XSIAM-Analyst Data Analysis With XQL Practice Test 5 — 15 Questions

Question 1

An analyst wants to examine the distribution of event severities in a dataset and display the count of events for each severity level. Which XQL command should the analyst use?

Accepted Answer

`stats count() by severity`. To see how many events fall into each severity level, the analyst needs an aggregation grouped by severity. The `stats` command with `count()` by severity returns one row per severity with its event count, which is the standard XQL pattern for distribution analysis and directly answers the question.

Answer

`filter severity != null`

Answer

`sort severity`

Answer

`dedup severity`

Question 2

An analyst is building a query to detect brute-force login attempts. The analyst wants to count failed logins per source IP within each 5-minute window and only surface IPs exceeding 10 failures. Which XQL construct is designed for this time-bucketed aggregation?

Accepted Answer

The `bin` command to group timestamps into 5-minute buckets, followed by `stats count() by source_ip, bin`.. Time-bucketed aggregation requires creating fixed intervals from timestamps and then counting events within each interval. The `bin` command produces those intervals, and `stats count()` grouped by source IP and the bucket returns failures per IP per 5-minute window, enabling the threshold check for brute-force activity.

Answer

The `sort` command on the timestamp field followed by `limit 10`.

Answer

The `dedup` command on the timestamp field to collapse events into 5-minute intervals.

Answer

The `join` command to combine login events with a reference table of time windows.

Question 3

A security analyst is investigating a potential data exfiltration incident. They need to identify all unique destination IP addresses that received more than 100 MB of outbound traffic from any internal host during the last 24 hours, and then display the total bytes sent to each of those IPs. The relevant network traffic data is stored in the 'xdr_data' dataset with fields 'action_local_ip', 'action_remote_ip', and 'action_remote_bytes'. Which XQL query correctly produces the required result?

Accepted Answer

dataset = xdr_data | filter action_local_ip in ipv4_private() | comp sum(action_remote_bytes) as total_bytes by action_remote_ip | filter total_bytes > 100000000. To find destinations that received more than 100 MB in total, the query must first sum all outbound bytes per destination IP, then filter on that sum. Filtering individual events before aggregation would exclude smaller transfers that contribute to the total. Using sum is correct because the requirement is about data volume, not event count. The filter after aggregation must reference the aggregated alias, not the original field.

Answer

dataset = xdr_data | filter action_local_ip in ipv4_private() | comp count(action_remote_bytes) as total_bytes by action_remote_ip | filter total_bytes > 100000000

Answer

dataset = xdr_data | filter action_local_ip in ipv4_private() | comp sum(action_remote_bytes) as total_bytes by action_remote_ip | filter action_remote_bytes > 100000000

Answer

dataset = xdr_data | filter action_local_ip in ipv4_private() and action_remote_bytes > 100000000 | comp sum(action_remote_bytes) as total_bytes by action_remote_ip | filter total_bytes > 100000000

Question 4

An analyst wants to classify network events into risk buckets based on the numeric field `bytes_out`: under 1 MB is 'low', 1 MB to 100 MB is 'medium', and above 100 MB is 'high'. The classification should be added as a new field in the pipeline without removing existing fields. Which approach accomplishes this in XQL?

Accepted Answer

Add an `alter` stage using a chained conditional expression such as `alter risk = if(bytes_out < 1000000, "low", if(bytes_out <= 100000000, "medium", "high"))`.. Creating a derived classification field requires a stage that can compute new field values while preserving the event stream, which is what `alter` does. Nesting conditional expressions inside it evaluates thresholds in order and assigns the correct tier. Filtering only excludes events, aggregating collapses rows, and deduplication removes events without any labeling logic.

Answer

Add a `stats` stage that computes `count()` grouped by `bytes_out`.

Answer

Add a `filter` stage with the three threshold comparisons joined by logical OR operators.

Answer

Add a `dedup` stage on `bytes_out` and let XSIAM automatically infer the risk tier from the value.

Question 5

An analyst is building an XQL query to detect suspicious PowerShell execution. They want to exclude any command line that contains the string 'Invoke-WebRequest' and also exclude any command line that contains 'DownloadString'. Which XQL filter expression correctly applies both exclusions?

Accepted Answer

filter not (command_line contains "Invoke-WebRequest" or command_line contains "DownloadString"). To exclude command lines that contain either of two substrings, the correct approach is to negate a disjunction of contains conditions. The expression 'not (command_line contains "A" or command_line contains "B")' keeps only events where neither substring appears. Using inequality operators or exact-match lists would only filter exact matches and miss embedded occurrences, leaving many suspicious commands undetected.

