매달, 우리는 1000명 이상의 사람들이 시험 준비를 잘하고 시험을 잘 통과할 수 있도록 도와줍니다.
Fortinet NSE 5 - FortiAnalyzer 7.6 Analyst 온라인 연습
최종 업데이트 시간: 2026년02월14일
당신은 온라인 연습 문제를 통해 Fortinet FCP_FAZ_AN-7.6 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 FCP_FAZ_AN-7.6 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 194개의 시험 문제와 답을 포함하십시오.
정답:
Explanation:
In the exhibit, the data structure shows a checksum field and a data field with a long, seemingly encoded string. This format is indicative of a file that has been compressed or encoded for storage and transfer.
Export Data Type:
The data field is likely a base64-encoded string, which is commonly used to represent binary data in text format. Base64 encoding is often applied to data that has been compressed (zipped) for easier handling and transfer. The checksum field, with an MD5 hash, provides a way to verify the integrity of the data after decompression.
Option Analysis:
A. The export data type is zipped: Correct. The compressed and encoded format of the data suggests that the export is in a zipped format, allowing for efficient storage and transfer.
B. The playbook is misconfigured: There is no indication of misconfiguration in this exhibit. The presence of the checksum and data fields aligns with standard export practices.
C. The option to include the connector was not selected: There is no evidence in the output to conclude that connectors are missing. Connectors are typically listed separately and would not directly affect the checksum and encoded data structure.
D. Your colleague put a password on the export: There’s no indication of password protection in the exhibit. Password protection would likely alter the data structure, and there would be some mention of encryption.
Conclusion:
Correct Answer
A. The export data type is zipped.
This answer is consistent with the typical use of base64 encoding for compressed (zipped) data exports in FortiAnalyzer.
Reference: FortiAnalyzer 7.4.1 documentation on exporting playbooks and data compression methods.
정답:
Explanation:
FortiAnalyzer offers several features for monitoring, alerting, and incident management, each serving different purposes. Let's examine each option to determine which one best supports a proactive security approach.
Option A - FortiView Monitor:
FortiView is a visualization tool that provides real-time and historical insights into network traffic, threats, and logs. While it gives visibility into network activity, it is generally more reactive than proactive, as it relies on existing log data and incidents.
Conclusion: Incorrect.
Option B - Outbreak Alert Services:
Outbreak Alert Services in FortiAnalyzer notify administrators of emerging threats and outbreaks based on FortiGuard intelligence. This is beneficial for awareness of potential threats but does not offer a hands-on, investigative approach. It’s more of a notification service rather than an active, proactive investigation tool.
Conclusion: Incorrect.
Option C - Incidents Dashboard:
The Incidents Dashboard provides a summary of incidents and current security statuses within the network. While it assists with ongoing incident response, it is used to manage and track existing incidents rather than proactively identifying new threats.
Conclusion: Incorrect.
Option D - Threat Hunting:
Threat Hunting in FortiAnalyzer enables security analysts to actively search for hidden threats or malicious activities within the network by leveraging historical data, analytics, and intelligence. This is a proactive approach as it allows analysts to seek out threats before they escalate into incidents.
Conclusion: Correct.
Conclusion:
Correct Answer
D. Threat hunting
Threat hunting is the most proactive feature among the options, as it involves actively searching for threats within the network rather than reacting to already detected incidents.
Reference: FortiAnalyzer 7.4.1 documentation on Threat Hunting and proactive security measures.
정답:
Explanation:
In the exhibit, the playbook configuration shows the analyst working with the "Attach Data" action within a playbook. Here’s a breakdown of key aspects:
Incident ID: This field is linked to the "Playbook Starter," which indicates that the playbook will attach data to an existing incident.
Attachment: The analyst is configuring an attachment by selecting Run_REPORT with a placeholder ID for report_uuid. This suggests that the report’s UUID will dynamically populate as part of the playbook execution.
Analysis of Options:
Option A - Creating a Trigger Variable:
A trigger variable would typically be set up in the playbook starter or initiation configuration, not within the "Attach Data" action. The setup here does not indicate a trigger, as it’s focusing on data attachment.
Conclusion: Incorrect.
