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Palo Alto Networks XDR-Engineer Exam Syllabus Topics:

Topic
Details

Topic 1

Detection and Reporting: This section of the exam measures skills of the detection engineer and covers creating detection rules to meet security requirements, including correlation, custom prevention rules, and the use of behavioral indicators of compromise (BIOCs) and indicators of compromise (IOCs). It also assesses configuring exceptions and exclusions, as well as building custom dashboards and reporting templates for effective threat detection and reporting.

Topic 2

Cortex XDR Agent Configuration: This section of the exam measures skills of the XDR engineer and covers configuring endpoint prevention profiles and policies, setting up endpoint extension profiles, and managing endpoint groups. The focus is on ensuring endpoints are properly protected and policies are consistently applied across the organization.

Topic 3

Maintenance and Troubleshooting: This section of the exam measures skills of the XDR engineer and covers managing software component updates for Cortex XDR, such as content, agents, Collectors, and Broker VM. It also includes troubleshooting data management issues like data ingestion and parsing, as well as resolving issues with Cortex XDR components to ensure ongoing system reliability and performance.

Topic 4

Ingestion and Automation: This section of the exam measures skills of the security engineer and covers onboarding various data sources including NGFW, network, cloud, and identity systems. It also includes managing simple automation rules, configuring Broker VM applets and clusters, setting up XDR Collectors, and creating parsing rules for data normalization and automation within the Cortex XDR environment.

Topic 5

Planning and Installation: This section of the exam measures skills of the security engineer and covers the deployment process, objectives, and required resources such as hardware, software, data sources, and integrations for Cortex XDR. It also includes understanding and explaining the deployment and functionality of components like the XDR agent, Broker VM, XDR Collector, and Cloud Identity Engine. Additionally, it assesses the ability to configure user roles, permissions, and access controls, as well as knowledge of data retention and compute unit considerations.

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Palo Alto Networks XDR Engineer Sample Questions (Q15-Q20):

NEW QUESTION # 15
A new parsing rule is created, and during testing and verification, all the logs for which field data is to be parsed out are missing. All the other logs from this data source appear as expected. What may be the cause of this behavior?

A. The Broker VM is offline
B. The filter stage is dropping the logs
C. The parsing rule corrupted the database
D. The XDR Collector is dropping the logs

Answer: B

Explanation:
In Cortex XDR,parsing rulesare used to extract and normalize fields from raw log data during ingestion, ensuring that the data is structured for analysis and correlation. The parsing process includes stages such as filtering, parsing, and mapping. If logs for which field data is to be parsed out are missing, while other logs from the same data source are ingested as expected, the issue likely lies within the parsing rule itself, specifically in the filtering stage that determines which logs are processed.
* Correct Answer Analysis (C):The filter stage is dropping the logsis the most likely cause. Parsing rules often include afilter stagethat determines which logs are processed based on specific conditions (e.
g., log content, source, or type). If the filter stage of the new parsing rule is misconfigured (e.g., using an incorrect condition like log_type != expected_type or a regex that doesn't match the logs), it may drop the logs intended for parsing, causing them to be excluded from the ingestion pipeline. Since other logs from the same data source are ingested correctly, the issue is specific to the parsing rule's filter, not a broader ingestion problem.
* Why not the other options?
* A. The Broker VM is offline: If the Broker VM were offline, it would affect all log ingestion from the data source, not just the specific logs targeted by the parsing rule. The question states that other logs from the same data source are ingested as expected, so the Broker VM is likely operational.
* B. The parsing rule corrupted the database: Parsing rules operate on incoming logs during ingestion and do not directly interact with or corrupt the Cortex XDR database. This is an unlikely cause, and database corruption would likely cause broader issues, not just missing specific logs.
* D. The XDR Collector is dropping the logs: The XDR Collector forwards logs to Cortex XDR, and if it were dropping logs, it would likely affect all logs from the data source, not just those targeted by the parsing rule. Since other logs are ingested correctly, the issue is downstream in the parsing rule, not at the collector level.
Exact Extract or Reference:
TheCortex XDR Documentation Portalexplains parsing rule behavior: "The filter stage in a parsing rule determines which logs are processed; misconfigured filters can drop logs, causing them to be excluded from ingestion" (paraphrased from the Data Ingestion section). TheEDU-260: Cortex XDR Prevention and Deploymentcourse covers parsing rule troubleshooting, stating that "if specific logs are missing during parsing, check the filter stage for conditions that may be dropping the logs" (paraphrased from course materials). ThePalo Alto Networks Certified XDR Engineer datasheetincludes "data ingestion and integration" as a key exam topic, encompassing parsing rule configuration and troubleshooting.
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 # 16
When onboarding a Palo Alto Networks NGFW to Cortex XDR, what must be done to confirm that logs are being ingested successfully after a device is selected and verified?

