CrowdStrike CCFH-202 Exam Dumps & Practice Test Questions
Question 1:
Which scenario below best represents suspicious behavior exhibited by a system process
A. PowerShell running with the RemoteSigned execution policy
B. A web browser like Internet Explorer initiating multiple DNS queries
C. PowerShell executing a PowerShell script
D. A non-networking process, such as notepad.exe, attempting to make an outbound network connection
Correct Answer: D
Explanation:
Suspicious process behavior refers to unusual or abnormal actions taken by a software process that deviate from its expected or documented function. Such behavior often signals potential compromise, misuse, or malicious activity and is a critical focus in threat detection, endpoint protection, and security monitoring.
Let’s begin with Option A, which describes PowerShell running with the RemoteSigned execution policy. This policy is commonly used in secure enterprise environments to prevent unauthorized scripts from running. It allows locally created scripts to execute freely while requiring downloaded scripts to be signed by a trusted certificate authority. This configuration strikes a balance between usability and security and is not considered abnormal or suspicious in itself.
Option B mentions a web browser, like Internet Explorer, performing several DNS requests. Browsers typically generate multiple DNS lookups during normal operations—such as when loading a webpage with multiple resources like images, advertisements, or third-party scripts. This is normal behavior and not inherently suspicious unless paired with other indicators of compromise, like unusual destinations or domains associated with command-and-control infrastructure.
Option C involves PowerShell launching a script, which is a standard use case. PowerShell is a powerful administrative tool and scripting environment widely used for automation. On its own, executing a script is not unusual. While attackers frequently abuse PowerShell, the mere execution of a script doesn’t indicate malicious intent unless it involves obfuscation, suspicious command-line arguments, or interactions with the file system or network.
Option D, however, describes highly anomalous behavior: a non-networking process (e.g., notepad.exe) initiating outbound network communication. Notepad is a basic text editor designed solely for local operations. If such an application attempts to connect to the internet or another host, it raises immediate suspicion. This often points to process injection, DLL hijacking, or another technique where malware leverages legitimate, benign processes to evade detection and exfiltrate data or establish remote control.
In behavioral threat detection and endpoint monitoring, this type of event—where a non-networking process engages in unexpected communication—is considered a red flag. It warrants deeper investigation to determine whether the system has been compromised or is participating in an attack chain.
Therefore, Option D is the correct choice, as it exemplifies a clear deviation from standard behavior and strongly indicates potentially malicious activity.
Question 2:
When analyzing a FileWritten event, which field most accurately reflects the system time when the event occurred?
A. ContextTimeStamp_decimal
B. FileTimeStamp_decimal
C. ProcessStartTime_decimal
D. timestamp
Correct Answer: D
Explanation:
In the context of security event analysis, particularly when reviewing telemetry for file operations, accurately identifying the precise time an event occurred is essential for building an incident timeline and performing root cause analysis. This question asks which field provides the most accurate representation of the system time for a FileWritten event.
Let’s start with Option A: ContextTimeStamp_decimal. This field may hold a timestamp that is adjusted or calculated based on the context of the process or user session. While useful for sequencing events within a process or understanding relative timing, it doesn’t necessarily align with the absolute system time when the file write occurred. Analysts generally do not rely on this field for exact event timing.
Option B: FileTimeStamp_decimal refers to timestamps stored within the file system metadata, such as "last modified" or "created" times. Although useful, these values can be manipulated by attackers or altered by legitimate tools, making them less reliable for forensic analysis. Malware often tampers with file metadata to hide its activity, rendering this field insufficient for accurate event tracking.
Option C: ProcessStartTime_decimal indicates the time at which the process responsible for the file write was initiated. While this is useful for context—especially to understand what the process did before or after the file event—it doesn’t capture the exact moment the file was written. A process can remain active for minutes or even hours, performing various operations during its lifetime.
Option D: timestamp, on the other hand, provides the actual system-recorded time at which the FileWritten event was logged. It is generated by the telemetry sensor at the time the system detects the write operation. This field is reliable, standardized, and immune to manipulation by userland processes. It is commonly used for chronological sorting of events and for building accurate incident timelines across multiple telemetry sources.
