Cisco 350-201 Practice Test 98
Question 970
For an original CBRCOR educational scenario, consider: A high-volume firewall alert stream contains many repetitive benign health checks and the organization wants a model to replace all analyst review. Select TWO complementary steps: the targeted correction and its strongest prechange validation.
- Check: Compare running artifact hashes, pipeline history, monitoring results and recovery exercise for one staged playbook update.
- Change: Automate deterministic enrichment and routing while reserving ambiguous or high-impact decisions for evidence-based human approval.
- Check: Measure repetitive analyst effort, validation accuracy, false-positive rate and impact for each proposed automated step.
- Change: Add format validation, safe API request construction, secret redaction and controlled action conditions before using the script operationally.
- Fully automate permanent firewall bans for every probabilistic outlier to maximize alert throughput.
Explanation
Correct Answer
B:Routine lookups are automation candidates, but uncertainty and potential business damage require explicit decision boundaries. Acceptance: The workflow reduces toil without allowing uncertain classification to trigger unreviewed account or network disruption.
C:Routine lookups are automation candidates, but uncertainty and potential business damage require explicit decision boundaries. Required intervention: Automate deterministic enrichment and routing while reserving ambiguous or high-impact decisions for evidence-based human approval.
Incorrect Answers
A:Check: Compare running artifact hashes, pipeline history, monitoring results and recovery exercise for one staged playbook update. Routine lookups are automation candidates, but uncertainty and potential business damage require explicit decision boundaries.
D:Change: Add format validation, safe API request construction, secret redaction and controlled action conditions before using the script operationally. Routine lookups are automation candidates, but uncertainty and potential business damage require explicit decision boundaries.
E:For this select two assessment, the shortcut changes or bypasses the necessary correctness, governance or operational boundary rather than correcting it. Routine lookups are automation candidates, but uncertainty and potential business damage require explicit decision boundaries.
Question 971
A SOC analyst is examining this cybersecurity operations situation: A machine-learning classifier becomes more aggressive after analysts mark hundreds of false positives as unresolved incidents. A proposed shortcut is: Train from unreviewed alert closure labels and permanently trust every model score above an arbitrary value. What should the SOC analyst recommend as the narrowly scoped correction? Select ONE best answer.
- Use the provider-approved authentication scheme with protected headers, least-privilege token scope and redacted diagnostic logging.
- Train from unreviewed alert closure labels and permanently trust every model score above an arbitrary value.
- Introduce labeled case feedback, domain-specific validation and confidence thresholds with override and audit trails.
- Use a durable event identifier, bounded retry semantics and complete response traversal when modifying the incident workflow.
Explanation
Correct Answer
C:This targeted action addresses the observed security or operational control gap without discarding necessary controls. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
Incorrect Answers
A:Use the provider-approved authentication scheme with protected headers, least-privilege token scope and redacted diagnostic logging. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
B:ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly. For this best change assessment, the shortcut changes or bypasses the necessary correctness, governance or operational boundary rather than correcting it.
D:Use a durable event identifier, bounded retry semantics and complete response traversal when modifying the incident workflow. A machine-learning classifier becomes more aggressive after analysts mark hundreds of false positives as unresolved incidents.
Question 972
A Cisco cybersecurity incident response review finds: A machine-learning classifier becomes more aggressive after analysts mark hundreds of false positives as unresolved incidents. The team proposes: Introduce labeled case feedback, domain-specific validation and confidence thresholds with override and audit trails. Which engineering principle most strongly supports the recommended response? Select ONE best answer.
- Automation performs a task, whereas orchestration coordinates dependent tasks, approvals and incident lifecycle across tools.
- Security automation must distinguish candidate input from approved action and avoid making logs a new credential exposure source.
- ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
- HTTP 401 generally concerns authentication, 403 access refusal and 404 missing resources, subject to the provider's documented contract.
Explanation
Correct Answer
C:Demonstrable success requires: Automated routing uses evaluated confidence thresholds and uncertain high-impact actions receive analyst review. This principle explains the proposed technical approach, which is: Introduce labeled case feedback, domain-specific validation and confidence thresholds with override and audit trails.
Incorrect Answers
A:Automation performs a task, whereas orchestration coordinates dependent tasks, approvals and incident lifecycle across tools. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
B:Security automation must distinguish candidate input from approved action and avoid making logs a new credential exposure source. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
D:HTTP 401 generally concerns authentication, 403 access refusal and 404 missing resources, subject to the provider's documented contract. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
Question 973
The SOC team has not yet approved changes. Current evidence indicates: A machine-learning classifier becomes more aggressive after analysts mark hundreds of false positives as unresolved incidents. Which evidence best tests the technical diagnosis before approving a disruptive change? Select ONE best answer.
- Compare running artifact hashes, pipeline history, monitoring results and recovery exercise for one staged playbook update.