A. Wait for an incident that involves the NGFW to populate
B. Conduct an XQL query for NGFW log data
C. Confirm that the selected device has a valid certificate
D. Retrieve device certificate from NGFW dashboard

Answer: B

Explanation:
When onboarding aPalo Alto Networks Next-Generation Firewall (NGFW)to Cortex XDR, the process involves selecting and verifying the device to ensure it can send logs to Cortex XDR. After this step, confirming successful log ingestion is critical to validate the integration. The most direct and reliable method to confirm ingestion is to query the ingested logs usingXQL (XDR Query Language), which allows the engineer to search for NGFW log data in Cortex XDR.
* Correct Answer Analysis (A):Conduct an XQL query for NGFW log datais the correct action.
After onboarding, the engineer can run an XQL query such as dataset = panw_ngfw_logs | limit 10 to check if NGFW logs are present in Cortex XDR. This confirms that logs are being successfully ingested and stored in the appropriate dataset, ensuring the integration is working as expected.
* Why not the other options?
* B. Wait for an incident that involves the NGFW to populate: Waiting for an incident is not a reliable or proactive method to confirm log ingestion. Incidents depend on detection rules and may not occur immediately, even if logs are beingingested.
* C. Confirm that the selected device has a valid certificate: While a valid certificate is necessary during the onboarding process (e.g., for secure communication), this step is part of the verification process, not a method to confirm log ingestion after verification.
* D. Retrieve device certificate from NGFW dashboard: Retrieving the device certificate from the NGFW dashboard is unrelated to confirming log ingestion in Cortex XDR. Certificates are managed during setup, not for post-onboarding validation.
Exact Extract or Reference:
TheCortex XDR Documentation Portalexplains NGFW log ingestion validation: "To confirm successful ingestion of Palo Alto Networks NGFW logs, run an XQL query (e.g., dataset = panw_ngfw_logs) to verify that log data is present in Cortex XDR" (paraphrased from the Data Ingestion section). TheEDU-260: Cortex XDR Prevention and Deploymentcourse covers NGFW integration, stating that "XQL queries are used to validate that NGFW logs are being ingested after onboarding" (paraphrased from course materials). ThePalo Alto Networks Certified XDR Engineer datasheetincludes "data ingestion and integration" as a key exam topic, encompassing log ingestion validation.
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 # 17
An XDR engineer is creating a correlation rule to monitor login activity on specific systems. When the activity is identified, an alert is created. The alerts are being generated properly but are missing the username when viewed. How can the username information be included in the alerts?

A. Select "Initial Access" in the MITRE ATT&CK mapping to include the username
B. Add a mapping for the username field in the alert fields mapping
C. Update the query in the correlation rule to include the username field
D. Add a drill-down query to the alert which pulls the username field

Answer: B

Explanation:
In Cortex XDR,correlation rulesare used to detect specific patterns or behaviors (e.g., login activity) by analyzing ingested data and generating alerts when conditions are met. For an alert to include specific fields likeusername, the field must be explicitly mapped in thealert fields mappingconfiguration of the correlation rule. This mapping determines which fields from theunderlying dataset are included in the generated alert's details.
In this scenario, the correlation rule is correctly generating alerts for login activity, but theusernamefield is missing. This indicates that the correlation rule's query may be identifying the relevant events, but the usernamefield is not included in the alert's output fields. To resolve this, the engineer must update thealert fields mappingin the correlation rule to explicitly include theusernamefield, ensuring it appears in the alert details when viewed.
* Correct Answer Analysis (C):Adding a mapping for theusernamefield in thealert fields mapping ensures that the field is extracted from the dataset and included in the alert's metadata. This is done in the correlation rule configuration, where administrators can specify which fields to include in the alert output.
* Why not the other options?
* A. Select "Initial Access" in the MITRE ATT&CK mapping to include the username:
Mapping to a MITRE ATT&CK technique like "Initial Access" defines the type of attack or behavior, not specific fields likeusername. This does not address the missing field issue.
* B. Update the query in the correlation rule to include the username field: While the correlation rule's query must reference theusernamefield to detect relevant events, including it in the query alone does not ensure it appears in the alert's output. Thealert fields mappingis still required.
* D. Add a drill-down query to the alert which pulls the username field: Drill-down queries are used for additional investigation after an alert is generated, not for including fields in the alert itself. This does not solve the issue of missingusernamein the alert details.
Exact Extract or Reference:
TheCortex XDR Documentation Portaldescribes correlation rule configuration: "To include specific fields in generated alerts, configure the alert fields mapping in the correlation rule to map dataset fields, such as username, to the alert output" (paraphrased from the Correlation Rules section). TheEDU-262: Cortex XDR Investigation and Responsecourse covers detection engineering, stating that "alert fields mapping determines which data fields are included in alerts generated by correlation rules" (paraphrased from course materials). ThePalo Alto Networks Certified XDR Engineer datasheetincludes "detection engineering" as a key exam topic, encompassing correlation rule configuration.
References:
Palo Alto Networks Cortex XDR Documentation Portal: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 # 18
Which method will drop undesired logs and reduce the amount of data being ingested?

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

Answer: A

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 # 19
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. Left
B. Inner
C. Right
D. Outer

Answer: A

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 # 20
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