Certification exam guide
A Google Cloud Certified Professional Security Operations Engineer detects, monitors, analyzes, investigates, and responds to security threats against workloads, endpoints, and infrastructure. This individual uses Google Cloud resources to protect an enterprise environment and is proficient in writing detection rules, log prioritization and ingestion, orchestration, and response automation. Further, this individual has experience leveraging posture and threat intelligence for detection and response.
This exam assesses your knowledge of performing tasks in Google Security Operations (SecOps) and Security Command Center (SCC). For more information on these platforms, please refer to the Google SecOps documentation and the SCC documentation.
Section 1: Platform operations (~14% of the exam)
1.1 Enhancing detection and response. Considerations include:
● Prioritizing telemetry sources (e.g., Security Command Center [SCC], Google Security Operations [SecOps], GTI, Cloud IDS) to detect incidents or misconfigurations within an enterprise environment
● Integrating multiple tools (e.g., SCC, Google SecOps, GTI, Cloud IDS, downstream third-party system) in the security architecture to enhance detection capabilities
● Justifying the use of tools with overlapping capabilities based on a set of requirements
● Evaluating the effectiveness of existing tools to identify gaps in coverage and mitigate potential threats
● Evaluating automation and cloud-based tools to enhance existing detection and response processes
1.2 Configuring access. Considerations include:
● Configuring user and service account authentication to security tools (e.g., SCC, Google SecOps)
● Configuring user and service account authorization for feature access using IAM roles and permissions
● Configuring user and service account authorization for data access using IAM roles and permissions
● Configuring and analyzing audit logs (e.g., Cloud Audit Logs, data access logs) for the solution
● Configuring API access for automations within security tools (e.g., service accounts, API keys, SCC, Google SecOps, GTI)
● Provisioning identities using Workforce Identity Federation
Section 2: Data management (~14% of the exam)
2.1 Ingesting logs for security tooling. Considerations include:
● Determining approaches for data ingestion within security tools (e.g., SCC, Google SecOps)
● Configuring an ingestion tool or features within security tools (e.g., SCC, Google SecOps)
● Assessing required logs for detection and response, including automated sources, within security tools (e.g., SCC Event Threat Detection, Google SecOps)
● Evaluating parsers for data ingestion in Google SecOps
● Configuring parser modifications or extensions in Google SecOps
● Evaluating data normalization techniques from log sources in Google SecOps
● Evaluating new labels for data ingestion
● Managing log and ingestion costs
2.2 Identifying a baseline of user, asset, and entity context. Considerations include:
● Identifying relevant threat intelligence information in the enterprise environment
● Differentiating event and entity data log sources (e.g., Cloud Audit Logs, Active Directory organizational context)
● Evaluating event and entity data matches for enrichment by using aliasing fields
Section 3: Threat hunting (~19% of the exam)
3.1 Performing threat hunting across environments. Considerations include:
● Developing queries to search across environment logs to identify anomalous activity
● Analyzing user behavior to identify anomalous activity
● Investigating the network, endpoints, and services to identify threat patterns or indicators of compromise (IOCs) using Google Cloud tools (e.g., Logs Explorer, Log Analytics, BigQuery, Google SecOps)
● Collaborating with the incident response team to identify active threats in the environment
● Developing hypotheses based on behavior, threat intel, posture, and incident data (e.g., SCC, GTI) 3.2 Leveraging threat intelligence for threat hunting. Considerations include:
● Searching for IOCs within historical logs
● Identifying new attack patterns and techniques in real time using threat intelligence and risk assessments (e.g., GTI, detection rules, SCC toxic combinations)
● Analyzing entity risk score to identify anomalous behavior
● Comparing and performing retrohunt of historical event data with newly enriched logs (e.g., Google SecOps rules engine, BigQuery, Cloud Logging)
● Searching proactively for underlying threats using threat intelligence (e.g., GTI, detection rules)
Section 4: Detection engineering (~22% of the exam)
4.1 Developing and implementing mechanisms to detect risks and identify threats. Considerations include:
● Reconciling threat intelligence with user and asset activity
● Analyzing logs and events to identify anomalous activity
● Assessing suspicious behavior patterns by using detection rules and searches across various timelines
● Designing detection rules that use risk values (e.g., Google SecOps reference lists) to identify threats matching risk profiles
● Discovering anomalous behavior of assets or users, and assigning risk values to the detections (e.g., Google SecOps Risk Analytics, curated detection rules)
● Designing detection rules to discover posture or risk profile changes within the environment (e.g., SCC Security Health Analytics [SHA], SCC posture management, Google SecOps)
● Identifying new or low prevalence processes, domains, and IP addresses that do not appear in threat intelligence sources using various methods (e.g., writing YARA-L rules, dashboards)
● Assessing how to use entity/context data within detection rules to improve their accuracy (e.g., Google SecOps entity graph)
● Configuring SCC Event Threat Detection custom detectors for IOCs 4.2 Leveraging threat intelligence for detection.
