Free CCSE-204 Braindumps Download Updated on Jul 26, 2026 with 64 Questions
CrowdStrike CCSE-204 Exam Practice Test Questions
NEW QUESTION # 37
Which CQL function should you use to count events by hostname?
- A. table()
- B. kvParse()
- C. groupBy()
- D. parseJson()
Answer: C
Explanation:
The groupBy() function is used to aggregate events by one or more fields, such as hostname, and return counts or other aggregate calculations. table() displays selected fields but does not perform grouped aggregation. parseJson() and kvParse() are parsing functions, not aggregation functions.
NEW QUESTION # 38
An internal security team identified a small number of high-risk users. They ask you to create an app that will monitor these users and trigger an alert when specific suspicious behavior is detected.
Which Falcon feature should you use to develop this app?
- A. Charlotte AI
- B. Falcon QueryBuilder
- C. Falcon Spotlight
- D. Falcon Foundry
Answer: D
Explanation:
The correct answer is C. Falcon Foundry .
CrowdStrike describes Falcon Foundry as its application development platform for building custom apps on the Falcon platform. CrowdStrike's materials state that Falcon Foundry allows customers to quickly create their own apps, and the Foundry documentation/blog content shows it supports application logic and storage needed for custom workflows and monitoring use cases. That is exactly what fits a requirement to build an app that monitors a defined set of high-risk users and triggers alerts on suspicious activity.
Why the other options are incorrect:
Falcon QueryBuilder is for constructing queries, not building an application. Falcon Spotlight is CrowdStrike's vulnerability management capability, not an app-development framework. Charlotte AI is an AI assistant capability, not the platform feature used to develop custom monitoring apps. The only option that matches "develop this app" is Falcon Foundry .
NEW QUESTION # 39
You are creating an AI-generated parser to process and normalize log data from various sources.
How would you ensure the parser accurately interprets and categorizes the log data?
- A. Ensure the parser has a minimum of 100 lines
- B. Create a set of log examples to match log patterns from different sources
- C. Write the parser in a high-level programming language (Python or Java)
Answer: B
Explanation:
The correct answer is B . CrowdStrike states that AI-generated parsers are built from sample log records .
Falcon Next-Gen SIEM analyzes those samples to learn the logs' structure and content, so providing representative examples is the documented way to help the parser interpret and categorize data correctly.
Options A and C are not supported by CrowdStrike documentation. There is no requirement for a minimum parser length, and Next-Gen SIEM parsers are not written as Python or Java programs; CrowdStrike's parser template shows a parser schema and script structure specific to Next-Gen SIEM.
NEW QUESTION # 40
An event has the following fields:
Which CQL query will output the frequency of a unique set of ComputerName, UserName, CommandLine?
- A. #event_simpleName = ProcessRollup2
| FileName = ssh.exe
| CommandLine = /\s-R\s.+\s-p/
| groupBy([ComputerName, UserName, CommandLine], function=count()) - B. #event_simpleName = ProcessRollup2 FileName = ssh.exe CommandLine = /\s-R\s.+\s-p/ | table ([ComputerName, UserName, CommandLine]) | count()
- C. #event_simpleName = ProcessRollup2 FileName = ssh.exe CommandLine = /\s-R\s.+\s-p/ | groupBy ([ComputerName, UserName, CommandLine])
- D. #event_simpleName = ProcessRollup2
| FileName = ssh.exe
| CommandLine = /\s-R\s.+\s-p/
| table([ComputerName, UserName, CommandLine], function=count())
Answer: A
Explanation:
CrowdStrike LogScale documentation states that groupBy() is used to group events by one or more specified fields, similar to SQL GROUP BY. The documentation also says the function parameter accepts aggregate functions, and its default is count(as=_count). That means the query that explicitly groups by ComputerName, UserName, and CommandLine and applies function=count() is the correct way to output the frequency of each unique combination of those three fields.
Why the other options are incorrect:
A is incorrect because table() formats output rows but does not aggregate unique combinations into frequencies the way groupBy() does. Adding count() after table() does not produce grouped counts for each unique triplet. B is incorrect because table() is not the aggregation function documented for grouped frequency counting; groupBy() is. D is close, but it relies on the default count behavior rather than explicitly specifying function=count(). Since the question asks which query will output the frequency of a unique set, C is the most correct and explicit choice.
NEW QUESTION # 41
You need to ingest a data source into Next-Gen SIEM. There is a prebuilt Pull connector.
What is required to configure the connector?
