Free and Premium Cisco 300-220 Dumps Questions Answers

The correct answer isThey produce false positives and false negatives. Automated dynamic malware analysis tools, such as sandboxes, execute suspicious files in isolated environments to observe runtime behavior. While these tools are extremely valuable in modern SOCs, they are not infallible and have well-known limitations.

False positives occur when benign software exhibits behaviors that resemble malicious activity, such as creating registry keys, spawning child processes, or making network connections. Conversely, false negatives occur when malware intentionallyevades detectionby altering its behavior. Many modern malware samples include sandbox evasion techniques such as checking for virtualized environments, delaying execution, requiring user interaction, or disabling malicious functionality when analysis artifacts are detected.

Option A is incorrect because dynamic analysis can, in fact, uncover runtime vulnerabilities and exploit behavior. Option C is misleading; while tooling coverage varies, language support is not the primary limitation. Option D refers tomanual analysis, whereas the question explicitly addresses automated tools.

From a threat hunting and malware analysis standpoint, professionals understand that sandbox results must becorrelated with other telemetry, including endpoint behavior, network traffic, and threat intelligence. Automated tools are best used as part of a layered analysis strategy, not as a single source of truth.

This limitation reinforces a core security principle:automation accelerates detection, but human judgment validates impact. Skilled analysts interpret sandbox results, investigate anomalies, and determine real-world risk. Understanding the strengths and weaknesses of dynamic analysis tools allows SOC teams to avoid overreliance on automation while still benefiting from its speed and scale.

The correct answer isdocumenting findings and updating detection logic. This represents thepost-hunt operationalization phase, which is critical for long-term security improvement.

While options A and C are necessary response actions, they address only thecurrent incident. Threat hunting’s strategic value comes from transforming discoveries intorepeatable detections, playbooks, and controls.

Professional threat hunting programs ensure that:

Successful hunts produce new SIEM rules

Detection gaps are closed

Findings are documented for future analysts

Lessons learned inform security architecture decisions

Option D continues exploration but fails to institutionalize knowledge. Without operationalizing results, organizations repeatedly rediscover the same threats.

This phase directly increases maturity in theThreat Hunting Maturity Model, shifting organizations from hero-driven hunting to scalable, resilient detection. It also moves defendersup the Pyramid of Pain, forcing adversaries to change tactics rather than indicators.

Therefore, optionBis the correct and most strategically important answer.

The correct answer isendpoint memory access telemetry. Credential dumping often involves accessing sensitive memory regions, such as LSASS, rather than deploying obvious malware.

Modern attackers frequently use:

Legitimate tools

In-memory techniques

Living-off-the-land binaries

These methods bypass file-based detection entirely. Email, DNS, and firewall logs provide limited visibility into memory-level abuse.

Endpoint memory telemetry enables detection of:

Unauthorized LSASS access

Suspicious handle requests

Abnormal process injection

This telemetry is foundational for detectingcredential access techniquesin modern environments. Therefore, optionBis correct.

The correct next action is tosubmit the file for sandboxing. In professional security operations and threat hunting workflows, sandboxing is the most appropriate step when a file originates from an untrusted source and hash-based reputation checks return anunknownresult. An unknown hash means the file has not yet been classified as benign or malicious by threat intelligence databases, which is common with newly created malware or targeted attacks.

Sandboxing allows the security team to performdynamic analysisby executing the file in an isolated, controlled environment. This process observes runtime behaviors such as process creation, registry modification, network communications, command-and-control callbacks, file system changes, and exploit attempts. These behaviors provide high-fidelity indicators that static analysis or hash lookups cannot reveal.

Option B, reviewing the directory path, is useful for contextual awareness but does not determine whether the file is malicious. Option C, running a full malware scan, is premature; modern malware often evades signature-based scans, especially when the file is previously unknown. Option D, investigating the reputation of the website, is a supporting activity but does not assess the actual behavior or payload of the downloaded file.

