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XSIAM-Engineer Online Test | XSIAM-Engineer Answers Real Questions

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Palo Alto Networks XSIAM-Engineer Answers Real Questions, Complete XSIAM-Engineer Exam Dumps

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Palo Alto Networks XSIAM Engineer Sample Questions (Q210-Q215):

NEW QUESTION # 210
An organization is migrating its core applications to Google Cloud Platform (GCP). The XSIAM team needs to ingest logs from various GCP services, including VPC Flow Logs, Cloud Audit Logs, and Kubernetes Engine (GKE) logs. Which of the following approaches is the most efficient and recommended for integrating these diverse GCP data sources into XSIAM?

A. Utilize GCP's Pub/Sub service to stream logs to a custom endpoint running a Python script that forwards logs to XSIAM's ingestion API.
B. Configure GCP Sink destinations to Pub/Sub topics, and then leverage the XSIAM native Google Cloud Platform data connector to ingest from these Pub/Sub topics.
C. Manually export each log type to Google Cloud Storage buckets and then use an XSIAM Data Collector to pull data from the buckets.
D. Install Cortex XDR agents on every GCP VM instance and configure them to collect application logs.
E. Use a third-party SIEM connector that pushes all GCP logs directly to XSIAM via syslog.

Answer: B

Explanation:
The most efficient and recommended approach for integrating diverse GCP data sources into XSIAM is to leverage GCP's native log export capabilities combined with XSIAM's dedicated GCP data connector. Specifically, configuring GCP Sinks to export logs to Pub/Sub topics allows for real-time streaming of logs. The XSIAM native Google Cloud Platform data connector is designed to seamlessly ingest from these Pub/Sub topics, ensuring efficient and reliable data flow. Manual exports (A) are inefficient. Custom scripts (B) introduce maintenance overhead. XDR agents (D) are for endpoint telemetry, not cloud service logs. Third-party SIEM connectors (E) can work, but a native XSIAM connector is generally more optimized and supported.

NEW QUESTION # 211
During the planning phase for an XSIAM deployment, an organization decides to utilize a Service Account for programmatic access to the XSIAM API for custom integrations and automation. Which of the following API endpoints and authentication methods are typically used for a Service Account to interact with the XSIAM platform for data query and alert management?

A. Option C
B. Option E
C. Option D
D. Option A
E. Option B

Answer: E

Explanation:
Palo Alto Networks XSIAM primarily uses API Keys for programmatic access via Service Accounts. The API Key is a long-lived credential passed in an HTTP header (commonly 'x-pan-api-key' or 'Authorization: Bearer '). This allows direct authentication for subsequent API calls to various endpoints for querying data, managing alerts, and other operations. Option A describes user-based authentication. Options C, D, and E are incorrect for XSIAM API interaction.

NEW QUESTION # 212
A large-scale XSIAM deployment is experiencing ingestion bottlenecks and high latency for certain critical data sources, specifically network flow data from dozens of firewalls and identity logs from multiple Active Directory domains. The current architecture uses a single Broker VM for all on-premise integrations. What steps should the XSIAM engineer take to diagnose and alleviate these ingestion performance issues, considering the specific data types involved?

A. Review the Broker VM's resource utilization (CPU, memory, network I/O) from the XSIAM console. For network flow data, consider deploying additional Broker VMS in a load-balanced configuration to distribute the ingestion load. For identity logs, optimize the AD query frequency and data volume transmitted.
B. Implement an intermediate Kafka cluster on-premise to buffer all logs before forwarding them to the Broker VM, thus smoothing out ingestion spikes.
C. Increase the CPU and memory allocated to the single Broker VM, as this is the most common cause of performance bottlenecks for all data types.
D. Check the XSIAM cloud-side ingestion health metrics; the bottleneck is likely within the XSIAM cloud, not the on-premise components.
E. Reduce the logging verbosity on the firewalls and Active Directory to decrease the overall volume of data being sent to XSIAM.

