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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Engineering | 10% | - Data parsing, normalization, and CIM alignment - Indexing performance and management - Data ingestion and onboarding |
| Topic 2: Detection Engineering | 40% | - Creation and tuning of detections (Correlation Searches) - Notable event generation and lifecycle management - Detection enrichment with context and risk-based alerting |
| Topic 3: Security Automation (SOAR) | 30% | - Playbook design and automation workflows - Incident response automation and orchestration |
| Topic 4: Security Operations and Program Development | 20% | - SOC process design and operational workflows - Threat intelligence integration |
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NEW QUESTION # 51
What methods can improve Splunk's indexing performance?(Choosetwo)
Answer: A,B
Explanation:
Improving Splunk's indexing performance is crucial for handling large volumes of data efficiently while maintaining fast search speeds and optimized storage utilization.
Methods to Improve Indexing Performance:
Enable Indexer Clustering (A)
Distributes indexing load across multiple indexers.
Ensures high availability and fault tolerance by replicating indexed data.
Optimize Event Breaking Rules (D)
Defines clear event boundaries to reduce processing overhead.
Uses correctLINE_BREAKERandTRUNCATEsettings to improve parsing speed.
NEW QUESTION # 52
What are the benefits of maintaining a detection lifecycle?(Choosetwo)
Answer: A,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 Detecting and Eliminating Outdated Searches (Answer A)#Removes unnecessary or redundant correlation searchesthat may slow down performance.#Prevents false positivescaused by outdated detection logic.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 # 53
What methods improve the efficiency of Splunk's automation capabilities? (Choose three)
Answer: A,D,E
Explanation:
How to Improve Splunk's Automation Efficiency?
Splunk's automation capabilities rely on efficient data ingestion, optimized searches, and automated response workflows. The following methods help improve Splunk's automation:
#1. Using Modular Inputs (Answer A)
Modular inputs allow Splunk to ingest third-party data efficiently (e.g., APIs, cloud services, or security tools).
Benefit: Improves automation by enabling real-time data collection for security workflows.
Example: Using a modular input to ingest threat intelligence feeds and trigger automatic responses.
#2. Optimizing Correlation Search Queries (Answer B)
Well-optimized correlation searches reduce query time and false positives.
Benefit: Faster detections # Triggers automated actions in SOAR with minimal delay.
Example: Usingtstatsinstead of raw searches for efficient event detection.
#3. Employing Prebuilt SOAR Playbooks (Answer E)
SOAR playbooks automate security responses based on predefined workflows.
Benefit: Reduces manual effort in phishing response, malware containment, etc.
Example: Automating phishing email analysis using a SOAR playbook that extracts attachments, checks URLs, and blocks malicious senders.
Why Not the Other Options?
#C. Leveraging saved search acceleration - Helps with dashboard performance, but doesn't directly improve automation.#D. Implementing low-latency indexing - Reduces indexing lag but is not a core automation feature.
References & Learning Resources
#Splunk SOAR Automation Guide: https://docs.splunk.com/Documentation/SOAR#Optimizing Correlation Searches in Splunk ES: https://docs.splunk.com/Documentation/ES#Prebuilt SOAR Playbooks for Security Automation: https://splunkbase.splunk.com
NEW QUESTION # 54
What can an engineer use to capture contextual values from a dashboard and create a drilldown to link to a new search?
Answer: A
Explanation:
Splunk dashboards use tokens to capture and pass contextual values between dashboard elements, user interactions, and drilldown destinations. Therefore, tokens are the mechanism an engineer can use when a user selects a visualization element and the resulting value must be inserted into another search.
A token can represent contextual information derived from an interaction-for example, a selected host, username, source IP, destination, process, or time range. A drilldown can then use that value to construct a more focused search. Conceptually, a dashboard could capture a selected src value and pass it to a destination search so the analyst moves directly from an aggregate visualization to events associated with that source.
This design improves investigation efficiency because the analyst does not need to manually copy values from one interface into another search. It also preserves analytical context as the user moves from summary information to detailed evidence.
Aliases concern alternate representations of fields; environment variables are not the standard dashboard mechanism for passing selected visualization context; and JSON may define dashboard structures but is not itself the contextual-value mechanism being tested.
Study Guide topics: dashboard tokens, drilldowns, contextual searches, analyst workflow, dashboard interactivity, investigation efficiency.
NEW QUESTION # 55
Which of the following should an engineer do as they evaluate their Threat Detection and Incident Response lifecycle?
Answer: A
Explanation:
Threat Detection and Incident Response must be evaluated in the context of the organization ' s business model, operating environment, assets, adversaries, and industry-specific threat landscape . Detection engineering cannot be prioritized effectively using technical indicators alone.
For example, credential-access activity affecting an ordinary laboratory workstation and the same activity affecting a privileged financial system may require substantially different priorities. Similarly, a healthcare organization, financial institution, manufacturer, and cloud provider have different critical systems, regulatory requirements, attack surfaces, and likely adversary objectives.
MITRE ATT & CK provides an excellent taxonomy for adversary tactics and techniques, but ATT & CK itself does not define an organization ' s risk appetite . Risk appetite depends on business governance and tolerance for operational, financial, regulatory, and security impact. Therefore, option B improperly substitutes a threat-behavior framework for a business risk-management decision.
Focusing on the least impactful threat vectors is equally inconsistent with risk-based security engineering.
Effective programs allocate detection and response resources according to realistic threats and organizational consequences.
The question appears in the lifecycle section on page 3 of the supplied material.
Study Guide topics: Threat Detection and Incident Response lifecycle, business context, risk prioritization, threat modeling, MITRE ATT & CK, program maturity.
NEW QUESTION # 56
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