During threat investigations and forensic analysis, the timestamp field becomes the central point of reference. It ensures consistency and integrity in event chronology, especially when aligning file events with process executions, user logins, or network activity. Analysts rely on this field for precision and clarity in their findings.
Thus, Option D is the correct answer because it reflects the authoritative system time for the FileWritten event, offering the most reliable and verifiable source for timing in security investigations.
Question 3:
Which Falcon platform search feature is best suited for helping a threat hunter distinguish between routine user activity (such as DevOps or testing) and potentially malicious actions?
A. Hash Search
B. IP Search
C. Domain Search
D. User Search
Correct Answer: D
Explanation:
In the realm of threat hunting and incident investigation, understanding who performed an action is just as essential as what action occurred. Differentiating between legitimate operational tasks—like those performed by DevOps engineers or testers—and unauthorized or suspicious behaviors hinges on the ability to attribute activity to specific users. In this context, the User Search function within the Falcon platform becomes particularly valuable.
Let’s consider what each option offers:
A. Hash Search is primarily used to identify specific files or executables by their unique hashes. This is helpful for spotting malware or known bad binaries, but it doesn’t provide context about which user executed or accessed the file. It is a powerful tool for object-based threat identification, but not for distinguishing between authorized and unauthorized usage.
B. IP Search can reveal which systems communicated with specific IP addresses, and is useful in uncovering suspicious network activity like connections to known malicious command-and-control servers. However, IP addresses lack direct user attribution, so while they show where traffic went or came from, they don’t clarify who initiated it.
C. Domain Search helps detect access to suspicious external domains, potentially revealing phishing attempts or malware callbacks. Yet like IP search, it is more focused on destinations than the identity of the user performing the action.
D. User Search, on the other hand, aggregates data specifically tied to user accounts. This search feature empowers analysts to observe behavior patterns over time, such as logins, command executions, and file access associated with particular users. For example, if a PowerShell script modifies system settings, knowing whether the user is a trusted DevOps engineer or a generic account not typically used for scripting is critical for context.
By reviewing historical actions, the User Search interface helps establish a baseline of normal user behavior. This makes it easier to identify anomalies—such as a non-technical account performing complex system modifications—which may indicate credential compromise or insider threats.
Thus, to effectively differentiate between expected user activity and actions suggestive of an adversary, the User Search tool provides the most relevant, context-rich information for a threat hunter.
Question 4:
An analyst reviews detection logs in the Falcon platform by organizing them from the oldest to the newest to determine which system was likely compromised first. What type of analysis does this method represent?
A. Host Visualization
B. Statistical Analysis
C. Temporal Analysis
D. Machine Learning
Correct Answer: C
Explanation:
In cybersecurity, identifying the initial point of compromise is crucial for understanding the full scope of an incident. When an analyst organizes detections chronologically to discover which system was affected first, this practice is known as temporal analysis. It involves examining events through the lens of time, allowing responders to reconstruct attack timelines and determine the progression of compromise.
Let’s break down each option:
A. Host Visualization refers to graphical representations of host activity. These visual dashboards can highlight trends and relationships between systems, but they are not inherently chronological. They provide a macro-level view, which helps spot anomalies, but they do not reveal the order in which events occurred.
B. Statistical Analysis uses data patterns, averages, and distributions to identify outliers or trends. This is useful for evaluating abnormal behavior (such as a spike in failed login attempts), but it doesn’t focus on the timing or sequence of individual events.
C. Temporal Analysis, the correct answer, is centered on time-based evaluation. By sorting detection logs by timestamp, analysts can identify the earliest indicators of compromise. This helps determine the likely “patient zero”—the first infected or exploited system. From this anchor point, an incident responder can map the attacker’s lateral movement, establish how long they were present (known as dwell time), and evaluate how the attack spread through the network.
Temporal analysis is especially important in large-scale or advanced persistent threat (APT) attacks. For instance, if multiple hosts show similar malware behavior, identifying the one that first exhibited signs can lead to the original phishing email, malicious attachment, or exploited vulnerability.
D. Machine Learning involves using algorithms to detect patterns or predict threats automatically. While ML can assist in identifying suspicious behavior, it does not involve manually sorting logs based on time, nor does it perform chronological reconstruction unless programmed to do so.
In summary, the analyst’s approach of sorting events by age to determine the first victim is a classic example of temporal analysis, a foundational method in cyber incident response.