Answer: A

Explanation:
Ingestion bottlenecks, especially with high-volume data like network flows and frequent identity updates, often point to resource constraints or architectural limitations of the Broker VM. Option B is the most comprehensive and correct approach: 1. Diagnose: Reviewing the Broker VM's resource utilization (CPU, memory, network I/O) from the XSIAM console is the first critical step. This directly indicates if the Broker VM itself is becoming a bottleneck. 2. Network Flow Data: Network flow data (e.g., NetFlow, IPFIX, firewall session logs) can be extremely high volume. A single Broker VM might be overwhelmed. Deploying additional Broker VMS and distributing the firewall log forwarding across them (load-balancing) is a standard and effective scaling strategy for high-volume data. Each Broker VM can handle a certain throughput. 3. Identity Logs: While generally lower volume than network flows, frequent AD queries for identity updates can still impact performance. Optimizing the AD query frequency (e.g., using change notifications instead of full syncs, or adjusting intervals) and ensuring only necessary data fields are transmitted can significantly reduce the load. Option A: While increasing resources can help, it's a temporary fix if the architecture itself is not scalable for the data volume. It's better to understand the specific bottleneck before just throwing more resources at it. Option C: An intermediate Kafka cluster can help, but it adds complexity and is generally considered if the Broker VM scaling isn't sufficient or if there are extreme burst patterns. It's not the primary or first-line solution for general ingestion bottlenecks with XSIAM Broker VMs. Option D: Reducing logging verbosity should be a last resort, as it directly impacts detection capabilities by removing valuable telemetry. Option E: While XSIAM cloud-side health should always be monitored, the description points to on-premise data sources and a single Broker VM, making the Broker VM a more likely initial point of failure for bottlenecks.

NEW QUESTION # 213

A. Option C
B. Option B
C. Option E
D. Option D
E. Option A

Answer: D

Explanation:
While options A, B, and C could be contributing factors in different scenarios, the phrase 'despite being populated in entity_id previous steps' and 'not for others' (implying it works elsewhere) points to a variable scoping issue. In complex playbooks, especially those with nested tasks, conditional branches, or parallel execution, variables defined within certain contexts (like a sub-playbook, a 'for-each' loop, or an isolated task group) might not be directly accessible or automatically passed to subsequent steps outside of their immediate scope. XSIAM's playbook engine enforces variable visibility. If 'entity_id' was, for example, an output of a command run within a 'parallel' task or a sub-playbook, it might need to be explicitly passed as an input to the failing command step, or promoted to a higher-level context variable, to be accessible. This is a common and often subtle debugging challenge in complex automation workflows.

NEW QUESTION # 214
During an internal audit, it was discovered that several development machines in the 'DevOps' organizational unit (OU) have a legacy RDP port (3389) exposed to the internal network without proper Network Security Group (NSG) restrictions. This violates the company's internal security policy. You need to configure an XSIAM ASM rule to detect such instances. The machines are tagged with 'Environment: Development' and 'OU: DevOps'. Which approach is most suitable for creating this targeted ASM rule?

A. Develop a custom XQL query that correlates 'xdr_asset_inventory' data with 'xdr_network_sessions' data, filtering by asset tags and destination port.
B. Configure an endpoint policy in XSIAM to block RDP connections on all 'DevOps' machines.
C. Utilize the XSIAM 'Network Mapper' to visually identify exposed RDP ports and manually mark them as non-compliant.
D. Create an ASM rule based on a predefined 'Exposed RDP Port' template, then add a filter for the 'DevOps' OU.
E. Set up a recurring vulnerability scan through XSIAM integrations targeting the 'DevOps' network segment.

Answer: A

Explanation:
Option B is the most suitable for a targeted ASM detection rule. An XQL query can effectively combine asset metadata (tags from xdr_asset_inventory) with network telemetry (xdr_network_sessions) to precisely identify machines with the specified tags that are also observed communicating on port 3389. This allows for granular detection based on specific organizational context. Option A might exist, but the customization based on OU and environment tags via XQL offers more precision. Option C is for visual identification, not automated detection. Option D is a remediation action, not a detection rule. Option E is a scanning approach, which is periodic, whereas an ASM rule provides continuous monitoring based on live telemetry.

NEW QUESTION # 215
......

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