True to use the data in production
This makes tests fragile, dependent on unpredictable org data.
Salesforce advises against using SeeAllData=True except in rare edge cases.

C. Explicitly set SeeAllData to True and generate data in test methods
Contradictory:
If you’re generating data, you don’t need SeeAllData=True.
Again, it violates best practices and creates unstable tests.

Conclusion
The architect should recommend D: Do not use SeeAllData and generate all necessary test data within the test methods to ensure tests run consistently in any environment, including production.

Explanation:

Why A is the only correct recommendation
The new business unit presents two decisive characteristics that make any form of forced integration or shared-org approach completely unnecessary and counterproductive. First, it does not share customers with any of the existing four BUs or with the global Customer 360 org, eliminating the single biggest reason for real-time read/write synchronization. Second, it explicitly requires maximum flexibility in its own business processes, meaning it needs the freedom to evolve objects, record types, validation rules, approval workflows, pricing logic, page layouts, and release cadence independently without stepping on the toes of the other units. A brand-new, completely stand-alone org gives exactly that freedom while introducing zero integration cost, zero data-duplication risk, zero performance impact on the existing ecosystem, and zero governance overhead from having to coordinate releases or metadata changes with the other five orgs.

Why B is incorrect
Deploying the new BU inside the existing Customer 360 org would instantly destroy the required process flexibility. The 360 org is already a highly governed, consolidated environment that enforces a unified data model and global standards. Forcing the new BU into it would mean living with the same constrained objects, shared profiles, locked-down release windows, and regression-testing burden as the rest of the enterprise—exactly the opposite of what the new BU needs.

Why C is incorrect
Re-using the org of another existing BU (even one in the same geography) makes no sense when customers are not shared. The new BU would inherit a data model, process automation, custom settings, and release cadence that were built for a completely different business unit. This would create artificial constraints and future technical debt with zero benefit.

Why D is incorrect
Building yet another real-time integration to the Customer 360 org is expensive, fragile, and entirely unnecessary. The existing four BUs integrate because they actively share the same customers and need a unified 360 view. Since the new BU has no overlapping customers at all, adding integration complexity, licensing costs (MuleSoft/Heroku Connect), latency, error handling, and monitoring would be a classic case of over-engineering with negative ROI.

References
Salesforce Well-Architected Framework – Multi-Org Strategy explicitly states that a new, fully independent org is the recommended pattern when the business unit has distinct processes and does not share customer records with other units. Trailhead’s “Salesforce Multi-Org Strategy” module uses almost this exact scenario as the clearest justification for a stand-alone org.

Bottom Lines
Memorize the trigger phrase: “new BU + does NOT share customers + needs flexibility” → always brand-new isolated org (A). Whenever the question emphasizes zero customer overlap, never pick an integration-heavy option (D) or force-fit into an existing org (B or C). This pattern is one of the most frequently tested org-strategy questions on the real Development Lifecycle and Deployment Architect exam.

Explanation:

In Salesforce, test methods are not allowed to perform real HTTP callouts. If a test method attempts to make a callout without using Test.setMock(), the platform will throw a runtime exception with a message like:
"Methods defined as TestMethod do not support Web service callouts"
This is a strict enforcement to ensure that tests are isolated, repeatable, and do not depend on external systems.

🔧 Correct Testing Pattern for Callouts
To properly test callouts in Apex:
Create a class that implements HttpCalloutMock
Use Test.setMock(HttpCalloutMock.class, new YourMockClass())
Wrap the callout logic between Test.startTest() and Test.stopTest()
Assert the response

❌ Why the Other Options Are Incorrect
❌ A. The test class fails without error message since the test class will simply skip the webservice callout during the execution
Why it's wrong: Salesforce does not silently skip callouts in test context. Instead, it throws a clear and explicit error if a test method tries to perform a real HTTP callout without a mock.
What actually happens: The test fails with a message like:
"Methods defined as TestMethod do not support Web service callouts"

❌ C. The test class would make the web service callout and may or may not fail depending on the circumstances on the web service end
Why it's wrong: Salesforce completely blocks real HTTP callouts from test methods unless a mock is defined using Test.setMock().
What actually happens: The platform prevents the callout from even being attempted, regardless of the external service's availability or behavior.