Considerations include:
● Scoring alerts based on the risk level of IOCs
● Using latest IOCs to search within ingested security telemetry
● Measuring the frequency of repetitive alerts to identify and reduce false positives Section 5: Incident response (~21% of the exam) 5.1 Containing and investigating security incidents. Considerations include:
● Collecting evidence on the scope of the incident, including forensic images and artifacts
● Observing and analyzing alerts related to the incident using security tooling (e.g., SCC, Google SecOps)
● Analyzing the scope of the incident using security tooling (e.g., Logs Explorer, Log Analytics, BigQuery, Cloud Logging, Cloud Monitoring)
● Collaborating with other engineering teams for detection and long-term remediation efforts
● Isolating affected services and processes to prevent further damage and spread of attack
● Analyzing identified artifacts based on forensic analysis (e.g., Hash, IP, URL, Binaries) (GTI)
● Performing root cause analysis using security tools (e.g., SCC, Google SecOps SIEM) 5.2 Building, implementing, and using response playbooks. Considerations include:
● Determining the appropriate response steps for automation
● Prioritizing high-value enrichments based on threat profiles
● Evaluating appropriate integrations to be leveraged by playbooks
● Designing new processes in response to newly identified attack patterns from recent incidents
● Recommending new orchestrations and automation playbooks based on gaps in the current implementation (e.g., Google SecOps SOAR)
● Implementing mechanisms to notify analysts and stakeholders of incidents 5.3 Implementing the case management lifecycle. Considerations include:
● Assigning cases into appropriate response stages
● Implementing efficient workflows for case escalation
● Assessing the effectiveness of case handoffs
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Section 6: Observability (~10% of the exam)
6.1 Developing and maintaining dashboards and reports to provide insights. Considerations include:
● Identifying key security analytics (e.g., metrics, KPIs, trends)
● Implementing dashboards to visualize security telemetry, ingestion metrics, detections, alerts, and IOCs (e.g., Google SecOps SOAR, SIEM, Looker Studio)
● Generating and customizing reports (e.g., Google SecOps SOAR, SIEM) 6.2 Configuring health monitoring and alerting. Considerations include:
● Identifying important metrics for health monitoring and alerts
● Creating dashboards that centralize metrics
● Creating alerts with thresholds for specific metrics
● Configuring notifications using Google Cloud tools (e.g., Cloud Monitoring)
● Identifying health issues using Google Cloud tools (e.g., Cloud Logging)
● Configuring silent source detection
Sample Question and Answers
QUESTION 1
Which phase of the cloud data life cycle involves activities such as data categorization and classification, including data labeling, marking, tagging, and assigning metadata?
A. Store
B. Use
C. Destroy
D. Create
Answer: D
Explanation:
The cloud data life cycle defines distinct stages that data goes through from its origin until its
disposal. The Create phase is the very first stage, and this is where data is generated or captured by
systems, applications, or users. At this point, data does not yet have context for storage or use, so it
must be appropriately categorized and classified. Activities like labeling, marking, tagging, and
assigning metadata are critical because they establish the foundation for enforcing controls
throughout the rest of the life cycle.
Classification ensures that data is aligned with sensitivity levels, regulatory requirements, and
business value. For example, financial records may be labeled oeconfidential while general marketing
content may be marked oepublic. These distinctions guide how encryption, access controls, and
monitoring will be applied in subsequent phases such as storage, sharing, or use.
According to industry frameworks, starting security at the Create phase ensures that controls oefollow
the data across environments. Without proper classification at creation, organizations risk
mismanaging sensitive data downstream.
QUESTION 2
Which phase of the cloud data life cycle involves the process of crypto-shredding?