Answer

filter command_line not in ("Invoke-WebRequest", "DownloadString")

Answer

filter command_line not contains "Invoke-WebRequest" and command_line not contains "DownloadString"

Answer

filter command_line != "Invoke-WebRequest" and command_line != "DownloadString"

Question 6

A threat hunter wants to detect beaconing by finding source-destination pairs whose connection count is unusually high within a one-hour window. The analyst groups events by source IP, destination IP, and a one-hour time bucket, then counts connections. Which XQL feature supports creating that one-hour time bucket for grouping?

Accepted Answer

Use the `bin` function on the timestamp field inside the grouping clause of the `stats` stage to bucket events into fixed one-hour intervals.. Time-bucketed aggregation is achieved by applying a binning function to the timestamp inside the grouping clause of the aggregation stage. That converts continuous timestamps into fixed one-hour intervals so counts can be compared per pair per window. Deduplication, self-joins, and sorting do not create time buckets and cannot yield the required per-hour connection counts.

Answer

Use the `sort` stage on the timestamp field and manually inspect consecutive events that fall within an hour of each other.

Answer

Use the `dedup` stage on the timestamp field to collapse events into hourly representatives before counting.

Answer

Use the `join` stage to merge the dataset with itself on the timestamp field to form hourly groups.

Question 7

An analyst is building an XQL query that pulls from a dataset with millions of events and must reduce the volume of data returned to the console while still preserving counts for reporting. Which TWO statements about XQL pipeline behavior are accurate in this scenario? (Choose two.)

Accepted Answer

Using `stats` with a grouping field collapses matching events into aggregated rows, so the returned row count is typically far smaller than the input event count.. Early filtering minimizes the event set before costly stages run, and grouped aggregation collapses many events into few rows while retaining counts, so both directly serve the goal of reducing returned volume without losing reporting fidelity. Truncating early destroys data, deduplication does not count, and column narrowing does have a performance benefit.

Answer

Field selection stages do not affect performance because XSIAM always materializes every field from storage regardless of the query.

Answer

Adding a `limit` stage at the very start of the pipeline guarantees the aggregation still sees every original event.

Answer

The `dedup` stage preserves every duplicate event while adding a count column for each repeated value.

Question 8

An analyst is hunting for encoded PowerShell activity and needs to match command lines containing base64-style strings. The analyst wants a case-insensitive regular expression match against the command_line field. Which XQL filter expression satisfies this?

Accepted Answer

filter command_line ~= "[A-Za-z0-9+/]{40,}={0,2}". XQL distinguishes literal comparison operators from pattern operators. The ~= operator applies a case-insensitive regular expression, which is required to catch base64 payloads that vary in letter casing. The pattern itself uses a quantifier to find long encoded runs with optional padding, matching typical encoded PowerShell command lines rather than short coincidental strings.

Answer

filter command_line = "[A-Za-z0-9+/]{40,}={0,2}"

Answer

filter command_line contains "[A-Za-z0-9+/]{40,}={0,2}"

Answer

filter command_line in ("[A-Za-z0-9+/]{40,}={0,2}")

Question 9

A threat hunter is analyzing DNS activity in XSIAM using XQL. The hunter wants to identify domains that were queried by more than 50 unique endpoints within the last hour, as this could indicate a broad beaconing or DNS tunneling campaign. The hunter writes a query that groups DNS query events by the queried domain name and counts distinct source endpoints. Which TWO XQL components are required to correctly compute the number of unique endpoints per domain? (Choose two.)

Accepted Answer

Apply a filter stage after aggregation to retain only domains where the distinct endpoint count exceeds 50.. To count unique endpoints per domain, the hunter must use the comp stage with distinct_count() on the endpoint identifier grouped by domain, then apply a filter stage to keep only domains exceeding the 50-endpoint threshold. These two components together compute and threshold the distinct endpoint count, enabling detection of broad DNS campaigns.

Answer

Use the arrayexpand stage to expand the list of queried domains into separate rows before counting endpoints.

Answer

Use the join stage to combine DNS events with an endpoint inventory dataset to resolve endpoint identifiers to unique hostnames.

Answer

Use the dedup stage on the endpoint identifier field before aggregation to remove duplicate endpoint entries per domain.

Question 10

An analyst needs to create an XQL query that enriches network connection events with geographic location data. The analyst has a lookup table named 'ip_geolocation' that maps IP addresses to country codes. The goal is to add a 'country' field to each event based on the destination IP address. Which XQL stage and syntax correctly performs this enrichment?

Accepted Answer

dataset = xdr_data | filter event_type = ENUM.NETWORK | lookup ip_geolocation destination_ip as ip_address return country. The lookup stage is specifically designed for enriching XQL query results with data from lookup tables. By matching destination_ip against the ip_address field in the ip_geolocation table and returning the country field, the analyst efficiently adds geographic context to network events. This is the standard enrichment pattern in XSIAM for adding external data to query results.