❌ D. It is impossible to miss the Test.setMock() statement, the Developer Console will not let the developer save it since the test method callout
Why it's wrong: The Developer Console allows saving and compiling test classes even if Test.setMock() is missing.
What actually happens: The test class will compile successfully, but it will fail at runtime when the test method executes and hits the callout.

Explanation:

UC wants:
Major releases (every 4 weeks): Dev → SIT → UAT → Training
Minor releases (every week): Dev → UAT

Key points for UAT environment strategy:
Major releases are larger, riskier, and often impact multiple areas, so:
They should be tested in a Full Copy sandbox that:
Mirrors production data volume, complexity, and integrations
Gives the most realistic conditions for end-to-end UAT

Minor releases are smaller changes (bug fixes, small enhancements), so:
They can usually be validated in a Partial Copy sandbox, which:
Has a representative subset of data
Is cheaper and faster to refresh
Still sufficient for validating smaller, low-risk changes

Parallel workstreams
Since major and minor releases are running on different cadences (4-week vs weekly), you often need separate environments so that:
Major release UAT can test future-state changes in a Full Copy
Minor release UAT can test current or near-current production fixes in a Partial Copy
They don’t block each other or constantly overwrite each other’s changes.

Why the other options are not ideal
❌ B. Minor and Major releases use separate Developer Pro
Developer Pro sandboxes are not suited for UAT:
Limited data volume
Not representative of production data or performance
They’re better for dev/config, not business-user UAT.

❌ C. Minor releases use Developer and Major releases Full copy
Even worse than B for UAT: a plain Developer sandbox is very restricted and not appropriate for realistic UAT by end users.

❌ D. Minor and Major releases use the same Full copy
Using the same UAT environment for both:
Causes collision of timelines (major-next-version code vs minor-current-version fixes)
Makes it difficult or impossible to test a minor fix against the current prod state while a major release is mid-cycle in UAT.

So, the valid UAT environment strategy is:
A: Minor releases use a Partial Copy and Major releases use a Full Copy sandbox.

Explanation:

This question tests the understanding of matching the type and urgency of a change to the appropriate release cadence within a defined governance model. The key is to balance speed with control.

Why D is Correct:
This is the correct answer because it adheres to proper governance while satisfying the urgency.
Urgent & Simple: The change is a "new report" for an "urgent client request." This classifies as a simple, high-priority change. It is not a complex code change.

Daily Release Cadence: The purpose of a "daily" release process is precisely for handling urgent, low-risk changes like reports, dashboards, minor permission tweaks, or new list views. It allows for a fast turnaround outside of the slower, more rigid minor and major release cycles.

Proper Path to Production: The recommendation correctly states to create the report in a full sandbox first. A full sandbox contains a copy of production data, which is essential for building and validating a report to ensure it runs correctly and performs well with real data volumes. After validation, it is then deployed to production via the daily release pipeline. This maintains the integrity of the development lifecycle without introducing unnecessary delay.

Why A and B are Incorrect:
Both of these options recommend bypassing the full sandbox and creating the report directly in production. This is a major violation of sound release management principles. It bypasses any testing with production data, carries a high risk of performance issues or errors, and undermines the controlled deployment process. The "major" and "minor" cycles are also too slow for an "urgent" request.

Why C is Incorrect:
While this option correctly uses the full sandbox for development, it incorrectly assigns the change to the "major release process." A major (quarterly) release is for large, complex, strategic changes that require extensive testing and coordination. Using it for a single, urgent report would be a massive inefficiency and would defeat the purpose of having a daily release cadence for exactly this type of scenario.

Key Takeaway:
A mature release management strategy uses different cadences for different types of changes. The architect must recommend the path that is fastest while still maintaining quality and control. For a simple, urgent change like a report, the "daily" process is the correct vehicle, and it must still follow the path of development -> testing (in a representative sandbox) -> deployment.