- Exercise missing and set variable cases while checking permissions, output paths and redacted logs.
- Exercise malformed indicators, valid samples and an induced error while inspecting outbound requests and sanitized logging.
- Compare labeled outcomes and detection quality across representative source groups before automatic escalation.
Explanation
Correct Answer
D:ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly. Applied case: A machine-learning classifier becomes more aggressive after analysts mark hundreds of false positives as unresolved incidents.
Incorrect Answers
A:Compare running artifact hashes, pipeline history, monitoring results and recovery exercise for one staged playbook update. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
B:Exercise missing and set variable cases while checking permissions, output paths and redacted logs. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
C:Exercise malformed indicators, valid samples and an induced error while inspecting outbound requests and sanitized logging. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
Question 974
A candidate correction has been tested and the platform team requires a post-change acceptance check. Original problem: A machine-learning classifier becomes more aggressive after analysts mark hundreds of false positives as unresolved incidents. The response owner needs positive and negative results and evidence that legitimate security and business workflows still function. Which acceptance result demonstrates correct security operations behavior without disrupting authorized workflows? Select ONE best answer.
- Final case status reflects verified completion or failure, and delayed enrichments remain traceable.
- Automated routing uses evaluated confidence thresholds and uncertain high-impact actions receive analyst review.
- Evidence collection reads the intended files only and rejects unsafe or ambiguous paths without destructive side effects.
- The client recognizes success without body content and retrieves all permitted paginated intelligence results.
Explanation
Correct Answer
B:ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly. The result must be validated using the documented device, service or access context.
Incorrect Answers
A:Final case status reflects verified completion or failure, and delayed enrichments remain traceable. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
C:Evidence collection reads the intended files only and rejects unsafe or ambiguous paths without destructive side effects. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
D:The client recognizes success without body content and retrieves all permitted paginated intelligence results. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
Question 975
An implementation sign-off request identifies the following problem: A machine-learning classifier becomes more aggressive after analysts mark hundreds of false positives as unresolved incidents. Which paired technical change and validation should close the implementation review? Select ONE best answer.
- Change: Add format validation, safe API request construction, secret redaction and controlled action conditions before using the script operationally. Verify: Exercise malformed indicators, valid samples and an induced error while inspecting outbound requests and sanitized logging.
- Change: Introduce labeled case feedback, domain-specific validation and confidence thresholds with override and audit trails. Verify: Compare labeled outcomes and detection quality across representative source groups before automatic escalation.
- Change: Validate required environment values, handle missing variables explicitly and prevent secret values from entering command output or shell history. Verify: Exercise missing and set variable cases while checking permissions, output paths and redacted logs.
- Change: Trace the script's parsing, conditional logic and collection operations, using typed decoded event fields for reliable classification. Verify: Run the script against a fixture with successes, failures, missing fields and nested JSON values and compare expected counts.
Explanation
Correct Answer
B:ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly. The change is successful only when: Automated routing uses evaluated confidence thresholds and uncertain high-impact actions receive analyst review.
Incorrect Answers
A:Change: Add format validation, safe API request construction, secret redaction and controlled action conditions before using the script operationally. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
C:Change: Validate required environment values, handle missing variables explicitly and prevent secret values from entering command output or shell history. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
D:Change: Trace the script's parsing, conditional logic and collection operations, using typed decoded event fields for reliable classification. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
Question 976
A SOC analyst is examining this cybersecurity operations situation: A SOC uses an anomaly score from one endpoint model as universal truth across cloud, mobile and network telemetry populations. A proposed shortcut is: Train from unreviewed alert closure labels and permanently trust every model score above an arbitrary value. What should the SOC analyst recommend as the narrowly scoped correction? Select ONE best answer.
- Respect published quota and Retry-After behavior, use bounded backoff and distinguish lookup failure from indicator verdict.
- Train from unreviewed alert closure labels and permanently trust every model score above an arbitrary value.
- Add format validation, safe API request construction, secret redaction and controlled action conditions before using the script operationally.
- Introduce labeled case feedback, domain-specific validation and confidence thresholds with override and audit trails.
Explanation
Correct Answer
D:This targeted action addresses the observed security or operational control gap without discarding necessary controls. Applied case: A SOC uses an anomaly score from one endpoint model as universal truth across cloud, mobile and network telemetry populations.
Incorrect Answers
A:Respect published quota and Retry-After behavior, use bounded backoff and distinguish lookup failure from indicator verdict. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
B:ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly. Use this instead: Introduce labeled case feedback, domain-specific validation and confidence thresholds with override and audit trails.
C:Add format validation, safe API request construction, secret redaction and controlled action conditions before using the script operationally. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
Question 977
A Cisco cybersecurity incident response review finds: A SOC uses an anomaly score from one endpoint model as universal truth across cloud, mobile and network telemetry populations. The team proposes: Introduce labeled case feedback, domain-specific validation and confidence thresholds with override and audit trails. Which engineering principle most strongly supports the recommended response? Select ONE best answer.