From a threat hunting and incident response standpoint, sandboxing bridges the gap betweendetection and confirmation. If the sandbox analysis confirms malicious behavior, the team can escalate to containment actions such as isolating the endpoint, blocking hashes and domains, and performing scope analysis to identify other affected systems. Additionally, sandbox results can be used to create new SIEM detections and EDR behavioral rules, strengthening future defenses.

This approach aligns with professional best practices:unknown file + untrusted source = dynamic analysis first. It ensures accurate classification while minimizing unnecessary disruption to the user or environment.

The correct answers areA. Service and version of the web serverandC. Technology used by the application. The error message shown in the exhibit is a classic example ofverbose error handling, which unintentionally discloses sensitive internal details about the web application stack.

First, the error page explicitly referencesApache Tomcat/6.0.16at the bottom. This directly exposes theweb server/service and its exact version, makingOption Acorrect. From an attacker’s perspective, this is valuable intelligence because it allows them to search forknown vulnerabilities, exploits, or misconfigurationsspecific to that version of Tomcat. Older versions of Tomcat, in particular, have a long history of publicly documented security flaws.

Second, the stack trace references components such as:

org.apache.jasper.compiler.*

.jsp files (e.g., /user/left.jsp)

javax.servlet.http.HttpServlet

These details clearly reveal thetechnology stack used by the application, namelyJava Server Pages (JSP)running onApache Tomcat with Apache Jasper. This confirmsOption Cas correct. Exposing application technology helps attackers tailor attacks such as deserialization exploits, JSP injection attempts, or framework-specific vulnerabilities.

Option B is incorrect because the error message doesnotstate or imply that Apache Jasper is vulnerable to path injection; it merely shows a compilation/runtime error. Option D is incorrect because the error page provides no information about theclient-side browser version—all disclosed details relate to server-side processing.

From a professional security and threat hunting perspective, verbose error messages significantlyincrease attack surface visibility. Best practices dictate that production systems should returngeneric error messagesto users while logging detailed stack traces internally. This scenario reinforces why proper error handling and information disclosure controls are critical defensive measures.

In summary, the penetration test error message exposes:

Theweb server service and version

Theapplication technology stack

Therefore, the correct answers areA and C.

The correct answer isanalyzing abnormal behavior patterns across identity, endpoint, and network telemetry. This approach represents the foundation of modern threat hunting and directly addresses adversaries who deliberately avoid traditional detections.

Advanced attackers increasingly rely onliving-off-the-land techniques, stolen credentials, and legitimate administrative tools such as PowerShell, WMI, RDP, and cloud APIs. These activities rarely generate malware signatures or known IOCs, making alert-driven and signature-based defenses insufficient. As a result, mature threat hunting programs shift focus towardbehavioral analysis and anomaly detection.

Option A and D rely on static indicators such as IPs, domains, and hashes. These sit at thelowest levels of the Pyramid of Painand are trivial for attackers to change. Option B is purely reactive and limited to known malware, offering little value against stealthy intrusions.

By correlating identity logs (authentication patterns, geolocation anomalies), endpoint telemetry (process execution, parent-child relationships), and network activity (unusual connections, lateral movement patterns), hunters can detectIndicators of Attack (IOAs)rather than waiting for confirmed compromise. This enables identification of credential misuse, privilege abuse, and lateral movement even when no malware is present.

This methodology aligns withMITRE ATT&CK TTP-based hunting, which focuses on tactics and techniques instead of tools or infrastructure. It also reflects a higher tier in theThreat Hunting Maturity Model, where organizations proactively search for unknown threats rather than responding to alerts.

In professional SOC environments, this shift dramatically increases detection coverage against advanced adversaries and reduces dwell time. Therefore, optionCis the most accurate and strategically sound answer.

The correct answer isMonitoring failed AWS console login attempts. The Bash script shown in the exhibit is clearly designed toparse AWS CloudTrail logsand extract specific authentication-related events.

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

gunzip -c *.json.gz indicates the script is processingcompressed 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 offailed 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 detectcredential 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 supportsidentity-focused detection, which is critical in AWS environments where IAM misuse is one of the most common initial access vectors. It aligns withMITRE ATT&CK – Credential Access and Initial Access, particularly techniques involving valid account abuse.