Explanation:

Why C is correct
With 10 concurrent Apex developers working on both new features and bug fixes, the only safe and scalable way to prevent one developer from silently overwriting or breaking another’s work is to enforce feature branching (also known as Git Flow or trunk-based with short-lived branches). Each developer works in their own isolated branch (named after the user story or bug ticket), commits freely, and only merges into a common integration branch (usually “develop” or “main”) after code review and successful CI build. The integration branch is continuously built and tested in a shared sandbox, catching conflicts and regressions early while keeping individual work safe from interference.

Why A is incorrect
Fixing bugs directly in HEAD (main/develop) while allowing feature work in branches creates a dangerous asymmetry. Bug fixes land immediately in the integration stream without isolation, dramatically increasing the risk of merge conflicts, broken builds, and half-finished hotfixes blocking everyone else. This is a known anti-pattern that large teams quickly abandon.

Why B is incorrect
Having all 10 developers commit directly to the same branch without isolation is the fastest way to create constant merge hell, overwritten changes, and broken builds. Even with continuous testing, the overhead of resolving conflicts multiple times per day and the risk of someone pushing breaking code directly to the shared branch makes this completely unmanageable at this team size.

Why D is incorrect
Salesforce has no built-in conflict detection for Apex, Visualforce, Lightning components, or most metadata when multiple developers work in the same org or sandbox. Conflicts are only detected at deploy time (or destructively overwritten if using org-based development). Relying on this is a guaranteed recipe for lost work and production failures.

References
Salesforce Well-Architected Framework → Source Control & Branching
“For teams larger than 3–5 developers, use feature branches merged into a common integration branch via pull requests.”
Trailhead → “Source Control Best Practices”
Explicitly recommends separate branches per work item and a protected main/develop branch.
Salesforce DevOps Center documentation
Defaults to the exact pattern in option C.

Bonus Tips
Memorize: 10+ concurrent developers → always separate branches + merge to common integration branch (C).
Any option that says “work in the same branch” or “don’t use source control” is instantly wrong.
Bug fixes in HEAD (A) is a trap answer that shows up often — never pick it.
This exact scenario is one of the most frequently tested branching-strategy questions on the real Development Lifecycle and Deployment Architect exam.

Explanation:

C. Utilize a source control system to allow separate branches for current development and production bug fixes
This is the core of the Hotfix Branching Strategy.
A critical production fix (hotfix) must be developed and tested in isolation from the ongoing, larger development project.
An architect should recommend creating a dedicated hotfix branch that branches directly from the production code (the main or master branch).
This isolation ensures that the fix is quick and clean, containing only the necessary changes, preventing the large, in-progress feature code from accidentally being deployed along with the fix.

D. Refresh a sandbox for replication of the issue and testing the use-case scenarios once the code is fixed
To accurately diagnose, fix, and ensure the critical issue is resolved, the team must replicate the problem in an environment that closely mirrors production.
The best practice is to refresh a dedicated hotfix/bug-fix sandbox (typically a Developer Pro or Partial Copy) from Production's current state. This provides the exact configuration, security settings, and, in the case of a Partial Copy, the necessary data volume to reproduce the issue.
The fix is developed and tested in this fresh environment, minimizing the risk of the fix introducing new regressions.

❌ Analysis of Incorrect Options
A. Create separate branches for current development and production bug fixes and deploy the fix with current development when ready
The first part (separate branches) is correct (C), but the second part ("deploy the fix with current development") is a major anti-pattern. Critical production fixes cannot wait until the large, ongoing feature development is "ready." This would lead to unacceptable downtime and severity, defeating the purpose of a hotfix.

B. Utilize one branch for both development and production bug fixes to avoid out-of-sync branches and simplify deployment
This is highly risky. Using one branch for both the large, unstable development project and the critical fix would violate the principle of isolation. The fix would be dependent on the readiness of the entire new development, leading to potential delays and introducing instability into the production fix.