COBOL Scenario-Based Questions 2025

This article concerns real-time and knowledgeable COBOL Scenario-Based Questions 2025. It is drafted with the interview theme in mind to provide maximum support for your interview. Go through these COBOL Scenario-Based Questions 2025 to the end, as all scenarios have their importance and learning potential.

To check out other Scenarios Based Questions:- Click Here.

Disclaimer:
These solutions are based on my experience and best effort. Actual results may vary depending on your setup. Codes may need some tweaking.

1) Your nightly batch overran because a COBOL job processed double the records. How do you stabilize it without rewriting everything?

First, sample the input deltas to confirm it’s a volume spike, not a logic loop.
Add a lightweight pre-filter pass to drop obviously invalid or duplicate records early.
Convert high-cost IF chains into table-driven lookups using OCCURS and SEARCH for predictable cost.
Move expensive edits from record-level to group-level (e.g., validate codes once per group).
Use multi-file reads only if they’re sequentially aligned; avoid random backtracking on tape/sort.
Push heavy sorts to the sort utility with CHKPOINT; feed COBOL with already grouped data.
Add a restart marker and micro-checkpoints so re-runs pick up mid-stream.
Measure again and lock a max-record throttle for emergency windows.

2) A COBOL program misreads negative amounts after a data migration. What’s your first diagnostic path?

Confirm COMP-3 (packed) vs DISPLAY and sign nibble expectations in the copybook.
Check code page/EBCDIC variant on both sides; mismatched translations flip signs subtly.
Compare one record hex-dump pre-/post-migration to pinpoint nibble or byte order issues.
Verify compiler options (TRUNC/ARITH) that change numeric handling at runtime.
Validate field alignment after REDEFINES; migrations often shift group item boundaries.
Ask for the exact source ledger rule for sign handling (credit/debit).
Build a tiny harness to parse just one field and print bytes for proof.
Fix with a minimal mapping layer—don’t change all downstream fields at once.

3) You’re called into a CICS outage: a COBOL transaction is looping. How do you make it observable fast?

Add a defensible loop counter and EXIT path; keep it behind a feature flag.
Log only key identifiers to SMF/CICS journal—avoid payload spam during incident.
Wrap READs/WRITEQs with simple timers and emit “hotspot” tags on slow calls.
Use COMMAREA size guardrails and verify that stale TSQ/TDQ reads aren’t recycling.
Turn on a safe level of LE traceback for abends, not full verbose.
Prove or rule out storage overlay with eye-catchers around working-storage blocks.
Capture two short transaction samples, not a million—enough to reproduce pattern.
Ship a rollback that limits concurrency for this trans ID until fixed.

4) A regulator asks to reconstruct year-old outputs. Your COBOL job was updated three times since then. Now what?

Rehydrate the exact copybooks, compiler options, and sort parms from that date.
Use source control tags to rebuild the old load module reproducibly.
If data format evolved, write a small adapter to “down-convert” to the old layout.
Rerun on a sandbox LPAR with frozen JCL streams and dated utilities.
Prove parity by spot-matching control totals and hash samples.
Document every delta and sign-off path for audit trails.
Lock the reconstructed artifacts as immutable evidence.
Create a permanent “regeneration recipe” doc for next time.

5) Finance complains totals don’t tie after a weekend run. Where do you anchor reconciliation?

Start with independent control totals (record counts, amounts) at each stage.
Recalculate derived fields outside the program to cross-check arithmetic.
Verify sort sequences and duplicates—missing last-duplicate logic skews totals.
Inspect truncation rules (TRUNC/ARITH) against field definitions.
Segment by business key (e.g., branch) to localize the variance.
Compare a clean subset through the whole chain to isolate the step.
Confirm upstream feed cutoff times didn’t drift.
Patch with a guard job that enforces balanced groups pre-post run.

6) You inherit a 20K-line COBOL with tangled IFs. How do you reduce risk before touching logic?