In summary, the script’s clear purpose is tomonitor failed AWS console login attempts, makingOption Athe correct and professionally validated answer.

The correct answer issmall, periodic outbound connections to a rare destination. Beaconing is a hallmark of command-and-control (C2) communication, particularly in stealthy malware campaigns.

Attackers design C2 channels to:

Minimize bandwidth usage

Blend into normal traffic

Avoid triggering threshold-based alerts

As a result, beaconing traffic often consists oflow-volume, regular intervalsconnecting to the same external destination. Cisco Secure Network Analytics is purpose-built to detect this type ofbehavioral anomalyusing NetFlow and telemetry analysis.

Option A suggests data exfiltration rather than beaconing. Option B is too broad and unspecific. Option D relates to denial-of-service or scanning activity, not C2.

This hunting technique aligns withMITRE ATT&CK – Command and Controland is explicitly covered in theCBRTHD blueprintunder network-based threat hunting. Detecting beaconing behavior forces attackers to significantly alter their communication strategy, increasing their operational cost.

Therefore,Option Cis the correct and Cisco-aligned answer.

The correct answer isCredential Access. In the MITRE ATT&CK framework,password sprayingis classified under theCredential Access tactic (TA0006), specifically techniqueT1110.003 – Password Spraying. This classification is based on the attacker’s primary objective:gaining valid credentialsby systematically attempting a small number of common or weak passwords across many user accounts.

Password spraying differs from brute-force attacks in that it intentionally avoids rapid or repeated attempts against a single account, thereby evading account lockout controls and basic detection mechanisms. Instead, attackers “spray” one password (for example,Winter2025!orPassword123) across a large number of users, exploiting the likelihood that at least one account will use that password.

Although successful password spraying often leads toinitial access, MITRE classifies it underCredential Accessbecause the technique’s defining action is theacquisition of credentials, not the system entry itself. Initial access is the outcome, while credential theft is the method. This distinction is critical for threat hunters, as it guides where detections and controls should be focused.

From a professional threat hunting perspective, defenders monitor authentication telemetry such as failed and successful logins across identity providers, VPNs, cloud services, and email platforms. Indicators include multiple authentication failures across many accounts from a single source IP, followed by one or more successful logins. Identity-centric logging and anomaly detection are foundational here, reinforcing the principle thatidentity is the primary attack surface in modern environments.

Understanding password spraying as a credential access technique helps organizations prioritize protections such as strong password policies, MFA enforcement, adaptive authentication, and detection logic tuned for low-and-slow authentication abuse.

The correct answer iscorrelating attacker behavior across multiple MITRE ATT&CK techniques. This approach focuses onbehavioral detection, which is the cornerstone of effective threat hunting and advanced security operations.

Attackers who abuse legitimate administrative tools—often referred to asliving-off-the-land techniques—intentionally avoid malware-based detections. File hashes, signatures, and known indicators provide minimal value because there may beno malicious files at all. Options A and D sit at the lowest levels of thePyramid of Pain, making them easy for adversaries to evade.

By correlating behavior across multiple ATT&CK techniques—such as credential access, lateral movement, privilege escalation, and command execution—defenders detecthowthe attacker operates rather thanwhat toolsthey use. This forces adversaries to fundamentally change tradecraft, which is costly, risky, and time-consuming.

Option C improves visibility but does not inherently raise attacker cost. Threat intelligence feeds are reactive and often lag behind active campaigns.

From a professional threat hunting perspective, correlating multiple low-signal behaviors into ahigh-confidence attack patternis how mature SOCs detect stealthy intrusions. This method also supports scalable detection engineering, improved alert fidelity, and reduced false positives.

This strategy directly aligns with higher tiers of theThreat Hunting Maturity Modeland the top of thePyramid of Pain, making optionBthe correct answer.

