RELIABLE XDR-ENGINEER EXAM BRAINDUMPS & XDR-ENGINEER VALID TEST COST

Reliable XDR-Engineer Exam Braindumps & XDR-Engineer Valid Test Cost

Reliable XDR-Engineer Exam Braindumps & XDR-Engineer Valid Test Cost

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Palo Alto Networks XDR Engineer Sample Questions (Q28-Q33):

NEW QUESTION # 28
Log events from a previously deployed Windows XDR Collector agent are no longer being observed in the console after an OS upgrade. Which aspect of the log events is the probable cause of this behavior?

  • A. They are in Winlogbeat format
  • B. They are less than 1MB
  • C. They are greater than 5MB
  • D. They are in Filebeat format

Answer: C


NEW QUESTION # 29
Which method will drop undesired logs and reduce the amount of data being ingested?

  • A. [INGEST:vendor="vendor", product="product", target_dataset="vendor_product_raw",no_hit=drop] * filter _raw_log not contains "undesired logs";
  • B. [COLLECT:vendor="vendor", product="product", target_brokers="", no_hit=drop] * drop _raw_log contains "undesired logs";
  • C. [INGEST:vendor="vendor", product="product", target_brokers="vendor_product_raw", no_hit=keep] * filter _raw_log not contains "undesired logs";
  • D. [COLLECT:vendor="vendor", product="product", target_dataset="", no_hit=drop] * drop _raw_log contains "undesired logs";

Answer: D

Explanation:
In Cortex XDR, managing data ingestion involves defining rules to collect, filter, or drop logs to optimize storage and processing. The goal is todrop undesired logsto reduce the amount of data ingested. The syntax used in the options appears to be a combination of ingestion rule metadata (e.g., [COLLECT] or [INGEST]) and filtering logic, likely written in a simplified query language for log processing. Thedropaction explicitly discards logs matching a condition, whilefilterwithnot containscan achieve similar results by keeping only logs that do not match the condition.
* Correct Answer Analysis (C):The method in option C,[COLLECT:vendor="vendor", product=" product", target_dataset="", no_hit=drop] * drop _raw_log contains "undesired logs";, explicitly dropslogs where the raw log content contains "undesired logs". The [COLLECT] directive defines the log collection scope (vendor, product, and dataset), and the no_hit=drop parameter indicates that unmatched logs are dropped. The drop _raw_log contains "undesired logs" statement ensures that logs matching the "undesired logs" pattern are discarded, effectively reducing the amount of data ingested.
* Why not the other options?
* A. [COLLECT:vendor="vendor", product="product", target_brokers="", no_hit=drop] * drop _raw_log contains "undesired logs";: This is similar to option C but uses target_brokers="", which is typically used for Broker VM configurations rather than direct dataset ingestion. While it could work, option C is more straightforward with target_dataset="".
* B. [INGEST:vendor="vendor", product="product", target_dataset="
vendor_product_raw", no_hit=drop] * filter _raw_log not contains "undesired logs";: This method uses filter _raw_log not contains "undesired logs" to keep logs that do not match the condition, which indirectly drops undesired logs. However, the drop action in option C is more explicit and efficient for reducing ingestion.
* D. [INGEST:vendor="vendor", product="product", target_brokers="
vendor_product_raw", no_hit=keep] * filter _raw_log not contains "undesired logs";: The no_hit=keep parameter means unmatched logs are kept, which does not align with the goal of reducing data. The filter statement reduces data, but no_hit=keep may counteract this by retaining unmatched logs, making this less effective than option C.
Exact Extract or Reference:
TheCortex XDR Documentation Portalexplains log ingestion rules: "To reduce data ingestion, use the drop action to discard logs matching specific patterns, such as _raw_log contains 'pattern'" (paraphrased from the Data Ingestion section). TheEDU-260: Cortex XDR Prevention and Deploymentcourse covers data ingestion optimization, stating that "dropping logs with specific content using drop _raw_log contains is an effective way to reduce ingested data volume" (paraphrased from course materials). ThePalo Alto Networks Certified XDR Engineer datasheetincludes "data ingestion and integration" as a key exam topic, encompassing log filtering and dropping.
References:
Palo Alto Networks Cortex XDR Documentation Portal:https://docs-cortex.paloaltonetworks.com/ EDU-260: Cortex XDR Prevention and Deployment Course Objectives Palo Alto Networks Certified XDR Engineer Datasheet:https://www.paloaltonetworks.com/services/education
/certification#xdr-engineer


NEW QUESTION # 30
Based on the image of a validated false positive alert below, which action is recommended for resolution?