Fence off behavior by pinning golden test files and expected totals.
Add paragraph-level counters and simple timing to see hot paths.
Extract constant decision tables into OCCURS arrays without changing outputs.
Wrap external I/O with stubs to run logic in memory for fast iteration.
Add assertions on field ranges right after reads to catch bad data early.
Document three end-to-end story cases as living specs with business.
Move common edit patterns to a single section; no functional change yet.
Only then, refactor smallest safe slice first and re-diff outputs.

7) A VSAM KSDS is showing growing CI splits. What’s your pragmatic fix under time pressure?

Reorg/redefine with a better FREESPACE tuned to insert patterns.
Consider alternate index for hot query paths; reduce random probes.
Batch “insert-heavy” periods into sort-then-load windows instead of ad-hoc inserts.
Pad keys if sequential keys cause hotspots; spread the insert load.
Monitor split rate per day; set a reorg threshold in operations runbook.
For near-term, throttle insert concurrency on peak windows.
Archive cold partitions to shrink active set size.
Document key design trade-offs for the next data model review.

8) A new downstream wants JSON, but your system is COBOL + VSAM. How do you bridge without rewriting core?

Define a stable copybook-to-JSON contract and freeze it.
Build a small adapter that maps COBOL groups to JSON fields one-for-one.
Keep conversions stateless; stream records to avoid memory blow-ups.
Escape/encode with a tested library; avoid homegrown JSON builders.
Version the schema and expose a deprecation window for consumers.
Log schema-mismatch events with the offending key for triage.
Run dual outputs (legacy + JSON) for one cycle to prove parity.
Document limits (e.g., max field length, character set) upfront.

9) You see frequent S0C7 data exceptions in one program. What’s your containment plan?

Add a defensive numeric check before using COMP-3/COMP fields.
Print the raw offending bytes once per run to pinpoint the field.
Validate copybook alignment—REDEFINES often hide shifts.
Enforce upstream edits where the bad data originates.
Use ARITH(EXTEND) if precision loss is suspected.
Redirect bad records to a quarantine file with counts.
Keep business flow alive; don’t hard-fail unless mandated.
Close with a permanent contract test for that field.

10) A sort step costs 70% of the batch window. What knobs do you try first?

Push sort into the utility with explicit key and eliminate in-program sorts.
Pre-filter and pre-aggregate to reduce sort volume.
Use blocked I/O and bigger buffers within site standards.
Split by natural partitions (e.g., region) and merge results.
Ensure key order matches downstream read pattern to avoid re-sort.
Cap record length by dropping unused trailing fields.
Validate that duplicate handling runs in the sort itself.
Track gains per change; lock what works.

11) Two COBOL jobs fight over the same VSAM file overnight. How do you de-conflict?

Map exact time windows and file modes (read/update) for both.
If both must write, introduce a handshake dataset or ENQ discipline.
Consider snapshot-then-merge: one job reads a copy, the other writes.
If business allows, sequence them and compress gaps with tuning.
Add file-level retry with backoff instead of aggressive abends.
Create an operations calendar entry to prevent ad-hoc overlaps.
Long term, split by key range or move to a shared database.
Document owner of the “truth” dataset clearly.

12) Ops wants faster reruns after mid-stream abends. What do you add without a rewrite?

Write last successful checkpoint key to a lightweight control file.
Make processing idempotent past that marker (no double-posting).
Keep small recovery windows—every N thousand records.
Separate irreversible actions (payments) behind a confirm step.
Include a diagnostic snapshot near checkpoint for support.
Add a “replay from marker” JCL parm with validation.
Test both normal flow and replay flow before go-live.
Document the operator playbook clearly.

13) Your COBOL app reads multiple input feeds with slightly different layouts. How do you stay sane?

Normalize early: map all variants to one internal copybook.
Keep a per-feed validator that checks mandatory fields and ranges.
Version feeds explicitly; reject unknown versions loudly.
Log source feed ID with every error to speed triage.
Avoid sprinkling IF source-specific checks everywhere—centralize them.
Provide a small “contract test” file to each provider.
Fail just the bad record, not the whole job, where allowed.
Align SLAs so late feeds don’t block all processing.

