Cisco 300-220 Dumps - Pass Conducting Threat Hunting and Defending using Cisco Technologies for CyberOps Exam in First Attempt 2026

The correct answer is converting hunt findings into permanent detection rules. This action reflects the highest maturity outcome of threat hunting.

Threat hunting is not complete until discoveries are:

Documented

Operationalized

Automated where appropriate

Without converting findings into detections, SOC teams repeatedly rediscover the same threats, wasting time and effort.

Options A and B increase noise and risk false positives. Option D improves experience but does not institutionalize knowledge.

Cisco's CBRTHD blueprint emphasizes:

Continuous improvement

Detection engineering

Feedback loops between hunting and monitoring

By creating permanent detections, organizations:

Reduce dwell time

Improve consistency

Increase adversary cost

Therefore, Option C is the correct and most Cisco-aligned answer.

The correct answer is Monitoring failed AWS console login attempts. The Bash script shown in the exhibit is clearly designed to parse AWS CloudTrail logs and extract specific authentication-related events.

Breaking down the script behavior from a professional cloud security perspective:

gunzip -c *.json.gz indicates the script is processing compressed CloudTrail log files, which are typically stored in .json.gz format.

jq -c '.Records[]' parses individual CloudTrail records, a common approach when analyzing AWS activity logs.

The filter conditions explicitly check for:

eventSource == 'signin.amazonaws.com'

eventName == 'ConsoleLogin'

responseElements.ConsoleLogin == 'Failure'

These fields are definitive indicators of failed AWS Management Console login attempts. Additionally, the script extracts contextual fields such as:

Event time

Source IP address

Error message

AWS region

Username

MFA usage status

This data is exactly what security teams use to detect credential abuse, password spraying, brute-force attempts, and compromised IAM accounts. Monitoring failed console logins is a foundational cloud threat hunting activity, especially for identifying early stages of account takeover.

Option B is incorrect because the script does not establish AWS CLI sessions or authenticate to accounts. Option C is incorrect because instance errors would involve services like ec2.amazonaws.com and different event names. Option D is incorrect because the script is analyzing---not archiving---records, and it applies filtering logic rather than storage or lifecycle management.

From a threat hunting and cloud security standpoint, this script supports identity-focused detection, which is critical in AWS environments where IAM misuse is one of the most common initial access vectors. It aligns with MITRE ATT&CK -- Credential Access and Initial Access, particularly techniques involving valid account abuse.

In summary, the script's clear purpose is to monitor failed AWS console login attempts, making Option A the correct and professionally validated answer.

The correct answer is Observation of repeated failed logins followed by a successful login from a new location. This scenario represents an Indicator of Attack (IOA) because it reflects attacker behavior in progress, not confirmed compromise.

IOAs focus on patterns of malicious intent, such as credential abuse, reconnaissance, or lateral movement, even when no malware or known indicators are present. In this case, the sequence of failed authentication attempts followed by a successful login from an unusual location strongly suggests password spraying or credential stuffing, both common initial access techniques.

Options A, B, and D are classic Indicators of Compromise (IOCs). Hashes, domains, and IP addresses are static artifacts that indicate a system has already been compromised. These indicators sit low on the Pyramid of Pain and are easy for attackers to change.

Cisco's CBRTHD blueprint emphasizes hunting for IOAs because they enable:

Earlier detection

Reduced dwell time

Higher attacker cost

Cisco tools such as Secure Network Analytics, Secure Endpoint, and SIEM platforms are designed to correlate behavioral signals like authentication anomalies rather than relying solely on known bad indicators.

Therefore, Option C is the correct and Cisco-aligned answer.

The correct answer is analyzing authentication behavior anomalies across users and devices. Credential abuse is one of the most common and effective techniques used by modern attackers because it allows them to blend in with legitimate activity and bypass malware-based defenses.

Options A and B rely on malware indicators, which are often absent in credential-based attacks. Option D addresses only one potential delivery or command-and-control vector and does not detect misuse of valid credentials.

By analyzing authentication behavior, threat hunters can detect:

Impossible travel scenarios

Abnormal login times

Excessive failed logins followed by success

Logins from unusual devices or locations

Cisco tools such as Cisco Secure Network Analytics, VPN telemetry, and identity logs provide rich data sources for this type of hunting. This approach focuses on Indicators of Attack (IOAs) rather than Indicators of Compromise (IOCs), pushing detection higher on the Pyramid of Pain.

Within the CBRTHD blueprint, hunting for credential misuse is a core competency, especially in cloud and remote-access environments. Detecting these behaviors early significantly reduces attacker dwell time and limits the blast radius of compromise.

Therefore, Option C is the most effective and Cisco-aligned answer.

The correct answer is Attack trees. Attack trees are uniquely suited for modeling multi-step adversary behavior, which is essential when analyzing complex attack chains such as account takeover followed by data exfiltration.

Attack trees begin with a high-level attacker goal (for example, ''Exfiltrate customer data'') and then break that goal into multiple branches representing different paths an attacker could take. These paths can include credential compromise, API abuse, privilege escalation, lateral movement, and persistence. This structure mirrors how real adversaries think and operate.

Option A (STRIDE) is useful for identifying broad threat categories---such as spoofing, tampering, or information disclosure---but it does not naturally capture sequential attack paths. Option B (CVSS) focuses on vulnerability severity scoring, not adversary behavior. Option D (DREAD) assesses risk impact but does not visualize how attacks unfold across systems.

For threat hunters and defenders, attack trees provide a shared mental model between architects, SOC teams, and red teams. They directly inform detection engineering by highlighting critical choke points where attacker behavior must occur, such as token abuse, API enumeration, or anomalous role assumption in cloud environments.

In modern cloud security, where breaches often involve multiple low-severity issues chained together, attack trees offer far greater strategic value than component-by-component analysis. They also align closely with MITRE ATT&CK mapping, enabling defenders to translate threat models into actionable hunts.

Thus, option C is the most appropriate and professionally validated answer.