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| Section | Weight | Objectives |
|---|
| Measuring and Improving Security Program Effectiveness | 15% | - Security metrics and performance
- 1. Program maturity assessment
- 2. Risk measurement
- 3. Continuous improvement processes
|
| Advanced Incident Response and Management | 10% | - Incident response architecture
- 1. Response workflows
- 2. Incident management optimization
- 3. Investigation processes
|
| Scaling Cybersecurity Defenses and DevSecOps | 15% | - Security architecture at scale
- 1. Enterprise security operations design
- 2. Scalable defense strategies
- 3. DevSecOps integration
|
| Governance, Risk and Compliance | 10% | - Security governance
- 1. Policy alignment
- 2. Risk management frameworks
- 3. Compliance requirements
|
| Advanced Threat Intelligence and Analysis | 5% | - Threat intelligence architecture
- 1. Advanced threat analysis
- 2. Threat intelligence integration
- 3. Threat-informed defense
|
| Advanced Automation and Orchestration | 10% | - SOAR architecture
- 1. Playbook design
- 2. Workflow automation
- 3. Security orchestration
|
| Security Capability Selection, Placement and Configuration | 15% | - Security control architecture
- 1. Control placement strategies
- 2. Capability integration
- 3. Technology selection
|
| Security Data Management | 20% | - Data architecture design
- 1. Security data onboarding and normalization
- 2. Data lifecycle management
- 3. Data quality and governance
|
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Splunk Certified Cybersecurity Defense Architect Sample Questions (Q75-Q80):
NEW QUESTION # 75
What distributed computing model can be used to enable analysis of data closest to the data source for real-time monitoring?
- A. General purpose computing
- B. Supercomputing
- C. Edge computing
- D. Middle computing
Answer: C
Explanation:
Edge computing enables processing and analysis close to where data is generated. This supports real-time monitoring by reducing latency, limiting unnecessary data movement, and allowing faster local detection or response near the data source.
NEW QUESTION # 76
Which of the following best describes how data science, machine learning, behavioral analysis, and AI improve threat detection compared to traditional detection approaches?
- A. They highlight anomalies, relationships, and unique patterns to identify threats.
- B. They rapidly integrate with legacy SIEM infrastructure to identify threats.
- C. They escalate anomalies based on static thresholds to identify threats.
- D. They rely on defined rules and signatures to identify threats.
Answer: A
Explanation:
Data science, machine learning, behavioral analysis, and AI improve detection by identifying anomalies, relationships, and patterns that may not match predefined signatures or static rules.
This helps uncover unusual behavior, emerging threats, and subtle attack activity across large or complex datasets.
NEW QUESTION # 77
Buttercup games has implemented over 100 detections in their SOC. These detections consist mostly of vendor provided signatures and field matching that have been tuned, with a few that have been custom built. What more advanced detection methods should they deploy?
- A. Use an outlier based algorithm
- B. Define breaches of static thresholds
- C. Enrich with asset and identity information
- D. Use automation to pull additional data
Answer: A
Explanation:
An outlier-based algorithm is a more advanced detection method because it uses behavioral or statistical analysis to identify activity that deviates from expected patterns. This moves beyond tuned signatures and field matching into anomaly-based detection, which can help uncover unknown or subtle threats.
NEW QUESTION # 78
Kevin, a security architect, is planning a long-term retention strategy for security logs (e.g.7+ years) for compliance and forensic purposes. Which storage solutions are cost-effective for this requirement and still allow future access? (Choose all that apply.)
- A. SAN with cold storage tiers
- B. SQL data warehouse
- C. High performant SSD storage
- D. Cloud object storage with infrequent access
Answer: A,D
Explanation:
Cloud object storage with infrequent access is cost-effective for long-term log retention because it supports durable, low-cost storage while preserving future retrieval capability. A SAN with cold storage tiers can also support long retention by moving rarely accessed security logs to lower- cost storage while keeping them available for compliance review or forensic investigation.
NEW QUESTION # 79
A Cybersecurity Defense Architect must design log ingestion for a cloud-native environment using AWS. Which combination is most appropriate for near real-time ingestion of CloudTrail logs?
- A. Splunk Add-on for AWS with SQS-based S3 ingestion
- B. Universal forwarder installed on EC2 only
- C. Syslog relay from CloudTrail
- D. Manual CSV export and periodic upload
Answer: A
Explanation:
The Splunk Add-on for AWS supports SQS-based S3 ingestion, which is the recommended, scalable, near real-time method for ingesting CloudTrail logs delivered to S3 buckets.
NEW QUESTION # 80
......
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