14) A small COBOL change suddenly spiked CPU. What’s your rollback-friendly analysis?

Compare compiler listing before/after for OPT and ARITH settings.
Diff hot paragraphs via timing counters; locate the hotspot.
Look for changed data access patterns that broke sequential reads.
Check new STRING/UNSTRING loops for hidden O(n²) behavior.
Revert the one change behind a switch, measure again.
If needed, isolate heavy logic to a table lookup.
Keep a one-page “change impact” note for CAB.
Lock restored settings in build scripts.

15) Business wants same-day fixes, but change risk is high. How do you shield production?

Maintain a golden regression dataset with known outputs.
Enforce two-person review for copybooks and interfaces.
Ship feature toggles to disable new logic quickly.
Keep roll-backable load modules and JCL variants.
Add synthetic “canary” runs on a tiny sample before full run.
Publish a short risk statement with every change.
Timebox emergency changes to minimal scope.
Track post-release metrics for 24 hours.

16) You must integrate COBOL with an API gateway. Where do you draw the boundary?

Keep COBOL doing batch/business logic and data shaping.
Put protocol handling (HTTP/JSON/TLS) in the gateway or adapter.
Use simple flat contracts between COBOL and the adapter.
Ensure idempotency with request IDs to handle retries.
Throttle inbound calls so mainframe peaks stay stable.
Log correlation IDs end-to-end for traceability.
Version the contract; never break clients silently.
Pilot with read-only flows before writes.

17) A long-running job blows region storage intermittently. How do you tame it?

Review large tables in WORKING-STORAGE; move to files if rarely used.
Reuse buffers; avoid growing temp strings inside loops.
Validate LE runtime options for heap/stack limits.
Break job into phases with commit points.
Watch for REDEFINES overlays that corrupt memory.
Add a “memory watermark” log to see growth points.
Kill verbose trace in production; it eats memory.
Re-measure after each change; keep the smallest stable set.

18) You need to retire a COBOL module no one understands. How do you prove it’s safe?

Trace inputs/outputs for a full cycle to confirm consumers.
Build a shadow run that produces the same outputs and compare.
Ask business to sign off on “no consumer” results for two cycles.
Stage removal behind a switch; keep the load module available.
Archive last outputs and code for compliance retrieval.
Create a rollback plan in JCL with old flow ready.
Communicate sunset dates widely.
Only delete once metrics stay green.

19) Duplicate customers sneak in despite a match routine. What’s your incremental fix?

Add a phonetic or normalized key layer (name, address components).
Use weighted matching: not all fields equal; tune thresholds.
Keep a manual review lane for near-matches with low volume.
Feed confirmed merges back to training data for future passes.
Timestamp merges and keep lineage for reversals.
Don’t block the whole batch—quarantine edge records.
Publish match accuracy metrics to stakeholders.
Revisit quarterly as patterns shift.

20) A copybook is reused across five apps and keeps breaking. What governance works?

Version copybooks with semantic tags and changelogs.
Validate downstream builds against new versions automatically.
Announce deprecations with a firm grace period.
Keep compatibility shims for one cycle, max.
Nominate an owner; no “everyone edits” chaos.
Add a contract test per consumer.
Freeze last-known-good in production until all upgrade.
Audit references yearly to retire dead ones.

21) You’re asked to halve batch time but hardware is fixed. Where do you look first?

Offload sort/merge to utilities with tuned parms.
Partition by natural keys and run parallel where safe.
Reduce record length and drop unused fields early.
Convert repeated lookups to in-memory tables.
Stage heavy validations into pre-runs outside peak.
Cache stable reference data between steps.
Add checkpointing to avoid restart costs.
Reorder steps so slow I/O starts earlier.

22) A decimal rounding dispute hits monthly statements. How do you settle it?

