Address Jesper Schulz-Wedde's review on PR #156

- Rename 3 articles so their .good.al/.bad.al companion stems match
  (do-not-change-primary-key, testfield-required-setup-field,
  al-identifiers-english), fixing the R14 orphan-sample errors.
- do-not-change-primary-key.good.al: include Flow in the new table's
  own primary key so it actually models the discriminating dimension.
- al-build-output-must-not-pollute-project-root.md: drop the
  unsubstantiated AL0197 causal claim and the non-existent
  al.outputPath setting; reframe as build-artifact hygiene sourced
  from ALTool --outfolder / al_build outputPath.
- prefer-email-module.md: Email Message is Codeunit 8904, not a table;
  distinguish it from the underlying Sent/Outbox/Draft storage.
- file-datatype-saas.md: File.Open/Create/Read/Write fails to compile
  against a Cloud-scoped project, it does not compile and silently
  fail at runtime.
- namespace-must-be-verified-from-source.md: narrow to "resolve from
  the referenced object's source or symbols," since source-file line
  one is not the only authoritative source (symbol packages, comments
  before the namespace line).
- test-data-must-be-random-and-complete.md: drop "assume an empty
  database" and "collision-free" absolutes; reframe around
  independence from unrelated business records and reserving explicit
  values for scenario-defining inputs.
- binary-choice-must-be-boolean.md: scope to genuine true/false
  semantics, not mechanical two-member-enum-to-boolean conversion.
- document-report-word-layout.md: scope down to a sourced Microsoft
  Learn recommendation instead of an unconditional performance
  guarantee; cite the three Learn pages.
- Wire the new articles into their review skills' candidate-selection
  signals (file-datatype-saas, prefer-email-module,
  namespace-must-be-verified-from-source, var-parameters-require-an-
  addressable-variable) so they can actually enter a worklist.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

microsoft/knowledge/testing/test-data-must-be-random-and-complete.md

@@ -13,11 +13,13 @@ application-area: [all]
 
 ## Description
 
-An AL test suite should assume an empty database. Test data must be created programmatically inside the test rather than assuming a specific code, number, or name already exists in the environment — a hardcoded lookup against an assumed-existing record makes the test fail for reasons unrelated to the code under test. Every mandatory field on a created record also needs a value that respects its declared length; a partial setup that merely passes validation is not sufficient.
+A BC test company normally contains initialized system/setup data — an AL test suite should not assume an empty database, but it must be independent of unrelated business records: create the records and setup it owns rather than looking up a specific code, number, or name assumed to already exist, since that makes the test fail for reasons unrelated to the code under test. Every mandatory field on a created record also needs a value that respects its declared length; a partial setup that merely passes validation is not sufficient.
+
+Not every value should be generated, though. Incidental fixture data — identifiers, names, descriptions — should generally come from the standard library codeunits rather than be tied to specific existing data. But values that materially define the scenario under test — amounts, quantities, percentages, dates, thresholds, rounding precision — should stay explicit and deliberately chosen, not randomized: a rounding test needs values placed deliberately around the rounding boundary, not a random one that might miss it entirely.
 
 ## Best Practice
 
-Use the standard library codeunits (`Library - ERM`, `Library - Inventory`, `Library - Sales`, `Library - Utility`) to create records with collision-free random values, and fill every mandatory field with randomized, correctly-sized data. Reserve hardcoded values for tests that validate an external contract itself — a fixed JSON schema, an EDIFACT message, a counterparty code — where the hardcoded value documents the specification rather than arbitrary test logic.
+Use the standard library codeunits (`Library - ERM`, `Library - Inventory`, `Library - Sales`, `Library - Utility`) to generate incidental fixture values — they produce valid, unique-enough data via number series and controlled randomness, not a mathematical collision-free guarantee — and fill every mandatory field with correctly-sized data. Keep values that define the scenario's expected outcome explicit and fixed. Reserve hardcoded values for tests that validate an external contract itself — a fixed JSON schema, an EDIFACT message, a counterparty code — where the hardcoded value documents the specification rather than arbitrary test logic.
 
 See sample: `test-data-must-be-random-and-complete.good.al`.