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NEW QUESTION # 100
What are the benefits of maintaining a detection lifecycle?(Choosetwo)
Answer: B,D
Explanation:
Why Maintain a Detection Lifecycle?
Adetection lifecycleensures that security alerts, correlation searches, and automation playbooks arecontinuously refinedto maintainaccuracy, efficiency, and relevanceagainst modern threats.
#1. Detecting and Eliminating Outdated Searches (Answer A)#Removes unnecessary or redundant correlation searchesthat may slow down performance.#Prevents false positivescaused by outdated detection logic.
#Example:A Splunk ES search for anold malware variantmay no longer be effective # it should be updated to detectnew techniques used by attackers.
#2. Ensuring Detections Remain Relevant to Evolving Threats (Answer C)#Regular updatesensure thatnew MITRE ATT&CK techniquesand threat indicators are included.#Example:If attackers start usingLiving-off- the-Land (LotL) techniques, security teams mustupdate detection rules to identify suspicious PowerShell activity.
Why Not the Other Options?
#B. Scaling the Splunk deployment effectively- Lifecycle management improvesdetection accuracy, notinfrastructure scalability.#D. Automating the deployment of new detection logic- Automation helps, but lifecycle management isabout reviewing and updating detections, not just deployment.
References & Learning Resources
#Detection Management in Splunk ES: https://docs.splunk.com/Documentation/ES#Updating Threat Detections Using MITRE ATT&CK in Splunk: https://attack.mitre.org/resources#Best Practices for SOC Detection Engineering: https://splunkbase.splunk.com
NEW QUESTION # 101
What is an essential step in building effective dashboards for program analytics?
Answer: D
Explanation:
Building Effective Dashboards for Program Analytics
Well-designed dashboards help SOC teams visualize security trends, performance metrics, and compliance adherence efficiently.
#1. Applying Accelerated Data Models for Better Performance (B)
Speeds up dashboard loading times by using pre-aggregated datasets.
Improves SIEM performance when analyzing large volumes of security logs.
Example:
Instead of running a full search, an accelerated data model pre-indexes event counts by severity level.
#Incorrect Answers:
A: Using predefined templates without modification # Dashboards should be customized for security needs.
C: Avoiding the use of filters and tokens # Filters improve usability by allowing analysts to refine searches.
D: Limiting the number of visualizations # Dashboards should balance performance and visibility rather than limit insights.
#Additional Resources:
Splunk Accelerated Data Models
Building Fast and Efficient Dashboards
NEW QUESTION # 102
An engineer needs to create a new report capturing the vendors and products that detect a particular CVE in their environment. How can they ensure that the search associated with the report only includes accelerated data?
Answer: B
Explanation:
The appropriate approach is to query the Vulnerabilities data model using tstats , constrain the results to the particular CVE, and group or aggregate the results by vendor_product. This satisfies both requirements in the question: selecting vulnerability information associated with a CVE and ensuring that the search operates against accelerated data.
tstats is specifically designed to query indexed fields and accelerated data-model summaries efficiently. In conceptual form, the analytic follows this structure:
| tstats ... from datamodel=Vulnerabilities... where ...cve= < value > by ...vendor_product The exact field prefix depends on the dataset being addressed, but the architectural principle is the same: use the accelerated Vulnerabilities data model rather than scanning raw vulnerability events.
The Updates data model is not the appropriate semantic domain for reporting products associated with identified vulnerabilities. Likewise, simply searching for vendor_product without constraining the requested CVE would not satisfy the report requirement.
This question reinforces a recurring Cybersecurity Defense Engineer theme: choose the correct CIM domain and use accelerated-search mechanisms when operational reporting must scale efficiently.
Study Guide topics: Vulnerabilities data model, tstats, accelerated data, CVE reporting, vendor_product, search optimization.
NEW QUESTION # 103
What must be configured as a setting in a correlation search for a notable to be generated?
Answer: A
Explanation:
A correlation search must have the appropriate Adaptive Response Action configured when its intended outcome is creation of a notable event. Consequently, option C is correct.
The correlation search itself defines the analytics used to identify suspicious activity. Running that search successfully does not, by itself, mean every result automatically becomes a notable. The response configuration determines what Enterprise Security should do after the detection conditions are satisfied.
Configuring the notable-related adaptive response action supplies that operational behavior.
This separation is important because the same detection framework can support different outcomes.
Depending on design requirements, a correlation search may create analyst-facing findings, generate risk, invoke another response mechanism, or participate in additional automated workflows. The detection logic and response behavior therefore represent distinct parts of the engineering process.
A SOAR playbook is not a prerequisite for generating a notable; SOAR normally operates as a subsequent orchestration or response capability. Likewise, appending a | notable command to the SPL is not the configuration requirement being tested by this question.
Study Guide topics: Enterprise Security correlation searches, notable generation, Adaptive Response Actions, detection outcomes, response configuration.
NEW QUESTION # 104
There are multiple methods for communicating data with a REST endpoint. In the URL shown, what is the name of the key-value pairs represented after the question mark in the URL?
Answer: C
Explanation:
The key-value pairs appearing after the ? character in a URL are parameters , more specifically query parameters . The example structure shown in the supplied material follows the standard pattern:
?type=hash & data= < value >
Here, type=hash and data= < value > are query parameters. The question mark marks the beginning of the URL ' s query component, while an ampersand ( & ) separates multiple parameter pairs. Each parameter typically consists of a key followed by = and its corresponding value.
This must be distinguished from an HTTP payload , which is normally transmitted in the request body, particularly with operations such as POST or PUT. Headers are separate HTTP metadata elements containing information such as authorization credentials, content type, accepted response formats, and user-agent information. "KV Elements" is not the HTTP/REST terminology for the URL query component.
Understanding this distinction is important when configuring SOAR integrations because an API may require values in different locations. Supplying a required query parameter in the request body-or vice versa-can result in validation failures even when the correct data is present.
Study Guide topics: REST APIs; query parameters; HTTP requests; SOAR integrations; URL structure; API troubleshooting.
NEW QUESTION # 105
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