Document the rounding rule (banker’s vs half-up) with finance.
Enforce a single rounding spot—don’t round multiple times.
Use ARITH/COMP settings that match that rule.
Show a side-by-side before/after on three real cases.
Backfill a few months if mandated; automate it.
Freeze the rule in a shared spec and copybook.
Add a unit test that locks the behavior.
Communicate change windows to call centers.

23) Security flags a plaintext field in a file your COBOL job writes. What’s your fix?

Classify the field (PII, PCI) with risk owners.
Tokenize or encrypt at write time using approved routines.
Keep keys and crypto outside COBOL code paths.
Log only masked values in diagnostics.
Add a data retention limit and purge schedule.
Confirm downstream readers can handle masked data.
Run a one-time remediation for historical files.
Update the data handling standard for this flow.

24) A partner feed started sending multi-byte characters. Your job mis-parses names. Then what?

Detect encoding upfront and normalize to the expected code page.
Validate field length in bytes vs characters—don’t truncate mid-char.
Keep a safe fallback for unconvertible characters.
Quarantine bad lines with keys for follow-up.
Update copybook comments with encoding expectations.
Test with names containing accents and scripts beyond Latin.
Align with print/output teams on font support.
Monitor error rate post-fix for a week.

25) The same business rule appears in six COBOL programs. How do you centralize without chaos?

Move the rule into a callable module with a tight interface.
Keep input/output structures in a shared copybook.
Version the module and publish test vectors.
Replace callers gradually behind feature flags.
Measure performance; avoid extra I/O inside the module.
Provide a stub for unit testing callers.
Deprecate the old in-line logic with a removal date.
Document ownership and change control.

26) Month-end batch collides with ad-hoc analytics and slows to a crawl. What do you enforce?

Reserve a protected window for critical jobs.
Queue ad-hoc runs through a scheduler with priorities.
Cache reference data so analytics don’t hammer VSAM/DB.
Take consistent snapshots for readers during peak.
Publish a calendar and SLOs; escalate violations.
Add lightweight throttling on shared datasets.
Propose a read replica for heavy consumers.
Review demand quarterly and adjust windows.

27) A COBOL program silently drops trailing spaces, breaking a downstream matcher. How to fix predictably?

Clarify whether trim is allowed per data contract.
Preserve DISPLAY fields exactly when the contract demands it.
Add an explicit pad/trim step at the boundaries, not everywhere.
Lock field lengths in copybooks with clear comments.
Compare hex dumps before and after to prove behavior.
Share sample records with the consumer for sign-off.
Add a regression check on field lengths.
Avoid accidental TRIM in shared routines.

28) Operations sees random abends only on high volume days. What pattern do you chase?

Correlate error times with I/O peaks and region usage.
Check for counters overflowing (e.g., 32-bit record counts).
Look for boundary cases in OCCURS tables.
Verify sort work space isn’t exhausted.
Audit retry logic that amplifies load under errors.
Stress-test with synthetic volume to reproduce.
Add guardrails (caps) around suspect loops.
Fix the concrete limit; don’t just raise it blindly.

29) You must add a new fee rule without disturbing old reports. What’s the safe path?

Introduce a versioned rule flag tied to an effective date.
Keep both calculations and pick based on the date.
Validate on historical data where both rules should match.
Publish the new rule to reporting teams before activation.
Ensure audit logs show which rule version applied.
Run parallel for one cycle and compare totals.
Cut over with a clear rollback plan.
Retire the old rule only after sign-off.

30) A program reads an “optional” field that’s missing more often now. How do you reduce noise?

Treat the field as nullable and set a business default.
Log the absence once per run with a count, not per record.
Escalate only when the miss rate breaches a threshold.
Confirm the field’s optional status in the contract.
Backfill if required for analytics with a clear marker.
Avoid branching all over—normalize early.
Monitor trend over time for real drift.
Share a monthly quality report with the provider.

31) You need to expose a COBOL batch’s status to a dashboard. How do you do it cleanly?

