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Palo Alto Networks XSIAM Engineer Sample Questions (Q88-Q93):
NEW QUESTION # 88
A SOC needs to automate the 'containment' phase of incident response for critical endpoints. This involves isolating the affected endpoint from the network. The current endpoint security solution (ESX) has an API for network isolation, but it requires a dynamically generated authentication token for each request, which expires every 5 minutes. The XSIAM playbook must successfully acquire this token and use it for the isolation command. How should the XSIAM playbook be designed to handle this dynamic token authentication securely and reliably?
- A. Disable token authentication on ESX to simplify the XSIAM integration.
- B. Configure XSIAM with the ESX API credentials, assuming XSIAM will automatically handle token refreshing.
- C. Hardcode a static, long-lived token obtained from ESX into the XSIAM playbook configuration.
- D. Manually retrieve the token from ESX and paste it into the XSIAM playbook each time it runs.
- E. Implement two sequential steps in the playbook: one to call the ESX authentication API to get the token, and a second step using the output of the first step to make the isolation API call.
Answer: E
Explanation:
For APIs requiring dynamic, short-lived tokens, the playbook must explicitly manage the token acquisition. Option B describes the correct pattern: the first step in the playbook calls the ESX authentication API to obtain the token, and the subsequent step(s) use this token (passed as an output from the first step) in the 'Authorization' header or body of the actual isolation API call. This ensures the token is fresh and valid for each execution. Hardcoding (A) is insecure and will fail. Manual input (C) is not automation. XSIAM does not automatically handle all external API token refreshes (D) unless specifically designed into a connector. Disabling authentication (E) is a severe security risk.
NEW QUESTION # 89
An advanced XSIAM dashboard is required to analyze 'Lateral Movement' attempts, specifically focusing on RDP connections originating from non-standard internal subnets to critical servers. The dashboard should display: 1) Source IP, 2) Destination IP, 3) User, and 4) Connection time, for all such detected attempts. Additionally, it must provide a 'risk score' for each connection based on a custom lookup table of 'known risky internal IPs'. Which combination of XQL, lookup, and visualization would yield the most insightful dashboard?
- A.
- B.
- C. Use a pre-built 'Lateral Movement' widget, as custom risk scoring is not feasible.
- D.
- E. Manual parsing of RDP logs from endpoints and correlating them in a spreadsheet.
Answer: A
Explanation:
NEW QUESTION # 90
An organization is migrating from a traditional SIEM to Palo Alto Networks XSIAM. They have a large collection of custom correlation rules written in Splunk's SPL. A key objective is to translate these rules to XSIAM's Alert Query Language (AQL) to maintain existing detection capabilities. During the planning and resource evaluation, what is the most significant technical challenge to anticipate, and which XSIAM feature/resource is most critical for addressing it efficiently?
- A. The XSIAM Analytics Engine (XAE) being incompatible with custom AQL rules, limiting detection to Palo Alto Networks' pre-defined content.
- B. Insufficient storage capacity in Cortex Data Lake (CDL) to accommodate the translated rules, which are typically much larger in AQL than SPL.
- C. XSIAM's inability to ingest historical Splunk logs, necessitating a fresh start for all detection logic.
- D. The lack of direct Splunk SPL to XSIAM AQL automated conversion tools; requiring manual translation efforts and a strong understanding of both languages' syntax and data models.
- E. The absence of a graphical rule builder in XSIAM, forcing all rule creation to be done via command-line AQL.
Answer: D
Explanation:
The most significant technical challenge in migrating complex correlation rules from Splunk SPL to XSIAM AQL is the lack of direct, robust, and automated conversion tools. While some basic transformations might be possible, the nuanced differences in data models, function sets, and logical constructs between SPL and AQL often necessitate a significant manual translation effort. This requires security engineers with expertise in both languages and a deep understanding of how the original detection logic in Splunk maps to XSIAM's unified data model. Options B, C, D, and E are generally false or misrepresent XSIAM capabilities: XSIAM can ingest historical logs (B), rule size is not a primary concern (C), XSIAM does have a I-II-driven rule builder (D), and XAE is fully compatible with custom AQL rules (E).
NEW QUESTION # 91
An engineer wants to onboard data from a third-party vendor's firewall. There is no content pack available for it, so the engineer creates custom data source integration and parsing rules to generate a dataset with the firewall data.
How can the analytics capabilities of Cortex XSIAM be used on the data?
- A. Create a parsing rule and ensure the network fields exist (source IP. source port, target IP. target port. IP protocol).
- B. Create a behavioral indicator of compromise (BIOC) rule on the network fields (source IP, source port, target IP, target port. IP protocol).
- C. Create a data model rule with network fields mapped (source IP. source port, target IP. target port. IP protocol).
- D. Create a correlation rule on the network fields (source IP. source port, target IP. target port. IP protocol).
Answer: C
Explanation:
To leverage Cortex XSIAM analytics on custom-ingested firewall data, a data model rule must be created with the key network fields (source IP, source port, target IP, target port, IP protocol) mapped. This enables the data to align with XSIAM's analytics engine and be used for BIOCs, correlation rules, and advanced detections.
NEW QUESTION # 92
A large enterprise is planning a Palo Alto Networks XSIAM deployment to ingest security logs from 15,000 endpoints, 500 network devices, and 20 cloud accounts. The expected daily log volume is estimated at 10 TB, with peak ingestion rates reaching 20 TB/day during incident response. The organization requires a 90-day data retention period for hot data and a I-year retention for warm data, with cold data archived for 7 years. Which of the following hardware considerations are paramount for a successful XSIAM deployment in this scenario?
- A. Provisioning 100 GbE networking interfaces on all XSIAM cluster nodes to prevent network bottlenecks during data transfer.
- B. Allocating dedicated GPU resources for threat intelligence correlation and machine learning anomaly detection.
- C. Prioritizing high-frequency CPU cores over total core count for data ingestion and normalization.
- D. Implementing a hybrid storage architecture combining high-performance block storage for hot data, object storage for warm data, and tape libraries for cold archives.
- E. Ensuring sufficient NVMe SSDs for hot data storage to accommodate peak ingestion and query performance, with a focus on IOPS and throughput.
Answer: A,E
Explanation:
For a large-scale XSIAM deployment with high ingestion rates and demanding query performance, NVMe SSDs are crucial for hot data (B) due to their superior IOPS and throughput, directly impacting ingestion and query speeds. High-speed networking (E) is also critical to prevent bottlenecks during log ingestion from a diverse and large set of sources. While CPU core count is important, frequency is less of a primary driver than total processing power (A). XSIAM primarily leverages CPU for its analytical capabilities, not GPUs (C). While hybrid storage is a good concept, the question focuses on hardware for XSIAM's direct operation, and tape libraries are not part of its primary storage tiers (D).
NEW QUESTION # 93
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