Answer

dataset = xdr_data | filter event_type = ENUM.NETWORK | join type = left (dataset = ip_geolocation) as geo ip_address = geo.ip | fields destination_ip, geo.country

Answer

dataset = xdr_data | filter event_type = ENUM.NETWORK | alter country = map_lookup("ip_geolocation", destination_ip)

Answer

dataset = xdr_data | filter event_type = ENUM.NETWORK | enrich ip_geolocation destination_ip as ip_address return country

Question 11

A threat hunter is analyzing authentication logs in XSIAM using XQL. They want to identify potential brute-force attacks by finding source IP addresses that have more than 10 failed login attempts within a 5-minute window. The logs are in the 'auth_logs' dataset with fields 'src_ip', 'event_time', and 'status' (where 'status' is either 'success' or 'failure'). Which two XQL stages or functions are essential to achieve this? (Choose two.)

Accepted Answer

filter status = "failure". To detect brute-force attacks, the query must first isolate failed login events using a filter on status. Then it must count those failures per source IP within a 5-minute window, which requires an aggregation grouped by source IP and a time bucket. These two steps are the core of the detection. Sorting, deduplication, or external joins are either counterproductive or non-essential for the fundamental count-based logic.

Answer

sort by event_time desc

Answer

join with a threat intelligence dataset on src_ip

Answer

dedup src_ip

Question 12

An analyst is reviewing XQL query results and notices that the 'actor_process_command_line' field contains long command-line strings that make the output difficult to read. The analyst wants to extract only the first 100 characters of this field into a new field named 'short_cmd' for easier triage. Which XQL stage and function should the analyst use?

Accepted Answer

Use the alter stage with the substr() function to create short_cmd as a substring of actor_process_command_line.. The alter stage is the correct XQL stage for creating derived fields using expressions. By applying the substr() function to actor_process_command_line, the analyst creates a new short_cmd field containing only the first 100 characters. This preserves the original field while providing a more readable version for triage, which is a common practice when dealing with verbose command-line data.

Answer

Use the comp stage with the substr() function to aggregate command-line strings into 100-character segments.

Answer

Use the fields stage with the substr() function to select and truncate actor_process_command_line.

Answer

Use the filter stage with the substr() function to replace actor_process_command_line with its first 100 characters.

Question 13

A junior analyst is reviewing XQL query results and notices that the 'event_id' field is present in some rows but missing in others. They need to filter the results to only include rows where 'event_id' is not null. Which XQL filter condition should they use?

Accepted Answer

filter event_id is not null. To filter for rows where a field is not null, XQL provides the 'is not null' operator. This explicitly checks that the field contains a value, correctly excluding rows where the field is missing or null. Other approaches like comparing to null or empty string do not accurately handle null semantics and can lead to incorrect results.

Answer

filter event_id != null

Answer

filter event_id exists

Answer

filter event_id <> ""

Question 14

An analyst is building a query to detect abnormal process behavior. They want to calculate the ratio of network connections to process executions for each process name, but only for processes that have at least 5 executions. The relevant events are in the 'xdr_data' dataset with an 'event_type' field indicating 'process' or 'network'. Which XQL query correctly computes this ratio?

Accepted Answer

filter event_type in ("process", "network") | stats count() as cnt by process_name, event_type | stats sum(if(event_type="process", cnt, 0)) as exec_count, sum(if(event_type="network", cnt, 0)) as net_count by process_name | filter exec_count >= 5 | alter ratio = net_count / exec_count. To compute the ratio of network connections to process executions, the query must separately count network and process events per process name, then filter for processes with at least 5 executions, and finally divide network count by execution count. The correct query uses conditional sums in a second 'stats' to derive these counts before calculating the ratio.

Answer

filter event_type in ("process", "network") | stats count() as cnt by process_name, event_type | filter cnt >= 5 | stats sum(cnt) as total by process_name | alter ratio = total / cnt

Answer

filter event_type in ("process", "network") | stats count() as cnt by process_name | filter cnt >= 5 | alter ratio = count(event_type="network") / count(event_type="process")

Answer

filter event_type in ("process", "network") | stats count() as total by process_name, event_type | stats sum(total) as total_count, sum(if(event_type="network", total, 0)) as net_count by process_name | filter total_count >= 5 | alter ratio = net_count / total_count

Question 15

An analyst is building an XQL query to detect brute-force login attempts. The query must count failed login events per source IP within 10-minute windows and then filter for counts exceeding 20. Which two XQL commands or functions are essential to achieve this? (Choose two.)

Accepted Answer

stats. To detect brute-force attempts, the analyst must group events into time windows using `bin` and then aggregate counts using `stats`. The `bin` command creates the 10-minute buckets, and `stats` counts the failed logins per source IP within each bucket. A subsequent filter on the count would complete the detection. The other commands are not needed for this specific aggregation.

Answer

arrayindex

Answer

dedup

Answer

join