Emit a tiny status file with start/end times and counts.
Keep a stable schema and one line per step.
Let the dashboard poll or subscribe; don’t bake UI into COBOL.
Add error codes that map to a known catalog.
Timestamp in UTC and document the format.
Avoid giant logs—keep them operational, not narrative.
Test with a simulated failure state.
Store last N runs for trend visibility.

32) Users want “near real-time” updates from a batch system. What’s your compromise?

Identify the 10% of data that needs freshness and split it.
Run micro-batches more frequently for that subset.
Keep the big, heavy processing on its usual cadence.
Publish SLAs so expectations are clear.
Ensure idempotent updates to avoid double-posting.
Use a queue or snapshot for consumers.
Monitor staleness and alert before it hurts.
Reassess if demand grows past a threshold.

33) An aging COBOL app is reliable but hard to change. How do you pace modernization?

Freeze interfaces and document current behavior.
Carve out non-core capabilities into services first.
Add tests around the riskiest rules before refactors.
Replace shared utilities (e.g., formatting) with modern ones.
Keep performance budgets so users don’t feel regressions.
Migrate read-only reporting first to reduce batch load.
Plan dual-run periods with parity checks.
Communicate milestones and “no-go” criteria.

34) A restart caused duplicate postings yesterday. How do you stop repeats?

Introduce idempotency keys for every financial event.
On restart, skip any key already processed.
Log duplicates to a review queue instead of posting.
Batch commits so partial groups are safe to replay.
Validate that downstream also honors the key.
Add alerts if duplicate attempts spike.
Prove the fix with a forced restart test.
Document the operational restart steps.

35) Your team mixes COBOL and Java around the same data. What guardrails matter?

Define one source of truth and write path per entity.
Freeze shared copybook/POJO mappings under version control.
Use contract tests that parse the same sample in both stacks.
Keep character encoding rules identical.
Align rounding and date rules to avoid subtle drift.
Decide conflict resolution rules upfront.
Monitor for schema drift with a nightly check.
Review cross-stack changes together.

36) A small precision change cascaded into reporting noise. How do you prevent such surprises?

Maintain a data contract with precision and scale fields.
Run contract tests on every build with example data.
Alert when field length/scale changes in copybooks.
Communicate changes with impact notes to analytics.
Stage changes behind an “effective date” switch.
Keep old precision during overlap and reconcile.
Update rounding tests accordingly.
Record a post-change variance report.

37) End users complain about “missing transactions,” but logs show success. What do you check?

Time windows—late arrivals may roll into the next cycle.
Filters that drop records silently; log drops with reasons.
Joins/merges that require both sides present.
Dedup logic that hides legit second events.
Output routing—files may be in the wrong location.
Downstream lag—dashboards might be stale.
Sample one case end-to-end with hex dumps.
Write a short FAQ for common “missing” cases.

38) You must onboard a new country with unusual address formats. How do you adapt safely?

Add country-specific validation behind a data-driven table.
Avoid hard-coding patterns in multiple programs.
Keep unknown components in pass-through fields.
Localize sorting/collation rules where needed.
Provide clear error messages to data entry teams.
Pilot with sample sets from that country.
Monitor rejection rates and tune rules.
Document exceptions for service teams.

39) Performance is fine individually, but the end-to-end is slow. Where do you look?

Hand-offs: sort-write-read chains that add latency.
Sequencing: steps that could run in parallel safely.
Data bloat: fields carried but unused downstream.
Duplicate processing across modules.
External waits (DB, MQ) that stack up.
Scheduler gaps between jobs.
Network/file contention windows.
Visualize the critical path and attack top two.

40) A vendor insists on a new field mid-quarter. How do you lower risk?

Add it as optional with a default and version tag.
Keep old consumers unbroken with a compatibility view.
Validate with sample payloads and contract tests.
Communicate a firm activation date.
Run dual outputs showing both formats for one cycle.
Provide a mapping guide to partners.
Monitor early and often post-go-live.
Lock changes until quarter-end.