- A. HEC token
- B. Data Source API key
- C. Falcon Log Collector hostname
- D. Falcon API URL
Answer: B
Explanation:
The correct answer is D. Data Source API key .
CrowdStrike's Next-Gen SIEM onboarding examples for prebuilt connectors show that, for pull-style integrations, you typically provide the API key generated in the external data source so Falcon Next-Gen SIEM can connect and start ingesting data. For example, CrowdStrike's Abnormal integration walkthrough says to enter the API key you generated , after which Falcon Next-Gen SIEM automatically connects and starts ingesting data.
Why the other options are incorrect:
A). HEC token is used for HTTP Event Collector push-style ingestion, not for a prebuilt pull connector.
B). Falcon Log Collector hostname is not the standard required credential for configuring a pull connector.
C). Falcon API URL is not the key external credential typically required by these pull connectors.
For prebuilt pull connectors, the required configuration is generally the data source's API key or equivalent credential .
NEW QUESTION # 42
You are onboarding a log source that includes a timestamp with a different timezone.
How should you address any time parsing errors that occur?
- A. Adjust the log source to reflect the correct timezone before sending logs
- B. Clone the parser and drop the timestamp field, use ingesttimestamp instead
- C. Clone the parser and change the timestamp field name
- D. Clone the parser and manually apply the timezone parameter
Answer: D
Explanation:
The correct answer is A . CrowdStrike documentation states that when a timestamp does not include timezone information, or when you need to control timezone interpretation, you should pass the timezone parameter to parseTimestamp() or findTimestamp(). Since parsers are where ingest-time transformations are defined, the correct engineering approach is to create or clone a custom parser for that log source and explicitly apply the needed timezone handling there. CrowdStrike's custom parser docs explain that parsers are used to control how incoming events are transformed during ingest, and the timestamp parsing docs explain that timezone can be set directly in the parser logic.
Why the other options are incorrect:
B is not the documented parser-side solution. While changing the source may work operationally in some environments, CrowdStrike's parsing guidance focuses on fixing time interpretation in the parser by using timezone or related timestamp parsing controls. C is incorrect because changing the timestamp field name does not solve timezone parsing. D is incorrect because dropping the source timestamp and relying on ingest time would lose the original event time, which is exactly what parsers are meant to preserve by converting source timestamps into @timestamp. CrowdStrike explicitly states that one of the most important jobs of a parser is assigning correct timestamps to events.
NEW QUESTION # 43
A parser needs to preserve the original third-party field name and also map it to an ECS-compatible field.
What is the best approach?
- A. Keep the original Vendor field and assign its value to a new ECS field
- B. Delete the original field after mapping
- C. Rename the original field to the ECS field
- D. Store both values only in @rawstring
Answer: A
Explanation:
A CPS-compliant approach keeps the original Vendor field while also assigning the value to a normalized ECS field. This preserves source fidelity and enables standardized search and detections. Renaming away the original field loses source context, and storing only in @rawstring prevents structured analysis.
NEW QUESTION # 44
Which field is compliant with CrowdStrike Parsing Standard (CPS)?
- A. Parser.type
- B. Parser.name
- C. #event.dataset
- D. #event.trigger
Answer: C
Explanation:
The correct answer is B. #event.dataset .
CrowdStrike's CPS documentation explicitly lists #event.dataset as one of the CPS-compliant parser tags.
The CPS migration documentation also repeats that CPS-compliant parsers use tags for fields including #ecs.
version , #event.dataset , and #event.kind .
Why the other options are incorrect:
Parser.type and Parser.name are not listed as CPS-compliant tags in the CPS standard.
#event.trigger is also not listed among the CPS-compliant fields/tags.
Therefore, the only CPS-compliant option given is #event.dataset .
NEW QUESTION # 45
Which field should be used in a correlation rule when detections must be based on the original event occurrence time?
- A. @timestamp
- B. @ingesttimestamp
- C. @rawstring
- D. @id
Answer: A
Explanation:
@timestamp represents the time the event actually occurred and is the appropriate field for event-time-based detections and correlations. @ingesttimestamp reflects when the platform received the event, which may differ due to delays. @rawstring is raw event content, and @id is not a time field.
NEW QUESTION # 46
You want a Next-Gen SIEM dashboard to update automatically when new data is available.
Which action would you take?