Option B - Creating an Output Variable:
The field Attachment with a report_uuid placeholder suggests that the analyst is defining an output variable that will store the report data or ID, allowing it to be attached to the incident. This variable can then be referenced or passed within the playbook for further actions or reporting.
Conclusion: Correct.
Option C - Creating a Report in the Playbook:
While Run_REPORT is selected, it appears to be an attachment action rather than a report generation task. The purpose here is to attach an existing or dynamically generated report to an incident, not to create the report itself.
Conclusion: Incorrect.
Option D - Creating a SOC Report:
Similarly, this configuration is focused on attaching data, not specifically generating a SOC report.
SOC reports are generally predefined and generated outside the playbook.
Conclusion: Incorrect.
Conclusion:
Correct Answer B. The analyst is trying to create an output variable to be used in the playbook.
The setup allows the playbook to dynamically assign the report_uuid as an output variable, which can then be used in further actions within the playbook.
Reference: FortiAnalyzer 7.4.1 documentation on playbook configurations, output variables, and data attachment functionalities.
정답:
Explanation:
Option A - Purging Log Entries:
A SELECT query in SQL is used to retrieve data from a database and does not have the capability to delete or purge log entries. Purging logs typically requires a DELETE or TRUNCATE command.
Conclusion: Incorrect.
Option B - WHERE Clause Requirement:
In SQL, a SELECT query does not require a WHERE clause. The WHERE clause is optional and is used only when filtering results. A SELECT query can be executed without it, meaning this statement is false.
Conclusion: Incorrect.
Option C - Displaying Database Schema:
A SELECT query retrieves data from specified tables, but it is not used to display the structure or schema of the database. Commands like DESCRIBE, SHOW TABLES, or SHOW COLUMNS are typically used to view schema information.
Conclusion: Incorrect.
Option D - Usage in Macros:
FortiAnalyzer and similar systems often use macros for automated functions or specific query-based tasks. SELECT queries are typically not included in macros because macros focus on procedural or repetitive actions, rather than simple data retrieval.
Conclusion: Correct.
Conclusion:
Correct Answer
D. They are not used in macros.
This aligns with typical SQL usage and the specific functionalities of FortiAnalyzer.
Reference: FortiAnalyzer 7.4.1 documentation on SQL queries, database operations, and macro usage.
정답:
Explanation:
In the exhibit, we see a playbook in FortiAnalyzer designed to retrieve events based on specific criteria, create an incident, and attach relevant data to that incident.
The "Get Event" task configuration specifies filters to match any of the following conditions:
Severity = High
Event Type = Web Filter
Tag = Malware
Analysis of Events:
In the FortiAnalyzer Event Monitor list:
We need to identify events that meet any one of the specified conditions (since the filter is set to "Match Any Condition").
Events Matching Criteria:
Severity = High:
There are two events with "High" severity, both with the "Event Type" IPS.
Event Type = Web Filter:
There are two events with the "Event Type" Web Filter. One has a "Medium" severity, and the other has a "Low" severity.
Tag = Malware:
There are two events tagged with "Malware," both with the "Event Type" Antivirus and "Medium" severity.
After filtering based on these criteria, there are four distinct events:
Two from the "Severity = High" filter.
One from the "Event Type = Web Filter" filter.
One from the "Tag = Malware" filter.
Conclusion:
Correct Answer
D. Four events will be added.
This answer matches the conditions set in the playbook filter configuration and the events listed in the Event Monitor.
Reference: FortiAnalyzer 7.4.1 documentation on event filtering, playbook configuration, and incident management criteria.
정답:
정답:
정답:
Explanation:
The requirement here is to construct a SQL query that retrieves logs with specific fields, namely "Source IP" and "Destination Port," for entries where the source IP address matches 10.0.1.10. The correct syntax is essential for selecting, filtering, ordering, and grouping the results as shown in the expected outcome.
Analysis of the Options:
Option A
SELECT srcip AS "Source IP", dstport AS "Destination Port": This syntax selects srcip and dstport, renaming them to "Source IP" and "Destination Port" respectively in the output.
FROM $log: Specifies the log table as the data source.
WHERE $filter AND srcip = '10.0.1.10': This line filters logs to only include entries with srcip equal to 10.0.1.10.
ORDER BY dstport DESC: Orders the results in descending order by dstport.