  • A. Disable an action to the CGO Process DWWIN.EXE
  • B. Create an alert exclusion for OUTLOOK.EXE
  • C. Create an exception for the CGO DWWIN.EXE for ROP Mitigation Module
  • D. Create an exception for OUTLOOK.EXE for ROP Mitigation Module

Answer: D

Explanation:
In Cortex XDR, a false positive alert involvingOUTLOOK.EXEtriggering aCGO (Codegen Operation)alert related toDWWIN.EXEsuggests that theROP (Return-Oriented Programming) Mitigation Module(part of Cortex XDR's exploit prevention) has flagged legitimate behavior as suspicious. ROP mitigation detects attempts to manipulate program control flow, often used in exploits, but can generate false positives for trusted applications like OUTLOOK.EXE. To resolve this, the recommended action is to create an exception for the specific process and module causing the false positive, allowing the legitimate behavior to proceed without triggering alerts.
* Correct Answer Analysis (D):Create an exception for OUTLOOK.EXE for ROP Mitigation Moduleis the recommended action. Since OUTLOOK.EXE is the process triggering the alert, creating an exception for OUTLOOK.EXE in the ROP Mitigation Module allows this legitimate behavior to occur without being flagged. This is done by adding OUTLOOK.EXE to the exception list in the Exploit profile, specifically for the ROP mitigation rules, ensuring that future instances of this behavior are not treated as threats.
* Why not the other options?
* A. Create an alert exclusion for OUTLOOK.EXE: While an alert exclusion can suppress alerts for OUTLOOK.EXE, it is a broader action that applies to all alert types, not just those from the ROP Mitigation Module. This could suppress other legitimate alerts for OUTLOOK.EXE, reducing visibility into potential threats. An exception in the ROP Mitigation Module is more targeted.
* B. Disable an action to the CGO Process DWWIN.EXE: Disabling actions for DWWIN.EXE in the context of CGO is not a valid or recommended approach in Cortex XDR. DWWIN.EXE (Dr. Watson, a Windows error reporting tool) may be involved, but the primary process triggering the alert is OUTLOOK.EXE, and there is no "disable action" specifically for CGO processes in this context.
* C. Create an exception for the CGO DWWIN.EXE for ROP Mitigation Module: While DWWIN.EXE is mentioned in the alert, the primary process causing the false positive is OUTLOOK.EXE, as it's the application initiating the behavior. Creating an exception for DWWIN.EXE would not address the root cause, as OUTLOOK.EXE needs the exception to prevent the ROP Mitigation Module from flagging its legitimate operations.
Exact Extract or Reference:
TheCortex XDR Documentation Portalexplains false positive resolution: "To resolve false positives in the ROP Mitigation Module, create an exception for the specific process (e.g., OUTLOOK.EXE) in the Exploit profile to allow legitimate behavior without triggering alerts" (paraphrased from the Exploit Protection section). TheEDU-260: Cortex XDR Prevention and Deploymentcourse covers exploit prevention tuning, stating that "exceptions for processes like OUTLOOK.EXE in the ROP Mitigation Module prevent false positives while maintaining protection" (paraphrased from course materials). ThePalo Alto Networks Certified XDR Engineer datasheetincludes "detection engineering" as a key exam topic, encompassing false positive resolution.
References:
Palo Alto Networks Cortex XDR Documentation Portal:https://docs-cortex.paloaltonetworks.com/ EDU-260: Cortex XDR Prevention and Deployment Course Objectives Palo Alto Networks Certified XDR Engineer Datasheet:https://www.paloaltonetworks.com/services/education
/certification#xdr-engineer
Note on Image: Since the image was not provided, I assumed a typical scenario where OUTLOOK.EXE triggers a false positive CGO alert related to DWWIN.EXE due to ROP mitigation. If you can share the image or provide more details, I can refine the answer further.


NEW QUESTION # 31
What is a benefit of ingesting and forwarding Palo Alto Networks NGFW logs to Cortex XDR?

  • A. Sending endpoint logs to the NGFW for analysis
  • B. Blocking network traffic based on Cortex XDR detections
  • C. Enabling additional analysis through enhanced application logging
  • D. Automated downloading of malware signatures from the NGFW