The correct answer is SQL injection. The decoded HTTP request shown in the exhibit contains multiple unmistakable indicators of a SQL injection attack, including the use of SQL keywords and functions such as SELECT, CASE, SUBSTRING, ASCII, BIN, and conditional SLEEP() statements. These elements are characteristic of time-based blind SQL injection, a technique attackers use to extract database information when direct query results are not visible.

From a professional cybersecurity perspective, the presence of expressions like:

SELECT (CASE WHEN … THEN SLEEP(x))

SUBSTRING(password,1,1)

ASCII() and binary conversions

indicates that the attacker is probing the backend database character by character and using response timing to infer whether conditions are true or false. This is a well-known exploitation method used when error messages or query output are suppressed by the application.

The use of Base64 encoding does not represent the attack itself but rather an obfuscation technique to evade basic web application firewall (WAF) signatures and logging visibility. Encoding payloads allows attackers to bypass simple pattern-matching defenses, but once decoded, the underlying SQL injection becomes evident.

Option A (Unicode encoding) is incorrect because Unicode is commonly used for evasion, not exploitation. Option C (directory traversal) typically involves sequences like ../ to access filesystem paths, which are not present. Option D (XSS) targets client-side script execution and would include JavaScript payloads rather than database-focused logic.

According to the MITRE ATT&CK framework, this activity maps to Initial Access – Exploit Public-Facing Application (T1190). SQL injection remains one of the most exploited vulnerabilities in public-facing applications due to poor input validation and insecure coding practices.

For threat hunters and defenders, this scenario reinforces the importance of deep payload inspection, decoding obfuscated requests, monitoring for anomalous database query behavior, and enforcing secure development practices such as parameterized queries and input sanitization. SQL injection continues to be a high-impact, real-world attack vector despite being well understood, making it a critical focus area in web application threat hunting.

The correct answer isconverting hunt findings into permanent detections. Threat hunting is only effective when discoveries areoperationalized.

Without converting findings into SIEM, EDR, or NDR detections, organizations repeatedly identify the same attacker behaviors, wasting time and resources. Options A, B, and D improve capacity but do not eliminate blind spots.

Mature threat hunting programs ensure that:

Hunts produce detection rules

Alerts are tuned and validated

Knowledge is institutionalized

This is a defining trait ofhigh-maturity security organizationsand directly improves resilience. Therefore, optionCis correct.

The correct answer isit reveals operational discipline and intent. Attribution relies heavily on understandinghow attackers think and operate, not just the tools they use.

Operational discipline—such as careful reconnaissance, avoiding noisy exploitation, and operating during business hours—is ahuman behavioral pattern. These patterns are far more stable than infrastructure or malware and often correlate strongly with specific threat actor groups.

Option A and D focus on tooling, which changes frequently. Option B relates to persistence, not attribution.

Threat intelligence professionals use operational characteristics to distinguish between opportunistic criminals and advanced adversaries. Business-hour activity, careful lateral movement, and deliberate escalation often indicatetargeted intrusions, espionage, or financially motivated but sophisticated actors.

This information helps analysts align observed behavior with known threat actor profiles, improving attribution confidence. Thus, optionCis correct.

The correct answer isdocument findings and operationalize detections. In Cisco’s threat hunting methodology, confirmation of malicious activity isnot the end of the hunt.

The most critical next step is to:

Document attacker behavior

Identify detection gaps

Create or improve SIEM, EDR, or NDR detection rules

This ensures the organization does not repeatedly rediscover the same threat. Options C and D are incident response and communication activities, not threat hunting lifecycle steps. Option A skips the crucial improvement phase.

TheCBRTHD blueprintstrongly emphasizes:

Continuous improvement

Feedback loops

Detection engineering

By operationalizing findings, the SOC increases maturity and forces adversaries to change tactics.

Therefore,Option Bis correct.

The correct answers areUse of Windows Remote Management (C)andUse of tools and commands to connect to remote shares (E). Both are core mechanisms attackers leverage forlateral movementafter gaining valid credentials through techniques such as pass-the-hash or pass-the-ticket.

