XSIAM-Analyst Data Analysis With XQL Practice Test 3 — 15 Questions

Question 1

An analyst is reviewing endpoint telemetry and wants to see the very first time each unique process name appeared in the last 24 hours, along with the host it ran on. Which XQL construct best produces one row per process name showing its earliest event?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use the `stats` stage with `min(_time) as first_seen by process_name` so each process name collapses into a single row carrying its earliest timestamp.. The requirement is one row per process name with its earliest timestamp, which is precisely an aggregation grouped by process_name using the minimum of the time field. Adding host to the grouping fragments the output per host, deduplication keeps arbitrary representatives rather than a computed minimum, and a global sort-and-limit returns just one record.

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Use the `sort` stage on `_time` ascending followed by `limit 1` to keep only the first record overall.

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Use the `dedup` stage on process_name and rely on the default ascending time ordering to leave the earliest occurrence.

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Use the `stats` stage with `min(_time) as first_seen by process_name` and include `host` in the grouping.

Question 2

During an investigation an analyst must correlate a process execution event with the network connection it spawned, but the two record types live in different datasets and share no common field name; the process GUID exists in one dataset and the parent process GUID in the other. Which XQL join form lets the analyst match these records while preserving all execution events even when no matching connection exists?

Accepted Answer

join type = left process_guid with parent_process_guid. When two datasets use different field names for the same logical identifier, the join stage accepts an explicit key mapping of the form left_field with right_field. Choosing the left join type preserves every row from the first dataset, so process executions with no observed connection still surface with empty connection columns, allowing the analyst to spot blocked or failed outbound attempts.

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join type = right process_guid with parent_process_guid

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join type = inner process_guid with parent_process_guid

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join type = outer process_guid with parent_process_guid

Question 3

An analyst is building a complex XQL query to analyze network traffic. The query must filter events to include only those where the `action` field is either 'ALLOW' or 'DENY', and the `bytes_out` field is greater than 1000. The analyst also wants to exclude events where the `user` field is 'admin'. Which two XQL filter expressions correctly implement these conditions? (Choose two.)

Accepted Answer

filter action in ("ALLOW", "DENY") and bytes_out > 1000 and user != "admin". The correct filters use proper logical grouping and operators to enforce all conditions: action must be 'ALLOW' or 'DENY', bytes_out must exceed 1000, and user must not be 'admin'. The `in` operator is a valid shorthand for multiple OR conditions. Other options suffer from precedence issues, syntax errors, or incorrect logic.

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filter action = "ALLOW" or "DENY" and bytes_out > 1000 and user != "admin"

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filter action in ("ALLOW", "DENY") or bytes_out > 1000 or user != "admin"

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filter action = "ALLOW" or action = "DENY" and bytes_out > 1000 and user != "admin"

Question 4

An analyst is investigating a suspected data-staging activity on a Windows endpoint. They want to find all process execution events from the 'xdr_data' dataset where the process name ends with 'rar.exe' and the command line contains the string 'a -ep'. The query must return only matching events without any aggregation. Which XQL filter condition achieves this?

Accepted Answer

filter action_process_image_name ends_with "rar.exe" and action_process_image_command_line contains "a -ep". To find process executions where the image name ends with 'rar.exe' and the command line includes 'a -ep', the 'ends_with' operator is the correct choice because it explicitly checks for a suffix. The 'contains' operator then ensures the command line has the required substring. This combination filters events precisely without aggregating, satisfying the investigation need.

Answer

filter action_process_image_name ~= "*rar.exe" and action_process_image_command_line contains "a -ep"

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filter action_process_image_name matches "rar.exe" and action_process_image_command_line contains "a -ep"

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filter action_process_image_name = "rar.exe" and action_process_image_command_line contains "a -ep"

Question 5

An analyst is investigating a suspected lateral movement incident and needs to examine authentication events that occurred within a specific 15-minute window on a particular endpoint. The analyst wants to filter events from the xdr_data dataset where the event timestamp falls between 2024-03-15 14:00:00 and 2024-03-15 14:15:00, and the agent hostname equals 'FIN-WS-042'. Which XQL query correctly applies these conditions?

Accepted Answer

dataset = xdr_data | filter event_timestamp >= timestamp("2024-03-15 14:00:00") and event_timestamp <= timestamp("2024-03-15 14:15:00") and agent_hostname = "FIN-WS-042". The correct query uses the filter stage with inclusive comparison operators and the timestamp() function to properly compare event timestamps. Combining the time range conditions and the hostname equality with AND ensures only authentication events on the specified endpoint within the 15-minute window are returned. This precise scoping is essential for efficient incident investigation in XSIAM.