Question 5:
When Falcon flags a file for potential execution, which indicators are most relevant for performing an initial evaluation of its legitimacy?
A. VirusTotal, Hybrid Analysis, and Google pivot indicator lights enabled
B. File name, path, Local and Global prevalence within the environment
C. File path, hard disk volume number, and IOC Management action
D. Local prevalence, IOC Management action, and Event Search
Correct Answer: B
Explanation:
When using the CrowdStrike Falcon platform to examine suspicious file activity, analysts are encouraged to begin their analysis using the most immediately available and context-rich indicators. These initial indicators are critical in assessing whether the file is potentially harmful or likely to be benign. Among the most relevant indicators available directly within the Falcon console are the file name, file path, and the file’s local and global prevalence.
File Name: This helps identify potential masquerading. Threat actors often use deceptive file names resembling legitimate processes, such as “svch0st.exe” instead of the authentic “svchost.exe.” A suspicious or unusual name can be an early red flag.
File Path: Knowing where the file is attempting to execute from provides context. Malicious files are often found in obscure or unauthorized locations, such as hidden directories, user temp folders, or unusual application data paths. Anomalous file paths can point to attempts at evasion.
Local and Global Prevalence:
Local Prevalence shows how often the file has appeared in your organization's network. A file with low local prevalence could indicate it's newly introduced, warranting closer scrutiny.
Global Prevalence measures how often the file has been observed across all Falcon deployments worldwide. Low global prevalence could indicate a unique or targeted threat, while high prevalence might suggest a widely used legitimate file—or a commonly distributed malware.
These indicators collectively provide a fast and structured method for making a preliminary judgment on whether further investigation or containment is necessary. They are visible immediately in Falcon’s console and serve as the first line of triage.
Why other options are incorrect:
Option A references third-party tools (e.g., VirusTotal and Hybrid Analysis). While useful, these are generally used in later stages of investigation. Also, “Google pivot indicator lights” is not a Falcon feature.
Option C includes elements like “hard disk volume number” and “IOC Management action,” which don’t provide relevant insight at the detection level.
Option D lists tools used for deeper investigation or incident response, but not suitable for a quick, initial file assessment.
Thus, B is the best option for effective initial analysis within Falcon.
Question 6:
What is the primary advantage of adopting a structured threat hunting framework within a cybersecurity team?
A. Automatically generates incident reports
B. Eliminates false positives
C. Provides high fidelity threat actor attribution
D. Provides actionable, repeatable steps to conduct threat hunting
Correct Answer: D
Explanation:
A threat hunting framework serves as a systematic approach to uncovering hidden threats that evade traditional security tools. Rather than relying on random or reactive discovery methods, these frameworks allow cybersecurity teams to proactively investigate suspicious activities using structured, repeatable procedures.
The most important benefit of such a framework is its ability to provide actionable and repeatable steps. This allows analysts—regardless of their experience level—to follow a consistent methodology. The process typically involves:
Hypothesis Generation: Framing a question like “Is there any abnormal PowerShell activity in the network?”
Data Collection: Pulling relevant logs or endpoint telemetry.
Data Analysis: Correlating events, timelines, and anomalies.
Conclusion and Response: Determining if malicious behavior exists and initiating a response.
This repeatable structure not only improves consistency but also enhances the team’s efficiency and knowledge retention. Frameworks such as MITRE ATT&CK, Threat Hunting Maturity Models, or the Hunting Loop model from Sqrrl, enable threat hunters to map behaviors to known adversary tactics, making hunts more focused and evidence-driven.
Why the other choices are incorrect:
A (Automatically generates incident reports): Incident reporting is often the domain of SIEM or SOAR platforms, not the framework itself. While a framework may guide investigation, it doesn't handle automation or reporting natively.
B (Eliminates false positives): No threat hunting framework can eliminate false positives. However, over time, frameworks help analysts better differentiate between real threats and noise, thereby reducing false positives—not eradicating them.
C (Provides high fidelity threat actor attribution): Attribution involves intelligence gathering, geopolitical context, and behavioral patterning often outside the scope of standard hunting frameworks. While these frameworks might assist by aligning observed behavior with known threat groups, accurate attribution remains a more advanced discipline.