- A. Toggle the "Live" button to on
- B. Change the "Start Time" interval to 1 hour
- C. Change the "Relative Time Range" interval to 1 millisecond ago
- D. Change the "Fixed Time Range" to the current date
Answer: A
Explanation:
The correct answer is A . CrowdStrike LogScale documentation says the Live checkbox controls whether dashboard widget queries run as live or static queries. When enabled, the dashboard continuously updates with real-time data , which is exactly what the question asks for.
NEW QUESTION # 47
You notice that the format of incoming logs suddenly changes from JSON format to key-value pairs during log collection.
What action would you take to parse the data correctly?
- A. Disable parsing entirely
- B. Switch to fleet mode and monitor the logs
- C. Use a multi-source configuration with different parsers per source
- D. Restart the log collector in debug mode
Answer: C
Explanation:
The correct answer is A. Use a multi-source configuration with different parsers per source .
CrowdStrike's Falcon LogScale Collector documentation states that parsers can be set for each source . The collector configuration model also explains that the Sources section defines the source of the data, filters to be applied, and parsers . That means when different log formats are being collected, the correct design is to separate them by source and assign the appropriate parser to each source.
Why the other options are incorrect:
Switching to fleet mode or monitoring logs does not itself correct parsing logic. Restarting in debug mode may help troubleshoot, but it does not solve the format mismatch. Disabling parsing would make the data less useful, not more useful. The documented way to handle parser differences is to apply parsers at the source level.
NEW QUESTION # 48
A Falcon Log Collector has been configured with 4 sinks of type memory, each having a queue size of 2GB.
What is the minimum memory requirement produced by this configuration?
- A. 10 GB
- B. 8 GB
- C. 9 GB
- D. 12 GB
Answer: C
Explanation:
The correct answer is A. 9 GB .
CrowdStrike's Falcon LogScale Collector sizing documentation states that memory requirement for memory queues is linearly proportional to the number of sinks plus a constant baseline requirement of 1 GB .
The documentation gives a worked example: 1 GB baseline + queue sizes for each sink .
For this question:
* Number of sinks = 4
* Queue size per sink = 2 GB
* Total sink memory = 4 × 2 GB = 8 GB
* Add baseline memory = 1 GB
So the minimum memory requirement is:
8 GB + 1 GB = 9 GB .
That is why:
* A. 9 GB is correct
* B. 12 GB , C. 10 GB , and D. 8 GB are incorrect because they do not match CrowdStrike's documented sizing formula for memory queues.
NEW QUESTION # 49
What is the correct mode to enroll LogCollector into Fleet Management with configuration of the log sources stored and managed centrally in Next-Gen SIEM?
- A. Central
- B. Full
- C. Complete
- D. localConfig
Answer: B
Explanation:
The correct answer is A. Full .
CrowdStrike's Falcon LogScale Collector Fleet Management enrollment documentation states that the enrollment mode can be full or localConfig , and it specifically defines full as the mode that enrolls the collector into Fleet Management with the configuration of log sources stored and managed centrally in LogScale/Next-Gen SIEM.
Why the other options are incorrect:
B). Complete and C. Central are not documented enrollment mode names. D. localConfig is a valid mode, but CrowdStrike says that mode keeps the log source configuration managed and stored locally on the host , not centrally.
NEW QUESTION # 50
What is the recommended order of the three required activities to build an efficient CQL query?
- A. Filter > Format > Aggregate
- B. Filter > Aggregate > Format
- C. Format > Filter > Aggregate
- D. Aggregate > Filter > Format
Answer: B
Explanation:
The correct answer is B . CrowdStrike's query best-practices documentation says to filter first , then do transformations/formatting, then aggregate , and finally do any output-style post-processing such as table
/sorting. Among the choices given, Filter > Aggregate > Format is the best match because formatting/output belongs at the end for efficiency.
This is also consistent with CrowdStrike's explanation that CQL pipelines chain filter and transformation steps before aggregate functions, and that aggregate functions produce new result structures rather than raw events.
NEW QUESTION # 51
A correlation rule is generating a high volume of detections. You have been asked to temporarily deactivate it so your team can investigate.
What will happen to previously generated detections while the rule is in a deactivated state?
- A. Their status will change to closed and tagged as false positives in the console
- B. They will be immediately deleted from the console
- C. Their status will change to closed and tagged as true positives in the console
- D. They will not be impacted and will remain within the console
Answer: D
Explanation:
The correct answer is A . Deactivating a correlation rule stops it from generating new detections, but previously generated detections remain available in the console for review and investigation. Rule deactivation affects future rule execution state rather than retroactively changing, closing, or deleting detections that have already been created. That is why options B, C, and D are incorrect.
NEW QUESTION # 52
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