Security-Operations-Engineer Exam Dumps - Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam

This YARA-L rule is designed to correlate a real-time event (a DNS query, $dns) with known-bad indicators stored in the Google SecOps entity graph ($ioc). The code must correctly filter the entity graph to find the specific indicators from the custom MISP feed.

Two filters are required:

$ioc.graph.metadata.entity_type = "DOMAIN_NAME": This line is essential to filter the entity graph for IoCs that are domains. The rule is trying to match a DNS query ($dns_query) to a known C2 domain, so the entity type must be DOMAIN_NAME.

$ioc.graph.metadata.source_type = "ENTITY_CONTEXT": This is the key differentiator. The Google SecOps entity graph has multiple context sources. GLOBAL_CONTEXT (Option B) is for threat intelligence provided by Google (e.g., Google Threat Intelligence, Mandiant). DERIVED_CONTEXT (Option C) is for context inferred from UDM events. The prompt explicitly states the IoC feed is the organization's own "threat intelligence feed... ingested... with... MISP." This type of customer-provided, third-party intelligence is classified as ENTITY_CONTEXT. Adding this line ensures the rule only uses the custom MISP feed for its IoC data, as intended.

The other lines in the $ioc block, such as product_name = "MISP", further refine this ENTITY_CONTEXT search.

(Reference: Google Cloud documentation, "YARA-L 2.0 language syntax"; "Context-aware detections with entity graph"; "Populate the entity graph")

Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:

To reduce false positives in Threat Intelligence (TI) matching rules, the standard practice is to filter based on the confidence or severity of the threat indicator. Threat feeds often contain "noisy" indicators (like IP addresses associated with low-risk spam or scanning).

In Google Security Operations, entity context (such as TI data) is accessed in YARA-L using the graph variable. The UDM (Unified Data Model) field structure for threat severity within an entity is metadata.threat.severity.

By modifying the rule to include a condition checking graph.metadata.threat.severity, you ensure the rule only triggers on indicators that the intelligence source has deemed "High" or "Critical," thereby filtering out low-fidelity noise.

Option B correctly applies this logic: $graph.metadata.threat.severity = "CRITICAL" or $graph.metadata.threat.severity = "HIGH".

Option D (Adjusting the match window) only changes the frequency of grouping, not the criteria for what constitutes a threat, so the false positive IP would still trigger an alert (just once per day).

Option C refers to risk_score, which is often a calculated aggregate, whereas threat.severity is the direct attribute from the TI feed (MISP) needed for this specific tuning.