In conclusion, the core strength of using a framework lies in its ability to standardize, streamline, and scale threat hunting efforts, allowing teams to work more effectively and collaboratively. This makes D the most accurate and relevant answer.
Question 7:
Which of the following best represents a typical threat hunting lead within the Falcon platform?
A. A hunting query that returns processes with single-letter filenames like "a.exe" executing from temporary directories
B. Logs from a firewall indicating questionable traffic to an unidentified IP address
C. A support ticket describing a user’s machine becoming sluggish after clicking a suspicious email link
D. A third-party report referencing ransomware files with a distinct five-character file extension
Correct Answer: A
Explanation:
In the realm of proactive cybersecurity defense, threat hunting is the structured process of searching through environments to detect and isolate threats that evade automated security solutions. Within CrowdStrike Falcon, a threat hunting lead is a behavioral clue generated from endpoint telemetry that prompts deeper investigation—not a confirmed alert, but an insight worth analyzing.
Option A is the clearest representation of such a lead. It involves a pre-defined or custom Falcon query that detects processes with single-character filenames (e.g., a.exe) launching from temporary directories. This type of behavior is not normal for standard business operations and often signals attempts by malware to mask its activity or remain inconspicuous. Short filenames and execution from temporary locations (like %TEMP%) are tactics seen in commodity malware, fileless attacks, and adversaries staging tools before lateral movement. These anomalies, uncovered through Falcon’s query capabilities, can then be pivoted upon for further threat validation.
In contrast, option B describes network telemetry (e.g., firewall or security appliance logs). While important for broader investigations, this data typically originates outside of Falcon’s endpoint detection framework. Falcon focuses primarily on host-based behavior—such as process creation, file manipulation, and registry edits—rather than external device logs.
Option C refers to a reactive incident (user reporting an issue post-compromise). Although it may help initiate a response or retrospective investigation, it is not a Falcon-generated hunting lead. Threat hunting is about preemptively discovering suspicious activity without being prompted by user complaints or alerts.
Option D discusses an external threat intelligence artifact—a unique file extension used by ransomware. While useful for identifying IOCs (Indicators of Compromise), this isn't a native lead generated from Falcon's behavioral data. You might use this detail within Falcon to search for matching artifacts, but it’s not a behavioral anomaly discovered via Falcon’s proactive hunting features.
In summary, Falcon threat hunting relies on patterns derived from endpoint activity. Unusual process behaviors, like those described in A, align closely with how the platform is designed to surface potential threats—making it the most accurate representation of a Falcon threat hunting lead.
Question 8:
Under which specific PowerShell parameter does the Falcon Detections interface automatically decode encoded command-line content?
A. -Command
B. -Hidden
C. -e
D. -nop
Correct Answer: C
Explanation:
One of the tactics frequently used by attackers to avoid detection is encoding malicious scripts—particularly PowerShell commands—in Base64. Encoding conceals the script’s real intent from casual inspection and basic security tools. However, platforms like CrowdStrike Falcon are designed to detect and decode such obfuscation to aid analysts in identifying suspicious activity.
The Falcon Detections page automatically decodes Base64-encoded PowerShell commands only when it encounters the -EncodedCommand parameter, which is often abbreviated to -e. When this flag is present in a command line, Falcon recognizes that the next string is Base64-encoded, and it performs automatic decoding. The result—decoded command output—is then displayed directly within the Detections page, allowing analysts to see the real script content without manual decoding, speeding up incident analysis.
Let’s break down why the other choices are incorrect:
A (-Command): This parameter simply tells PowerShell to execute the string that follows. It doesn't imply encoding and thus doesn’t trigger Falcon’s automatic decoding mechanism. The command following this flag is executed as-is, without any Base64 interpretation.
B (-Hidden): This flag instructs PowerShell to run without showing a console window—commonly used to keep execution stealthy. While it's associated with malicious activity in some contexts, it doesn’t signal an encoded script, so Falcon doesn’t attempt to decode anything in this case.
D (-nop): This stands for “No Profile” and disables loading of the user’s PowerShell profile scripts. It's another obfuscation tactic often used during attacks, but again, it doesn’t indicate encoded data and won’t trigger decoding by Falcon.
