Free Fortinet FCP_FAZ_AN-7.6 Practice Exam with Questions & Answers

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 forselecting, filtering, ordering, and grouping the results as shown in the expected outcome.

Analysis of the Options:

Option A Explanation:

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 dstportDESC: 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 Explanation:

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 Explanation:

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 Explanation:

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.

In FortiAnalyzer,archive logsrefer to logs that have been compressed and stored to save space. This process involves compressing the raw log files into the .gz format, which is a common compression format used in Fortinet systems for archived data. Archiving is essential in FortiAnalyzer to optimize storage and manage long-term retention of logs without impacting performance.

Let’s examine each option for clarity:

Option A: Logs that are indexed and stored in the SQL database

This is incorrect. While some logs are indexed andstored in an SQL database for quick access and searchability, these are not classified asarchive logs. Archived logs are typically moved out of the database and compressed.

Option B: Logs a FortiAnalyzer administrator can access in FortiView

This is incorrect becauseFortiViewprimarily accesses logs that are active and indexed, not archived logs. Archived logs are stored for long-term retention but are not readily available for immediate analysis in FortiView.

Option C: Logs compressed and saved in files with the .gz extension

This is correct. Archive logs on FortiAnalyzer are stored in compressed .gz files to reduce space usage. This archived format is used for logs that are no longer immediately needed in the SQL database but are retained for historical or compliance purposes.

Option D: Logs previously collected from devices that are offline

This is incorrect. Although archived logs may include data from devices that are no longer online, this is not a defining characteristic of archive logs.

Comprehensive and Detailed Explanation From Exact Extract of knowledge of FortiAnalyzer 7.6 Study guide documents:

FortiAnalyzer’s ingestion pipeline does not “drop” logs simply because a parser is unavailable. The study guide states that when devices send logs,“Logs received are decompressed and saved in a log file on the FortiAnalyzer disk”(with a .log extension). This establishes that the raw log is still accepted and stored on disk as part of the normal workflow.

Normalization, however, depends on having a suitable parser. The study guide explains that“FortiAnalyzer uses predefined parsers to extract key fields from ingested logs and maps them to a consistent, standardized set of field names.”It further emphasizes that“Log parsers … are central to log normalization”because they convert unstructured/native logs into a standardized schema.

Therefore, ifno matching parserexists for a given device log, FortiAnalyzer can stillstore the incoming log(it is received, decompressed, and written to disk), but it cannot perform the “extract key fields” and “map to standardized field names” steps required for normalization. In practical terms, the log remains in its native/unstructured form (not normalized), which aligns exactly with optionC.

The objective is to create a filter that identifies all login attempts to the FortiAnalyzer web interface (GUI) coming fromLaptop1(IP 10.1.1.100) and excludes the admin user. This filter should match any user other than admin.

Filter Components Analysis:

Operation-login: This portion of the filter will target login actions specifically, which is correct for filtering login attempts.

performed_on==’’GUI(10.1.1.100)’: This indicates that the login attempt must occur on the GUI interface and originate from the specified IP, which matches Laptop1's IP address (10.1.1.100). This ensures that the filter only matches GUI logins from this specific device.

user!=admin: This part excludes logins by the admin user, meeting the requirement to capture only non-admin users.

Option Analysis:

Option A: Correctly specifies theOperation-login,performed_on==’’GUI(10.1.1.100)’, anduser!=admin. This setup effectively filters login attempts to the GUI from Laptop1, excluding the admin user.

Option B: Uses the incorrect IP 10.1.1.120 in the performed_on filter, which does not match Laptop1's IP (10.1.1.100).

Option C: This option includessrcip==10.1.1.100anddstip==10.1.1.210but incorrectly specifiesuser==admininstead ofuser!=admin, which does not match the requirement to exclude admin users.

Option D: This option does not specify theperformed_onfield to restrict it to the GUI and only includesdstip(destination IP) withoutsrcip. It also incorrectly uses user!-admin instead of the correct syntaxuser!=admin.

Conclusion:

Correct Answer:A. Operation-login and performed_on==’’GUI(10.1.1.100)’ and user!=admin

This filter precisely captures the required conditions: login attempts from Laptop1 to the GUI interface by any user except admin.

When a generated report does not contain the expected information even though the logs are confirmed to be present, it typically indicates anissue with the report's configuration. There are a few common reasons this might happen:

Option A - Check the Time Frame Covered by the Report:

Reports are generated based on a specific time frame. If the report’s time frame does not cover the period when the relevant logs were collected, those logs won’t appear in the report output. Verifying and adjusting the time frame is essential to ensure the report includes all relevant data.

Conclusion:Correct.

Option B - Disable Auto-Cache:

Auto-cache is designed to improve report generation speed by using cached data. Disabling auto-cache would typically only be relevant if the report is pulling outdated data from cache, but it doesn’t directly affect whether specific logs are included in a report.

Conclusion:Incorrect.

Option C - Increase the Report Utilization Quota:

The report utilization quota is related to the resource limits for generating reports. It does not directly influence whether certain data appears in a report. Increasing this quota would help only if there are resource issues preventing the report from completing, not if specific logs are missing from the report.

Conclusion:Incorrect.

Option D - Test the Dataset:

Datasets determine which logs and data fields are pulled into the report. If a dataset is configured incorrectly or does not include the required log fields, it could lead to missing information. Testing the dataset allows you to verify that it’s correctly configured and pulling the expected data.

Conclusion:Correct.

Conclusion:

Correct Answer:A. Check the time frame covered by the reportandD. Test the dataset.

These steps directly address the issues that could lead to missing information in a report when logs are available but not displayed.

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 thenormal 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.

Comprehensive and Detailed Explanation From Exact Extract of knowledge of FortiAnalyzer 7.6 Study guide documents:

The exhibit shows the log as a single-line key/value entry (not a columnar/table display), which aligns with FortiAnalyzer’sraw log formatview option. The study guide states:“You can toggle between viewing formatted and raw logs.”This directly supports observationD.

At the same time, what you are viewing in FortiAnalyzer Log View isnormalizeddata (FortiAnalyzer parses and maps device logs into standardized fields for consistent searching and analysis). The study guide explicitly states:“The log view allows you to view all log types received by FortiAnalyzer in normalized log format.”It also explains that FortiAnalyzer “uses predefined parsers to extract key fields from ingested logs and maps them to a consistent, standardized set of field names,” then stores them as normalized logs in the SIEM database. This supports observationA.

Finally, the study guide clarifies that even when you switch to raw log format in FortiAnalyzer, you are still observing the normalized-field representation produced by FortiAnalyzer’s parser/normalization process (rather than the untouched original device message). It notes that a FortiGate event log “has been normalized by FortiAnalyzer,” and when you switch “to raw log format,” you can observe the effect of normalization on common fields. This is whyCis not the best description for the exhibit.