Windows Remote Management (WinRM) is a legitimate administrative service used for remote command execution and system management. However, attackers frequently abuse WinRM to move laterally by executing commands on remote endpoints using stolen credentials. From a threat hunting perspective, abnormal WinRM usage—such as execution outside normal administrative hours, from unusual source hosts, or by non-administrative user accounts—is a strong indicator of lateral movement activity.

Similarly, the use of tools and commands to connect to remote shares (such as net use, wmic, SMB-based access, or mounting administrative shares like C$) is a classic lateral movement technique. Attackers use remote shares to transfer tools, stage payloads, and execute malware across systems. Monitoring these activities at the endpoint level helps identify suspicious authentication attempts, unexpected share access, and abnormal file transfers.

Option A (runas) relates more to privilege escalation than lateral movement. Option B is specific to Linux privilege persistence and is not relevant to endpoint lateral movement hunting in this context. Option D (scheduled task creation) is primarily associated with persistence rather than movement between systems.

By monitoring WinRM activity and remote share usage, security teams gain visibility intocredential-based movement, which remains one of the most common and dangerous attacker behaviors in enterprise environments. Effective lateral movement hunting focuses onhow credentials are used, not just how they are stolen.

The correct answer isreduction in attacker dwell time. Dwell time measures how long an attacker remains undetected in the environment.

As threat hunting maturity increases:

Detection becomes faster

Behavioral coverage improves

Attackers are identified earlier in the kill chain

Metrics such as alert volume or blocked IPs (Options A and D) do not reflect effectiveness and may even indicate excessive noise. Option B measures inputs, not outcomes.

Cisco’sCBRTHD blueprintfocuses onoutcomes, not activity. Reduced dwell time demonstrates:

Effective hunting

Better visibility

Stronger detection engineering

This metric directly correlates with reduced breach impact and improved resilience.

Therefore,Option Cis the correct and Cisco-aligned answer.

The correct answer isB. Using "SecurityScan/2.5" to access all /admin endpoints. This log entry most clearly aligns with anauthorized security assessmentactivity designed to test weaknesses previously exploited during a breach.

Authorized security assessments—such as penetration tests or red team exercises—are typicallycontrolled, scoped, and intentional. They focus on validating security controls by probing sensitive areas (like administrative interfaces) while avoiding destructive actions. The user-agent string"SecurityScan/2.5"strongly suggests a purpose-built security scanning tool, which is commonly used by internal security teams or third-party assessors during sanctioned testing.

Option A is incorrect because performing login attempts at scale usingleaked credentialswould constitute real-world malicious behavior unless explicitly approved and tightly controlled. Even in authorized tests, credential stuffing with known leaked credentials is highly sensitive and would be explicitly documented; the wording here implies adversary behavior rather than assessment activity.

Option C is clearly malicious and inappropriate for an authorized assessment. Executing ashutdown commandis a destructive action and would violate standard rules of engagement for professional security testing. Such activity would be classified as adversarial exploitation, not a controlled delivery method.

Option D represents benign reconnaissance. A genericweb crawler gathering public-facing informationis typically associated with search engines or basic reconnaissance and does not directly test weaknesses related to a prior breach. While reconnaissance is part of assessments, it is not a strong indicator of a targeted, authorized delivery method.

From a threat hunting and SOC perspective, recognizing authorized assessment activity is critical to avoid misclassifying tests as incidents. Indicators include identifiable scanner user agents, predictable request patterns, limited scope targeting (such as /admin paths), and non-destructive behavior. This scenario reinforces an important operational lesson:context, intent, and tooling matter. Among the options,SecurityScan/2.5 targeting administrative endpointsbest reflects a legitimate, authorized security assessment delivery method.

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

Attack trees begin with ahigh-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 capturesequential 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 ashared mental modelbetween architects, SOC teams, and red teams. They directly inform detection engineering by highlightingcritical choke pointswhere attacker behavior must occur, such as token abuse, API enumeration, or anomalous role assumption in cloud environments.

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

Thus, optionCis the most appropriate and professionally validated answer.