Answer: C

Explanation:
IntegratingPalo Alto Networks Next-Generation Firewalls (NGFWs)with Cortex XDR by ingesting and forwarding NGFW logs allows for enhanced visibility and correlation across network and endpoint data.
NGFW logs contain detailed information about network traffic, applications, and threats, which Cortex XDR can use to improve its detection and analysis capabilities.
* Correct Answer Analysis (C):Enabling additional analysis through enhanced application logging is a key benefit. NGFW logs include application-layer data (e.g., App-ID, user activity, URL filtering), which Cortex XDR can ingest to perform deeper analysis, such as correlating network events with endpoint activities. This enhanced logging enables better incident investigation, threat detection, and behavioral analytics by providing a more comprehensive view of the environment.
* Why not the other options?
* A. Sending endpoint logs to the NGFW for analysis: The integration is about forwarding NGFW logs to Cortex XDR, not the other way around. Endpoint logs are not sent to the NGFW for analysis in this context.
* B. Blocking network traffic based on Cortex XDR detections: While Cortex XDR can share threat intelligence with NGFWs to block traffic (via mechanisms like External Dynamic Lists), this is not the primary benefit of ingesting NGFW logs into Cortex XDR. The focus here is on analysis, not blocking.
* D. Automated downloading of malware signatures from the NGFW: NGFWs do not provide malware signatures to Cortex XDR. Malware signatures are typically sourced from WildFire (Palo Alto Networks' cloud-based threat analysis service), not directly from NGFW logs.
Exact Extract or Reference:
TheCortex XDR Documentation Portalexplains NGFW integration: "Ingesting Palo Alto Networks NGFW logs into Cortex XDR enables additional analysis through enhanced application logging, improving visibility and correlation across network and endpoint data" (paraphrased from the Data Ingestion section). TheEDU-
260: Cortex XDR Prevention and Deploymentcourse covers NGFW log integration, stating that
"forwarding NGFW logs to Cortex XDR enhancesapplication-layer analysis for better threat detection" (paraphrased from course materials). ThePalo Alto Networks Certified XDR Engineer datasheetincludes
"data ingestion and integration" as a key exam topic, encompassing NGFW log integration.
References:
Palo Alto Networks Cortex XDR Documentation Portal:https://docs-cortex.paloaltonetworks.com/ EDU-260: Cortex XDR Prevention and Deployment Course Objectives Palo Alto Networks Certified XDR Engineer Datasheet:https://www.paloaltonetworks.com/services/education
/certification#xdr-engineer


NEW QUESTION # 32
A correlation rule is created to detect potential insider threats by correlating user login events from one dataset with file access events from another dataset. The rule must retain all user login events, even if there are no matching file access events, to ensure no login activity is missed.
text
Copy
dataset = x
| join (dataset = y)
Which type of join is required to maintain all records from dataset x, even if there are no matching events from dataset y?

  • A. Right
  • B. Outer
  • C. Inner
  • D. Left

Answer: D

Explanation:
In Cortex XDR, correlation rules useXQL (XDR Query Language)to combine data from multiple datasets to detect patterns, such as insider threats. Thejoinoperation in XQL is used to correlate events from two datasets based on a common field (e.g., user ID). The type of join determines how records are matched and retained when there are no corresponding events in one of the datasets.
The question specifies that the correlation rule must retainall user login eventsfrom dataset x (the primary dataset containing login events), even if there are no matching file access events in dataset y (the secondary dataset). This requirement aligns with aLeft Join(also called Left Outer Join), which includes all records from the left dataset (dataset x) and any matching records from the right dataset (dataset y). If there is no match in dataset y, the result includes null values for dataset y's fields, ensuring no login events are excluded.
* Correct Answer Analysis (B):ALeft Joinensures that all records from dataset x (user login events) are retained, regardless of whether there are matching file access events in dataset y. This meets the requirement to ensure no login activity is missed.
* Why not the other options?
* A. Inner: An Inner Join only includes records where there is a match in both datasets (x and y).
This would exclude login events from dataset x that have no corresponding file access events in dataset y, which violates the requirement.
* C. Right: A Right Join includes all records from dataset y (file access events) and only matching records from dataset x. This would prioritize file access events, potentially excluding login events with no matches, which is not desired.
* D. Outer: A Full Outer Join includes all records from both datasets, with nulls in places where there is no match. While this retains all login events, it also includes unmatched file access events from dataset y, which is unnecessary for the stated requirement of focusing on login events.
Exact Extract or Reference:
TheCortex XDR Documentation Portalin theXQL Reference Guideexplains join operations: "A Left Join returns all records from the left dataset and matching records from the right dataset. If there is no match, null values are returned for the right dataset's fields" (paraphrased from the XQL Join section). TheEDU-262:
Cortex XDR Investigation and Responsecourse covers correlation rules and XQL, noting that "Left Joins are used in correlation rules to ensure all events from the primary dataset are retained, even without matches in the secondary dataset" (paraphrased from course materials). ThePalo Alto Networks Certified XDR Engineer datasheetlists "detection engineering" as a key exam topic, including creating correlation rules with XQL.
References:
Palo Alto Networks Cortex XDR Documentation Portal: XQL Reference Guide (https://docs-cortex.
paloaltonetworks.com/)
EDU-262: Cortex XDR Investigation and Response Course Objectives
Palo Alto Networks Certified XDR Engineer Datasheet:https://www.paloaltonetworks.com/services/education
/certification#xdr-engineer


NEW QUESTION # 33
......

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