- Continuous delivery of security content should preserve detection semantics and manage false positives as well as deployment reliability.
- Network timeouts may hide a committed server action, while oversized requests can be rejected or partially processed.
- Shell word splitting and working directory context can change targets even when the visible command appears harmless.
- ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
Explanation
Correct Answer
D:Demonstrable success requires: Automated routing uses evaluated confidence thresholds and uncertain high-impact actions receive analyst review. Applied case: A SOC uses an anomaly score from one endpoint model as universal truth across cloud, mobile and network telemetry populations.
Incorrect Answers
A:This option addresses a different security operations topic (CI/CD security content validation stages) rather than the documented ML feedback loops and SOAR confidence thresholds decision.
B:Network timeouts may hide a committed server action, while oversized requests can be rejected or partially processed. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
C:Shell word splitting and working directory context can change targets even when the visible command appears harmless. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
Question 978
The SOC team has not yet approved changes. Current evidence indicates: A SOC uses an anomaly score from one endpoint model as universal truth across cloud, mobile and network telemetry populations. Which evidence best tests the technical diagnosis before approving a disruptive change? Select ONE best answer.
- Simulate 429 and timeouts against a mock enrichment provider and inspect retry intervals and result classification.
- Compare labeled outcomes and detection quality across representative source groups before automatic escalation.
- Exercise malformed indicators, valid samples and an induced error while inspecting outbound requests and sanitized logging.
- Compare raw response content type, parser output and rendered report for one controlled incident record.
Explanation
Correct Answer
B:ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly. Applied case: A SOC uses an anomaly score from one endpoint model as universal truth across cloud, mobile and network telemetry populations.
Incorrect Answers
A:Simulate 429 and timeouts against a mock enrichment provider and inspect retry intervals and result classification. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
C:Exercise malformed indicators, valid samples and an induced error while inspecting outbound requests and sanitized logging. A SOC uses an anomaly score from one endpoint model as universal truth across cloud, mobile and network telemetry populations.
D:Compare raw response content type, parser output and rendered report for one controlled incident record. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
Question 979
A candidate correction has been tested and the platform team requires a post-change acceptance check. Original problem: A SOC uses an anomaly score from one endpoint model as universal truth across cloud, mobile and network telemetry populations. The response owner needs positive and negative results and evidence that legitimate security and business workflows still function. Which acceptance result demonstrates correct security operations behavior without disrupting authorized workflows? Select ONE best answer.
- Authorized urgent containment occurs while benign evidence does not trigger uncontrolled administrative changes.
- Automated routing uses evaluated confidence thresholds and uncertain high-impact actions receive analyst review.
- The team can distinguish individual automated jobs from a governed multi-step response with recoverable execution history.
- Only validated authorized indicators progress and no credentials appear in source files or logs.
Explanation
Correct Answer
B:ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly. The approved implementation step was: Introduce labeled case feedback, domain-specific validation and confidence thresholds with override and audit trails.
Incorrect Answers
A:Authorized urgent containment occurs while benign evidence does not trigger uncontrolled administrative changes. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
C:The team can distinguish individual automated jobs from a governed multi-step response with recoverable execution history. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
D:Only validated authorized indicators progress and no credentials appear in source files or logs. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
Question 980
For an original CBRCOR educational scenario, consider: A SOC uses an anomaly score from one endpoint model as universal truth across cloud, mobile and network telemetry populations. Select TWO complementary steps: the targeted correction and its strongest prechange validation.
- Change: Introduce labeled case feedback, domain-specific validation and confidence thresholds with override and audit trails.
- Check: Compare labeled outcomes and detection quality across representative source groups before automatic escalation.
- Train from unreviewed alert closure labels and permanently trust every model score above an arbitrary value.
- Check: Run the script against a fixture with successes, failures, missing fields and nested JSON values and compare expected counts.
- Change: Quote variable expansions, validate paths, preserve logs and check command exit status before moving or deleting any evidence.
Explanation
Correct Answer
A:ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly. Evidence: Compare labeled outcomes and detection quality across representative source groups before automatic escalation.
B:ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly. Required intervention: Introduce labeled case feedback, domain-specific validation and confidence thresholds with override and audit trails.
Incorrect Answers
C:For this select two assessment, the shortcut changes or bypasses the necessary correctness, governance or operational boundary rather than correcting it. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
D:Check: Run the script against a fixture with successes, failures, missing fields and nested JSON values and compare expected counts. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
E:Change: Quote variable expansions, validate paths, preserve logs and check command exit status before moving or deleting any evidence. ML-assisted SOAR decisions depend on training quality and deployment context; poor labels or domain shift can propagate errors rapidly.