Understanding how Falcon interprets PowerShell is crucial for analysts. When malicious actors try to hide commands using Base64, Falcon’s automatic decoding when -e is present helps security teams quickly uncover the hidden logic behind the script. This feature greatly improves visibility and reduces the time spent analyzing obfuscated payloads.
Therefore, C, representing the -e (EncodedCommand) parameter, is the correct answer since it directly prompts Falcon to decode and reveal the actual command being executed.
Question 9:
Which structured analysis method is specifically designed to compare multiple potential explanations and determine the most probable one based on available evidence?
A. Model hunting framework
B. Competitive analysis
C. Analysis of competing hypotheses
D. Key assumptions check
Correct Answer: C
Explanation:
The most effective method for systematically evaluating multiple possible explanations to identify the most plausible one is known as the Analysis of Competing Hypotheses (ACH). This structured technique is extensively used in both intelligence analysis and cyber threat detection. Its strength lies in promoting objectivity and minimizing confirmation bias, which often skews decision-making in environments where uncertainty is high.
ACH begins by listing all plausible hypotheses that could explain a given observation, such as suspicious activity on a network. Once the hypotheses are defined, the analyst gathers all relevant evidence and evaluates how each piece supports or contradicts each hypothesis. This is typically done using a matrix format, which allows the analyst to visualize conflicts and correlations between the evidence and the various hypotheses.
Unlike conventional thinking, where the hypothesis with the most supporting evidence might be chosen, ACH encourages selecting the hypothesis that has the least amount of contradictory evidence. This reversal of the typical approach ensures that analysts don’t unconsciously cherry-pick data to support a favored conclusion, which is a common cognitive bias.
Looking at the incorrect options:
A (Model hunting framework): While this may refer to certain detection models or analytic frameworks, it is not a formal technique used to compare and weigh multiple hypotheses against each other.
B (Competitive analysis): Typically used in business intelligence, this method compares organizations or products, not investigative hypotheses.
D (Key assumptions check): This technique is used to validate the assumptions that underlie a hypothesis or analysis but does not directly compare multiple hypotheses.
In cybersecurity, particularly during threat hunting or incident response, ACH is valuable because it helps analysts prioritize among possibilities like malware infections, user errors, or configuration issues. This prevents tunnel vision and ensures a broader, evidence-driven approach to investigations.
In conclusion, Analysis of Competing Hypotheses (ACH) is the structured analytic technique best suited to evaluating multiple explanations through evidence comparison, making C the correct answer.
Question 10:
In Falcon Event Search, which SPL field is automatically interpreted and converted into a human-readable UTC timestamp from Unix time?
A. utc_time
B. conv_time
C. _time
D. time
Correct Answer: C
Explanation:
When analyzing logs in Falcon Event Search, which uses Splunk as its backend, one of the key tasks is interpreting timestamps correctly. Most system-generated logs—especially those from endpoint detections or security telemetry—store times in Unix format, also known as Epoch time. To make this information understandable and usable for analysts, it must be translated into UTC-readable timestamps.
In Splunk (and by extension, Falcon Event Search), the default field that automatically handles this conversion is _time. This field is assigned during indexing based on the timestamp information embedded in the raw event data. Once indexed, _time is automatically displayed in a readable UTC format across the search interface.
Here’s why _time is essential:
It is the default timestamp field in Splunk and used for sorting, filtering, and correlating events.
Even if the source logs use Unix/Epoch format, Splunk converts them to _time during parsing, ensuring consistency in how time is displayed.
It enables powerful time-based queries, such as limiting events to the past 24 hours or correlating events across multiple hosts in specific time windows.
In Falcon Event Search, security teams rely on this field to reconstruct timelines and identify attack patterns.
Let’s examine why the other options are incorrect:
A (utc_time): Although the final displayed format is UTC, Splunk does not use a field named utc_time by default. This could exist in a custom environment, but it is not standard.
B (conv_time): This appears to be a hypothetical field name that does not exist in either Splunk or Falcon Event Search by default.
D (time): While it may seem correct, time is not a special field in Splunk. It does not carry the same built-in functionality as _time.
Ultimately, the _time field is fundamental in Splunk-based platforms like Falcon Event Search. It is the field that makes Unix timestamps human-readable, allows for precise filtering, and is crucial for accurate time-series threat analysis. Therefore, the correct answer is C.
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