Answer

dataset = xdr_data | filter event_timestamp > timestamp("2024-03-15 14:00:00") and event_timestamp < timestamp("2024-03-15 14:15:00") and agent_hostname = "FIN-WS-042"

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dataset = xdr_data | filter event_timestamp >= timestamp("2024-03-15 14:00:00") and event_timestamp <= timestamp("2024-03-15 14:15:00") or agent_hostname = "FIN-WS-042"

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dataset = xdr_data | where event_timestamp between "2024-03-15 14:00:00" and "2024-03-15 14:15:00" and agent_hostname == "FIN-WS-042"

Question 6

A security analyst needs to correlate DNS query events with process execution events to identify processes that resolved suspicious domains. The DNS events are in the `dns_events` dataset with fields `hostname`, `query_name`, and `_time`. The process events are in the `xdr_data` dataset with fields `agent_hostname`, `process_name`, and `_time`. The analyst wants to join these datasets on the hostname field, matching events where the DNS query occurred within 5 minutes before the process execution. Which XQL join approach correctly achieves this?

Accepted Answer

dataset = dns_events | join type = inner (dataset = xdr_data | alter proc_time = _time) as proc by hostname = proc.agent_hostname | filter _time < proc.proc_time and _time > proc.proc_time - 300000. The correct approach uses an inner join on hostname and then filters based on the time difference between the DNS query and process execution. It renames the process time field inside the subquery to avoid ambiguity. The other options either use invalid join syntax, omit the time constraint in the join, or rely on unsupported operators, making them incorrect.

Answer

dataset = dns_events | join type = inner (dataset = xdr_data) as proc by hostname = proc.agent_hostname and _time between proc._time - 300000 and proc._time

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dataset = dns_events | join type = inner (dataset = xdr_data) as proc by hostname = proc.agent_hostname | filter _time < proc._time and _time > proc._time - 300000

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dataset = dns_events | join type = inner (dataset = xdr_data | alter dns_time = _time) as proc by hostname = proc.agent_hostname | filter _time < proc.dns_time and _time > proc.dns_time - 300000

Question 7

A security analyst is building an XQL query to identify rare process executions on endpoints. The analyst wants to count how many times each process name appears across all endpoints in the last 24 hours, then display only processes that executed fewer than 5 times. The query should show the process name and its execution count, sorted from least to most frequent. Which XQL query achieves this?

Accepted Answer

dataset = xdr_data | filter event_type = ENUM.PROCESS and event_sub_type = ENUM.PROCESS_START | fields actor_process_image_name | comp count() as exec_count by actor_process_image_name | filter exec_count < 5 | sort asc exec_count. The correct query uses the comp stage to aggregate process execution counts grouped by process name, then applies a filter stage to retain only those with fewer than 5 occurrences, and sorts ascending by count. This pipeline correctly identifies rare processes by counting occurrences and filtering post-aggregation, which is a key pattern for threat hunting in XSIAM.

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Question 8

An analyst wants to review only the fields that matter for a phishing triage dashboard and must rename a field called 'email_subject' to 'subject_line' so the dashboard widget binds correctly. Which XQL stage accomplishes the rename while keeping the rest of the pipeline intact?

Accepted Answer

alter subject_line = email_subject. Renaming or deriving a column is the job of the alter stage, which assigns an expression to a new field name without collapsing rows. Assigning the new name to the existing field creates the subject_line column the dashboard widget expects, and the rest of the pipeline continues to see both the original and the renamed field.

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filter subject_line = email_subject

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comp subject_line = email_subject

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fields subject_line = email_subject

Question 9

A security analyst is examining authentication logs in the 'auth_data' dataset. They need to identify source IP addresses that have more than 10 failed login attempts within a 5-minute window. The query should group events into 5-minute buckets based on the event timestamp and count failures per IP per bucket. Which XQL pipeline correctly achieves this?

Accepted Answer

bin timestamp span = 5m as time_bucket | filter action = "failed_login" | stats count() as fail_count by source_ip, time_bucket | filter fail_count > 10. To count failed logins per source IP within 5-minute windows, the query must first bin the timestamp into 5-minute buckets, then aggregate counts by both source IP and the time bucket. Only then can it filter for counts greater than 10. The correct pipeline performs these steps in the proper order, ensuring the time-windowed analysis is accurate.

Answer

filter action = "failed_login" | stats count() as fail_count by source_ip | filter fail_count > 10 | bin timestamp span = 5m

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filter action = "failed_login" | bin timestamp span = 5m as time_bucket | stats count() as fail_count by source_ip | filter fail_count > 10

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filter action = "failed_login" | stats count() as fail_count by source_ip, timestamp | filter fail_count > 10 | bin timestamp span = 5m as time_bucket

Question 10

An analyst needs to write an XQL query that extracts the domain portion from a field named 'url' and then counts how many events are associated with each domain. The 'url' field contains values like 'https://www.example.com/path?query=1'. Which XQL query accomplishes this?

Accepted Answer

dataset = web_logs | alter domain = parse(url, "(?P<domain>https?://[^/]+)") | comp count() as event_count by domain. To extract a substring using a pattern, XQL provides the parse function with a regular expression that includes a named capture group. The regex() function is used for filtering, not extraction. Functions like extract_domain or parse_uri are not part of XQL. The correct query uses parse with a pattern that captures the domain portion, then aggregates counts using comp with a group-by on the extracted field.

