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| Certification Vendor: | Palo Alto Networks |
|---|---|
| Exam Name: | Palo Alto Networks XSIAM Engineer |
| Exam Number: | XSIAM-Engineer |
| Exam Format: | Scenario-based questions, Multiple choice |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 59 |
| Related Certifications: | Palo Alto Networks Certified XSIAM Analyst Palo Alto Networks Certified XDR Engineer Palo Alto Networks Certified XSOAR Engineer |
| Exam Duration: | 90 minutes |
| Passing Score: | 860 (scale 300–1000) |
| Available Languages: | English |
| Exam Price: | $250 USD |
| Recommended Training: | Cortex XSIAM: Security Operations, Integration, and Automation |
| Exam Registration: | Pearson VUE Registration |
| Sample Questions: | Palo Alto Networks XSIAM-Engineer Sample Questions |
| Exam Way: | Online proctored or onsite testing at authorized centers |
| Pre Condition: | Recommended: Knowledge of security operations, SIEM concepts, scripting (Python, SQL, XQL), and network fundamentals; no mandatory prerequisites |
| Official Syllabus URL: | https://www.paloaltonetworks.com/services/education/certification/xsiam-engineer |
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NEW QUESTION # 45
As a Palo Alto Networks XSIAM Engineer, you are tasked with creating a highly specialized ASM rule to identify 'Domain Fronting' attempts originating from internal client machines, targeting known legitimate content delivery networks (CDNs) but with suspicious 'Host' headers pointing to unapproved external domains. This requires deep inspection of HTTP headers. Assume XSIAM can process full HTTP session details. Which XQL construct and data source is most suitable?
Answer: A
Explanation:
Option B is the most appropriate. 'Domain Fronting' specifically manipulates the HTTP Host header. Therefore, 'xdr_http_sessions' is the ideal dataset as it provides parsed HTTP header information. The XQL query accurately filters for traffic to legitimate CDNs and then uses the 'alter' command with a 'case' statement to check if the 'Host:' header content differs from the actual 'dest_address' (the CDN domain). This logic directly identifies the core characteristic of domain fronting. Option A is too high-level (network sessions, not HTTP headers). Option C focuses on DNS, not the HTTP layer. Option D looks at a specific tool's command line, not all HTTP traffic. Option E relies on raw logs, which is inefficient and error-prone for structured data like HTTP headers.
NEW QUESTION # 46
During a rule review, an XSIAM engineer identifies a correlation rule that consistently triggers false positives due to a common, legitimate system process that temporarily matches a suspicious pattern. Simply adding the process name to a global exclusion list is not an option, as the process could still be malicious under different circumstances. How can this specific false positive scenario be mitigated without losing the rule's overall detection capability for actual threats?
Answer: E
Explanation:
Option B is the most precise and effective method. By implementing a conditional exclusion, you can specify exact circumstances under which the legitimate process should NOT trigger an alert, while still allowing the rule to catch instances where the same process might be used maliciously (e.g., if its parent process or command line arguments differ). This maintains the rule's fidelity for true threats while eliminating specific false positives. Options A, C, D, and E are either ineffective, harmful to detection, or merely reactive.
NEW QUESTION # 47
A sub-playbook is configured to loop with a For Each Input. The following inputs are given to the sub- playbook:
Input x: W,X,Y,Z
Input y: a,b,c,d
Input z: 9
Which inputs will be used for the second iteration of the loop?
Answer: D
Explanation:
In a For Each Input loop, each iteration takes the next value from the list inputs while keeping constant inputs unchanged.
On the second iteration:
x = X (second value of W,X,Y,Z)
y = b (second value of a,b,c,d)
z = 9 (constant for all iterations).
So, the values are X, b, 9.
NEW QUESTION # 48
Consider the following Python snippet intended to programmatically configure a custom XSIAM data source for a novel log format that arrives via HTTPS POST. The goal is to define specific extraction rules for 'event id' and 'username' from a JSON payload. Which of the following XSIAM API calls or programmatic steps is missing or incorrectly represented to achieve this specific data source configuration, assuming proper authentication has been established?
Answer: B
Explanation:
Option B correctly identifies the crucial missing element: the 'parser' object within the data source configuration. For custom data sources where you need to define how fields are extracted from a raw payload (especially JSON), XSIAM's API requires a 'parser' definition. This 'parser' typically includes the 'type' of parser (e.g., 'json', 'regex') and the 'mapping' or 'rules' to extract specific fields. The initial snippet's 'fields' array at the top level is for defining expected fields, not extraction rules . Option A correctly points out a common API endpoint naming convention but the primary issue is the payload structure. Option C has incorrect 'type' and mapping syntax. Option D is incorrect; while a UI mapper exists, API-driven configuration for custom parsers is indeed possible. Option E is generally false; while OAuth and service accounts are common, API keys are still widely used for various XSIAM APIs depending on context and setup.
NEW QUESTION # 49
A sophisticated attack involves lateral movement through compromised service accounts. An XSIAM Playbook is triggered by an alert indicating a service account login from an unusual country The Playbook needs to: 1. Validate the country against a trusted list. 2. If untrusted, initiate a password reset for the service account via an external identity management system API. 3. Suspend the service account temporarily. 4. Collect process and network connection data from the affected host using XQL. 5. Create a high-severity incident. Which of the following XSIAM Playbook task sequences and configurations, considering best practices for security and efficiency, would most accurately implement this scenario?
Answer: A
Explanation:
Option B provides the most accurate and secure implementation: 1. 'Load Data' (country list from KV store): Best practice for loading trusted lists securely and efficiently within a playbook. 2. 'Conditional' (country check): For branching based on the validation. 3. "Generic API Call' (password reset): To interact with an external identity management system for resetting passwords. This is more robust and scalable than 'Run Command Line' for external systems. 4. 'Generic API Call' (suspend account via identity system API): Similar to password reset, interacting with an identity system API is the proper way to suspend an account, ensuring centralized management and logging. 'Run Command Line' for suspension could be less secure or less integrated. 5. 'Execute XQL Query': For collecting specific data from XSIAM's rich dataset. 6. 'Create Incident: To log the high-severity event. Option A's 'Run Command Line' for suspension is less ideal than API. Options C, D, E are irrelevant or incomplete for the scenario.
NEW QUESTION # 50
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