GROUP BY srcip, dstport: Groups results by srcip and dstport, which is valid SQL syntax.
This option meets all the requirements to get the expected results accurately.
Option B
WHERE $filter AND Source IP != '10.0.1.10': Uses != instead of =. This would exclude logs from the specified IP 10.0.1.10, which is contrary to the expected result.
Option C
The ORDER BY clause appears before the FROM clause, which is incorrect syntax. SQL requires the FROM clause to follow the SELECT clause directly.
Option D
The GROUP BY clause should follow the FROM clause. However, here, it’s located after WHERE, making it syntactically incorrect.
Conclusion:
Correct Answer
A. Option A
This option aligns perfectly with standard SQL syntax and filters correctly for srcip = '10.0.1.10', while ordering and grouping as required.
Reference: FortiAnalyzer 7.4.1 SQL query capabilities and syntax for report customization.
정답:
Explanation:
In this output, we see two diagnostic commands executed on a FortiAnalyzer device:
diagnose fortilogd lograte: This command shows the rate at which logs are being processed by the FortiAnalyzer in terms of log entries per second.
diagnose fortilogd msgrate: This command displays the message rate, or the rate at which individual messages are being processed.
The values provided in the exhibit output show:
Log rate (lograte): Consistently high, showing values such as 70.0, 132.1, and 133.3 logs per second over different time intervals.
Message rate (msgrate): Lower values, around 1.4 to 1.6 messages per second.
Explanation
Interpretation of log rate vs. message rate: In FortiAnalyzer, the log rate typically refers to the rate of logs being stored or indexed, while the message rate refers to individual messages within these logs. Given that a single log entry can contain multiple messages, it's common to see a lower message rate relative to the log rate.
Understanding normal operation: In this case, the message rate being lower than the log rate is expected and typical behavior. This discrepancy can arise because each log entry may bundle multiple related messages, reducing the message rate relative to the log rate.
Conclusion
Correct Answer
A. The message rate being lower than the log rate is normal.
This aligns with the normal operational behavior of FortiAnalyzer in processing logs and messages.
There is no indication that both logs and messages are nearly finished indexing, as that would typically show diminishing rates toward zero, which is not the case here. Additionally, there’s no information in this output about specific ADOMs or a comparison between traffic logs and event logs. Thus, options B, C, and D are incorrect.
Reference: FortiOS 7.4.1 and FortiAnalyzer 7.4.1 command guides for diagnose fortilogd lograte and diagnose fortilogd msgrate.
정답:
Explanation:
When a new playbook is created on FortiAnalyzer, the system requires some time to parse and validate the playbook before it can be executed. Parsing involves checking the playbook's structure, ensuring that all syntax and logic are correct, and preparing the playbook for execution within FortiAnalyzer’s automation engine. This initial parsing step is necessary for FortiAnalyzer to load the playbook into its operational environment correctly.
Here’s why the other options are incorrect:
Option A: FortiAnalyzer needs that time to parse the new playbook
This is correct. The delay is due to the parsing and setup process required to prepare the new playbook for execution. FortiAnalyzer’s automation engine checks for any issues or dependencies within the playbook, ensuring that it can run without errors.
Option B: FortiAnalyzer needs that time to debug the new playbook
This is incorrect. Debugging is not an automatic process that FortiAnalyzer undertakes after playbook creation. Debugging, if necessary, is a manual task performed by the administrator if there are issues
with the playbook execution.
Option C: FortiAnalyzer needs that time to back up the current playbooks
This is incorrect. FortiAnalyzer does not automatically back up playbooks every time a new one is created. Backups of configuration and playbooks are typically scheduled as part of routine maintenance and are not triggered by playbook creation.
Option D: FortiAnalyzer needs that time to ensure there are no other playbooks running
This is incorrect. FortiAnalyzer can manage multiple playbooks running simultaneously, so it does not require waiting for other playbooks to finish before initiating a new one. The waiting time specifically relates to the parsing process of the newly created playbook.
Reference: FortiAnalyzer documentation states that after creating a playbook, a brief delay is expected as the system parses and validates the playbook. This ensures that any syntax errors or logical inconsistencies are resolved before the playbook is executed, making option A the correct answer.