A. Destroy
B. Create
C. Archive
D. Store
Answer: A
Explanation:
The Destroy phase of the cloud data life cycle is where information is permanently removed from
systems. A common technique in cloud environments for this phase is crypto-shredding (or
cryptographic erasure). Rather than physically destroying the media, crypto-shredding involves
deleting or revoking encryption keys used to protect the data. Once those keys are destroyed, the
encrypted data becomes mathematically unrecoverable, even if the underlying storage media remains intact.
This method is particularly useful in cloud environments where storage is virtualized and hardware
cannot easily be physically destroyed. Crypto-shredding provides compliance-friendly assurance that
sensitive data such as personally identifiable information (PII), financial data, or healthcare records
cannot be accessed after retention periods expire or contractual obligations end.
By incorporating crypto-shredding into the Destroy phase, organizations align with standards for
secure data sanitization. This ensures legal defensibility during audits and e-discovery and
demonstrates proper lifecycle governance. The emphasis is on making data inaccessible while still
maintaining operational efficiency and environmental responsibility.
QUESTION 3
In most redundant array of independent disks (RAID) configurations, data is stored across different disks. Which method of storing data is described?
A. Striping
B. Archiving
C. Mapping
D. Crypto-shredding
Answer: A
Explanation:
The method described is striping, which is a technique used in RAID configurations to improve
performance and distribute risk. Striping involves splitting data into smaller segments and writing
those segments across multiple disks simultaneously. For example, if a file is divided into four parts,
each part is written to a separate disk in the RAID array.
This parallelism enhances input/output (I/O) performance because multiple drives can be accessed
at once. It also provides resilience depending on the RAID level. While striping by itself (RAID 0)
increases performance but not redundancy, when combined with mirroring or parity (e.g., RAID 5 or
RAID 10), it offers both speed and fault tolerance.
The purpose of striping in the data management context is to optimize how data is stored, accessed,
and protected. It is fundamentally different from archiving, mapping, or crypto-shredding, as those
serve different objectives (long-term storage, logical placement, or secure deletion). Striping is
central to high-performance storage systems and supports availability in mission-critical environments.
QUESTION 4
As part of training to help the data center engineers understand different attack vectors that affect
the infrastructure, they work on a set of information about access and availability attacks that was
presented. Part of the labs requires the engineers to identify different threat vectors and their names.
Which threat prohibits the use of data by preventing access to it?
A. Brute force
B. Encryption
C. Rainbow tables
D. Denial of service
Answer: D
Explanation:
The described threat is a Denial of Service (DoS) attack. In security contexts, a DoS attack aims to
make a system, application, or data unavailable to legitimate users by overwhelming resources.
Unlike brute force or rainbow table attacks, which target authentication mechanisms, or encryption,
which is a defensive control, DoS focuses on disrupting availability”the oeA in the Confidentiality,
Integrity, Availability (CIA) triad.
DoS can be executed in many ways: flooding a network with traffic, exhausting server memory, or
overwhelming application processes. When scaled by multiple coordinated systems, it becomes a
Distributed Denial of Service (DDoS) attack. In either case, the effect is the same”authorized users
cannot access critical data or services.
For cloud environments, where service uptime is crucial, DoS protections such as rate limiting, autoscaling,
and upstream filtering are essential. Training data center engineers to recognize DoS helps
them understand the importance of resilience strategies and ensures continuity planning includes
availability safeguards.
QUESTION 5
An engineer has been given the task of ensuring all of the keys used to encrypt archival data are
securely stored according to industry standards. Which location is a secure option for the engineer to
store encryption keys for decrypting data?
A. A repository that is made private
B. An escrow that is kept separate from the data it is tied to
C. An escrow that is kept local to the data it is tied to
D. A repository that is made public
Answer: B
Explanation:
Industry best practice requires that encryption keys are stored separately from the data they protect.
This ensures that if the data storage system is compromised, attackers cannot immediately decrypt
sensitive information. The use of a secure escrow system is a recognized approach.
An escrow provides controlled storage for encryption keys, ensuring they are only accessible by
authorized processes and not co-located with the protected data. Keeping keys oelocal to the data
creates a single point of failure. A public or private repository without specialized protection
mechanisms would also be insufficient due to risks of insider threats or misconfiguration.
By placing keys in an independent escrow system, the organization enforces separation of duties,
strengthens defense-in-depth, and aligns with cryptographic standards from NIST and ISO.
This practice is vital when dealing with archival data, where long-term confidentiality must be preserved
even as systems evolve.