41) You suspect a silent data drift in an old COBOL job. What early-warning signals help?

Ratio checks (e.g., null rate, average amount) against baselines.
Key coverage—are all branches still showing up?
Top-N categories—did rankings shift unexpectedly?
Duplicate rate and reject counts.
Size of outputs compared to prior cycles.
Simple checksums of critical fields.
Alert thresholds with human review.
Monthly drift report to stakeholders.

42) A junior dev wants to add “quick print” logs everywhere. How do you guide them?

Log at boundaries—inputs, outputs, and key decisions.
Prefer structured, compact logs over prose.
Avoid printing sensitive data; mask where needed.
Rate-limit repeated errors to cut noise.
Tie logs to correlation IDs.
Keep log format stable for parsers.
Review a sample log with them for clarity.
Teach removing debug logs before release.

43) You need to prove a new COBOL rule didn’t change money. How do you give confidence?

Golden dataset with known amounts and edge cases.
Before/after run with side-by-side diffs.
Hash totals and per-key totals to catch tiny shifts.
Business validates three “story” cases live.
Lock results and attach to the CAB record.
Keep a rollback binary ready.
Monitor first cycle with alerts.
Publish a one-page “no change” note.

44) A once-a-year job always breaks because no one remembers it. What’s your fix?

Convert tribal memory into a concise runbook.
Automate prechecks (files present, dates valid).
Add a dry-run mode that validates inputs.
Keep a test copy with last year’s data.
Set calendar reminders and ownership.
Embed meaningful error messages.
Do a rehearsal two weeks prior.
Capture a post-run lessons-learned.

45) An upstream feed occasionally sends test data to prod. How do you protect yourself?

Validate environment tags in each record header.
Reject or quarantine non-prod tags loudly.
Notify the provider automatically with counts.
Keep a manual override only for true emergencies.
Audit trail for every rejection batch.
Share monthly compliance stats.
Escalate repeat violations formally.
Document penalties/SLA impacts.

46) Your COBOL program must produce stable files for external audits. What disciplines help?

Deterministic sort order for outputs.
Fixed precision and rounding documented.
Consistent headers/trailers with control totals.
Immutable archives with checksums.
Versioned layouts in copybooks.
No “debug only” fields leaking into prod.
Reproducible builds with tagged compiler options.
A short audit-ready data dictionary.

47) Two teams argue over who owns a field’s meaning. How do you resolve?

Map the field’s end-to-end usage across systems.
Identify business process owner, not just tech owner.
Write a clear definition and allowed values.
Decide authoritative source of truth.
Deprecate conflicting uses with a plan.
Add contract tests to enforce the definition.
Publish the glossary to all teams.
Review the field at quarterly governance.

48) You find hand-coded currency conversions scattered in COBOL. What’s the safer pattern?

Centralize conversion logic in one module.
Pull rates from a single, approved source.
Timestamp rates and log which rate used.
Cache rates per run to avoid inconsistency.
Round only once at the end per rule.
Provide unit tests with tricky amounts.
Version the rate source interface.
Remove all ad-hoc math from callers.

49) A production hotfix must go tonight. How do you reduce regret?

Minimal viable change—one file, one switch.
Pair review even if late; two sets of eyes.
Golden dataset quick run before deploy.
Rollback plan printed in the ticket.
Extra monitoring for first full run.
Post-fix review within 24 hours.
Document the reason and scope.
Avoid adding “nice-to-have” edits.

50) Your COBOL job writes duplicate header/trailer lines under rare retries. How to harden?

Make header/trailer writing idempotent with flags.
Guard with a single writer step after core processing.
Include run ID so duplicates are obvious and rejectable.
On restart, check presence before writing again.
Add a validator step that asserts exactly one header/trailer.
Quarantine bad outputs automatically.
Prove fix with forced restarts in test.
Keep logs crisp for audit review.