정답:
Explanation:
In FortiAnalyzer's incident management system, analysts have the option to manually manage incidents, which includes attaching relevant reports to an incident for further investigation and documentation. This feature allows analysts to consolidate information, such as detailed reports on suspicious activity, into an incident record, providing a comprehensive view for incident response.
Let's review the other options to clarify why they are incorrect:
Option A: You can manually attach generated reports to incidents
This is correct. FortiAnalyzer allows analysts to manually attach reports to incidents, which is beneficial for providing additional context, evidence, or analysis related to the incident. This functionality is part of the incident management process and helps streamline information for tracking and resolution.
Option B: The status of the incident is always linked to the status of the attached event
This is incorrect. The status of an incident on FortiAnalyzer is managed independently of the status of any attached events. An incident can contain multiple events, each with different statuses, but the incident itself is tracked separately.
Option C: Severity incidents rated with the level High have an initial service-level agreement (SLA) response time of 1 hour
This is incorrect. While incidents have severity levels, specific SLA response times are typically set according to the organization’s incident response policy, and FortiAnalyzer does not impose a default
SLA response time of 1 hour for high-severity incidents.
Option D: Incidents must be acknowledged before they can be analyzed
This is incorrect. Incidents on FortiAnalyzer can be analyzed even if they are not yet acknowledged. Acknowledging an incident is often part of the workflow to mark it as being actively addressed, but it is not a prerequisite for analysis.
Reference: According to FortiAnalyzer documentation, analysts can attach reports to incidents manually, making option A correct. This feature enables better tracking and documentation within the incident management system on FortiAnalyzer.
정답:
Explanation:
When using FortiAnalyzer to create playbooks that interact with FortiOS devices, an Incoming Webhook trigger is required on the FortiGate side to make the actions in an automation stitch accessible through the FortiOS connector. The incoming webhook trigger allows FortiAnalyzer to initiate actions on FortiGate by sending HTTP POST requests to specified endpoints, which in turn trigger automation stitches defined on the FortiGate.
Here’s an analysis of each option:
Option A: FortiAnalyzer Event Handler
This is incorrect. The FortiAnalyzer Event Handler is used within FortiAnalyzer itself for handling log events and alerts, but it does not trigger automation stitches on FortiGate.
Option B: Fabric Connector event
This is incorrect. Fabric Connector events are related to Fortinet's Security Fabric integrations but are not specifically used to trigger FortiGate automation stitches from FortiAnalyzer.
Option C: FortiOS Event Log
This is incorrect. While FortiOS event logs can be used for monitoring, they are not designed to trigger automation stitches directly from FortiAnalyzer.
Option D: Incoming webhook
This is correct. The Incoming Webhook trigger on FortiGate enables it to receive requests from FortiAnalyzer, allowing playbooks to activate automation stitches defined on the FortiGate device. This method is commonly used to integrate actions from FortiAnalyzer to FortiGate via the FortiOS connector.
Reference: According to FortiOS and FortiAnalyzer documentation, when integrating FortiAnalyzer
playbooks with FortiGate automation stitches, the recommended trigger type on FortiGate is an Incoming Webhook, allowing FortiAnalyzer to interact with FortiGate’s automation framework through the FortiOS connector.
정답:
Explanation:
In FortiAnalyzer’s SQL query syntax, the typical order for querying the database follows the standard SQL format, which is:
SELECT <column(s)> FROM <table> WHERE <condition(s)> GROUP BY <column(s)>
Option D correctly follows this structure:
SELECT devid FROM $log: This specifies that the query is selecting the devid column from the $log table.
WHERE 'user' = ': This part of the query is intended to filter results based on a condition involving the user column. Although there appears to be a minor typographical issue (possibly missing the user value after =), it structurally adheres to the correct SQL order.
GROUP BY devid: This groups the results by devid, which is correctly positioned at the end of the query.
Let’s briefly examine why the other options are incorrect:
Option A: SELECT devid FROM $log GROUP BY devid WHERE 'user', 'users1'
This is incorrect because the GROUP BY clause appears before the WHERE clause, which is out of order in SQL syntax.
Option B: SELECT FROM $log WHERE devid 'user', USER1' GROUP BY devid
This is incorrect because it lacks a column in the SELECT statement and the WHERE clause syntax is malformed.