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dataset = web_logs | alter domain = extract_domain(url) | comp count() as event_count by domain

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dataset = web_logs | alter domain = parse_uri(url, "domain") | comp count() as event_count by domain

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dataset = web_logs | alter domain = regex("https?://([^/]+)", url) | comp count() as event_count by domain

Question 11

A junior analyst is reviewing an XQL query that uses the `fields` command to select specific columns from a dataset. The analyst wants to include the `event_id`, `timestamp`, and `description` fields in the output, but also wants to rename `description` to `event_description` in the results. Which XQL command should be used to achieve this?

Accepted Answer

fields event_id, timestamp, description as event_description. The `fields` command supports renaming with the `as` keyword, allowing selection and aliasing in one step. This is the correct XQL syntax for the scenario. Other options use invalid commands or functions, or require unnecessary steps.

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select event_id, timestamp, description as event_description

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alter event_description = description | fields event_id, timestamp, event_description

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fields event_id, timestamp, rename(description, event_description)

Question 12

During an investigation an analyst needs to combine process execution events with their corresponding network connection events, matching on both the endpoint hostname and the process identifier, and wants only pairs that exist in both datasets. Which XQL approach is correct?

Accepted Answer

Use an inner `join` stage specifying both the hostname and process ID as the join keys so only records present in both datasets are returned.. Correlating two datasets on shared keys requires a join, and requiring rows to exist in both sources means an inner join. Specifying both hostname and process ID as keys keeps the pairing precise and avoids the fan-out that a single-key match on hostname would introduce. Unions, left joins, and aggregations do not satisfy the strict both-sides requirement.

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Use a `union` stage to concatenate the process dataset and the network dataset into one stream, then filter on matching hostname and process ID.

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Use a `dedup` stage on hostname followed by a `stats` count grouped by process ID to correlate the two datasets.

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Use a left `join` stage on hostname only, then rely on null process ID values to identify unmatched records.

Question 13

An analyst needs to enrich XSIAM event data with external threat intelligence. They want to use an XQL query to check if any source IP in the 'xdr_data' dataset appears in a threat intelligence feed stored as a lookup table named 'threat_feed'. The lookup table contains a field 'ip_address' with known malicious IPs. Which two XQL approaches correctly perform this enrichment? (Choose two.)

Accepted Answer

Use the 'join' command with type 'inner' to combine 'xdr_data' with 'threat_feed' on source_ip = ip_address.. The 'join' command with an inner join filters events to those matching the threat feed, effectively enriching by correlation. The 'lookup' command appends fields from the threat feed to matching events without filtering, preserving all data. Both are valid XQL methods for enriching with external data, depending on whether filtering is desired.

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Use the 'stats' command to count events by source_ip and then manually compare the results with the 'threat_feed' table outside of XQL.

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Use the 'alter' command to create a new field that concatenates source_ip with a static string, then compare with the lookup table.

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Use the 'filter' command with a subquery that selects ip_address from 'threat_feed' and checks if source_ip is in that list.

Question 14

An analyst is reviewing a dataset of endpoint process events in XSIAM. They need to produce a result set that shows, for each unique combination of process name and parent process name, the total number of events and the earliest timestamp observed. Which XQL query structure accomplishes this goal?

Accepted Answer

dataset = xdr_data | filter event_type = ENUM.PROCESS | comp count() as event_count, min(timestamp) as first_seen by process_name, parent_process_name. The comp command is the aggregation operator in XQL, and it supports grouping with a by clause that lists fields separated by commas. To count events and find the earliest timestamp per unique process name and parent process name pair, the correct syntax uses count() and min(timestamp) with by process_name, parent_process_name. This yields exactly the requested output.

Answer

dataset = xdr_data | filter event_type = ENUM.PROCESS | comp count() as event_count, min(timestamp) as first_seen by process_name and parent_process_name

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dataset = xdr_data | filter event_type = ENUM.PROCESS | stats count() as event_count, min(timestamp) as first_seen by process_name, parent_process_name

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dataset = xdr_data | filter event_type = ENUM.PROCESS | comp count() as event_count, earliest(timestamp) as first_seen by process_name, parent_process_name

Question 15

An analyst needs to extract the top-level domain from a field named `url` in the `xdr_data` dataset. The `url` field contains full URLs such as `https://www.example.com/path/page.html`. Which XQL function and syntax correctly extracts `example.com`?

Accepted Answer

| alter domain = extract(url, "https?://([^/]+)", 1). The `extract` function with a regular expression and capture group index is the correct way to isolate a portion of a string in XQL. The pattern `https?://([^/]+)` captures the hostname portion. While the result is `www.example.com` rather than `example.com`, this is the only valid syntax among the options that correctly uses `extract` with a capture group. The other options misuse functions or have invalid syntax.

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| alter domain = substring(url, "https?://", "/")

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| alter domain = replace(url, "https?://([^/]+).*", "$1")

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| alter domain = extract(url, "https?://([^/]+)")