51) Risk team asks for “explainability” of complex COBOL decisions. How do you deliver?

Emit decision code and key inputs per record.
Keep a readable rule table separate from code.
Provide a trace mode on small samples only.
Map each rule to a documented business policy.
Add a glossary for field names and ranges.
Share a one-page example walkthrough.
Version both rules and explanations.
Review with risk quarterly.

52) You must cut storage costs for historical files. What’s safe to change?

Compress archives with approved utilities.
Strip non-essential fields after a retention window.
Partition by time so restores are scoped.
Hash indexes for quick find without full scans.
Encrypt while archiving to consolidate steps.
Document retrieval SLAs clearly.
Test restore speed quarterly.
Delete only after legal sign-off.

53) A vendor wants you to accept variable-length records. How do you avoid surprises?

Define min/max sizes and mandatory fields.
Validate length before parsing any field.
Keep a schema version byte up front.
Quarantine records that fail structure checks.
Provide them with a validator tool or sample JCL.
Log size distribution for trend spotting.
Test with boundary cases (shortest/longest).
Revisit limits as the feed matures.

54) A cross-team refactor renamed a key field. Your program compiles but outputs are wrong. Why?

REDEFINES or group alignment shifted silently.
The new name implies a new meaning; mapping is wrong.
Dependent validations now fire incorrectly.
Sort keys changed order, so downstream broke.
Copybook version mismatch at build time.
Contract tests didn’t run for this consumer.
Fix by aligning copybooks and re-signing the contract.
Add a build gate to catch future mismatches.

55) Month-end spikes cause DB timeouts from COBOL. What do you do first?

Batch reads into larger, predictable units.
Pre-stage hot data to files for sequential access.
Add retry with backoff for transient timeouts.
Limit concurrency during peak windows.
Tune queries to use stable indexes only.
Cache reference lookups in memory.
Align with DB team on maintenance windows.
Watch connection pool limits and errors.

56) An analyst wants free-form comments in outputs; your layouts are fixed-width. Compromise?

Provide a capped comment field with clear max length.
Escape/normalize characters to keep fixed-width safe.
Offer an auxiliary “notes” file for long text.
Document truncation rules to avoid surprises.
Keep the main file schema stable for systems.
Pilot with real samples to set a sensible cap.
Add a warning when comments are truncated.
Revisit after two cycles based on usage.

57) A long COBOL loop occasionally hits division by zero. How do you guard it properly?

Validate divisors once up front and skip bad records.
Log the offending key and count, not every case.
Default to a safe value only if business accepts it.
Separate data errors from logic errors in logs.
Add a unit test with zero divisors to lock behavior.
Confirm upstream source isn’t silently changing rules.
Quarantine high-error input batches.
Close with a small data-quality dashboard.

58) The business wants “what-if” runs of the same job with different parameters. How to structure?

Externalize parameters in a small control file.
Keep outputs separated by scenario IDs.
Ensure pure functions inside—no hidden state.
Use the same golden dataset for comparability.
Log parameter sets alongside results.
Limit scenarios per run to control cost.
Provide a quick diff tool for results.
Document assumptions for each scenario.

59) Your COBOL app must publish to downstreams with different cutoffs. How do you manage timing?

Generate outputs per consumer schedule, not one size.
Stamp files with cutoff timestamps and run IDs.
Keep idempotent publishing—re-send safely if asked.
Don’t hold back early consumers for late ones.
Monitor missed cutoffs and alert.
Share a calendar and escalation path.
Add a dry-run to validate availability.
Review schedules quarterly.

60) A new teammate asks: “What are the biggest COBOL pitfalls to avoid on day one?”

Blindly editing copybooks without versioning.
Mixing encoding/precision rules across modules.
Sprinkling business rules everywhere instead of centralizing.
Logging sensitive data in clear text.
Ignoring contract tests and golden datasets.
Overusing in-program sorts instead of utilities.
Skipping restart/idempotency design.
Changing outputs without consumer sign-off.

Post Views: 13