Option C: SELCT devid WHERE 'user' - 'USER1' FROM $log GROUP BY devid
This is incorrect because the SELECT keyword is misspelled as SELCT, and the WHERE condition syntax is invalid.
Reference: FortiAnalyzer documentation for SQL queries indicates that the standard SQL order should be followed when querying logs in FortiAnalyzer. Queries should follow the format SELECT ... FROM ... WHERE ... GROUP BY ..., as demonstrated in option D.
정답:
Explanation:
To view Compromised Hosts on FortiAnalyzer, certain configurations need to be in place on both FortiGate and FortiAnalyzer. Compromised Host data on FortiAnalyzer relies on log information from FortiGate to analyze threats and compromised activities effectively.
Here’s why the selected answers are correct:
Option A: Enable device detection on the FortiGate devices that are sending logs to FortiAnalyzer
Enabling device detection on FortiGate allows it to recognize and log devices within the network, sending critical information about hosts that could be compromised. This is essential because FortiAnalyzer relies on these logs to determine which hosts may be at risk based on suspicious activities observed by FortiGate. This setting enables FortiGate to provide device-level insights, which FortiAnalyzer uses to populate the Compromised Hosts view.
Option B: Enable web filtering in firewall policies on FortiGate devices, and make sure these logs are sent to FortiAnalyzer Web filtering is crucial in identifying potentially compromised hosts since it logs any access to malicious sites or blocked categories. FortiAnalyzer uses these web filter logs to detect suspicious or malicious web activity, which can indicate compromised hosts. By ensuring that FortiGate sends these web filtering logs to FortiAnalyzer, the administrator enables FortiAnalyzer to analyze and identify hosts engaging in risky behavior.
Let’s review the other options for clarity:
Option C: Make sure all endpoints are reachable by FortiAnalyzer
This is incorrect. FortiAnalyzer does not need direct access to all endpoints. Instead, it collects data indirectly from FortiGate logs. FortiGate devices are the ones that interact with endpoints and then forward relevant logs to FortiAnalyzer for analysis.
Option D: Subscribe FortiAnalyzer to FortiGuard to keep its local threat database up to date
Although subscribing to FortiGuard helps keep threat intelligence updated, it is not a requirement specifically to view compromised hosts. FortiAnalyzer primarily uses logs from FortiGate (such as web filtering and device detection) to detect compromised hosts.
Reference: According to FortiOS and FortiAnalyzer documentation, device detection on FortiGate and enabling web filtering logs are both recommended steps for populating the Compromised Hosts view on FortiAnalyzer. These logs provide insights into device behaviors and web activity, which are essential for identifying and tracking potentially compromised hosts.
정답:
Explanation:
In a FortiAnalyzer Fabric, devices can participate in a cluster or grouping if they meet specific compatibility criteria.
Based on the outputs provided, let’s evaluate these criteria:
Version Compatibility:
All three devices, FortiAnalyzer1, FortiAnalyzer2, and FortiAnalyzer3, are running version v7.4.1-build0238, which is the same across the board. This version alignment is crucial because FortiAnalyzer Fabric requires that devices run compatible firmware versions for seamless communication and management.
Platform Type and Configuration:
All three devices are configured as Standalone in the HA mode, which allows them to operate independently but does not restrict their participation in a FortiAnalyzer Fabric. Each device is also on the FAZVM64-KVM platform type, ensuring hardware compatibility.
Global Settings:
Key settings such as adm-mode, adm-status, and adom-mode are consistent across all devices (adm-mode: normal, adm-status: enable, adom-mode: normal), which aligns with requirements for fabric integration and role assignment flexibility.
Each device also has the log-forward-cache-size set, which is relevant for forwarding logs within a fabric environment.
Based on the above analysis, all devices (FortiAnalyzer1, FortiAnalyzer2, and FortiAnalyzer3) meet the requirements to be part of a FortiAnalyzer Fabric.
Reference: FortiAnalyzer 7.4.1 documentation outlines that devices within a FortiAnalyzer Fabric should be on the same or compatible firmware versions and hardware platforms, and they must be configured for integration. Given that all devices match the version, platform, and mode criteria, they can all be part of the FortiAnalyzer Fabric.