SPLK-1002 Practice Test 36 — 15 Questions

Question 1

A security analyst uses Splunk to ingest firewall logs from multiple locations. The index is 'firewall' and the sourcetype is 'fw_log'. Each event contains fields: src_ip, dest_ip, action, bytes, and time. The analyst needs to find how many unique source IPs have been logged in the last hour to report potential scanning activity. The search should be efficient and accurate, returning only the total count of distinct source IPs. Which search accomplishes this?

Accepted Answer

index=firewall sourcetype=fw_log earliest=-1h | stats dc(src_ip) as UniqueIPs. Option D is correct because it uses the `stats dc(src_ip)` command, which directly calculates the distinct count of source IPs in a single, efficient pass over the data. The `earliest=-1h` time filter restricts the search to the last hour, and the `as UniqueIPs` alias provides the exact output requested: a single number representing the total count of unique source IPs.

Answer

index=firewall sourcetype=fw_log earliest=-1h | dedup src_ip | stats count as UniqueIPs

Answer

index=firewall sourcetype=fw_log earliest=-1h | top src_ip | stats count as UniqueIPs

Answer

index=firewall sourcetype=fw_log | timechart dc(src_ip) span=1h

Question 2

A large financial institution uses Splunk to consolidate logs from thousands of ATMs. Each ATM sends a heartbeat event every 5 minutes containing fields: atm_id, timestamp, status (OK or ERROR), and firmware_version. The operations team wants to find the number of ATMs that have reported at least one ERROR status in the last hour. The initial search is: index=atm sourcetype=heartbeat status=ERROR | dedup atm_id | stats count. However, this search returns a count that is too high because some ATMs report multiple errors within the hour. The team needs an accurate count of ATMs that had any error, regardless of how many error events each ATM generated. The search must be efficient due to the high volume of events. Which approach should be used?

Accepted Answer

index=atm sourcetype=heartbeat status=ERROR | stats dc(atm_id) as errored_atms. Option B is correct because `stats dc(atm_id)` computes the distinct count of `atm_id` values, directly giving the number of unique ATMs that had at least one ERROR event in the last hour. This is efficient as it processes all matching events in a single pass without needing intermediate deduplication or subsearches, which is critical for high-volume ATM log data.

Answer

index=atm sourcetype=heartbeat status=ERROR | stats count(atm_id) as error_events | eval error_events

Answer

index=atm sourcetype=heartbeat status=ERROR | stats count by atm_id | where count >=1 | dedup atm_id | stats count

Answer

index=atm sourcetype=heartbeat status=ERROR | stats values(atm_id)

Question 3

A network operations team uses Splunk to monitor netflow data stored in index='net' and sourcetype='netflow'. The events contain fields: src_ip, dest_ip, bytes, and protocols. The team needs to identify the top 5 source IPs by total bytes transferred (based on the bytes field). For each of those top source IPs, they also want to list the destination IPs and the number of times they communicated. The data volume is large, so performance is important. Which SPL approach returns the desired results efficiently?

Accepted Answer

index=net sourcetype=netflow [ search index=net sourcetype=netflow | stats sum(bytes) as total_bytes by src_ip | sort -total_bytes | head 5 | fields src_ip ] | stats count by src_ip, dest_ip. Option C is correct because it uses a subsearch to first identify the top 5 source IPs by total bytes, then passes those IPs to the outer search to efficiently compute the count of communications per destination IP. This approach minimizes the data processed in the outer search by filtering only the relevant source IPs, which is critical for performance on large netflow datasets.

Answer

index=net sourcetype=netflow | eventstats sum(bytes) as total_bytes by src_ip | stats count by src_ip, dest_ip

Answer

index=net sourcetype=netflow | stats sum(bytes) as total_bytes, count by src_ip, dest_ip | sort -total_bytes | head 5

Answer

index=net sourcetype=netflow | top limit=5 src_ip by bytes | fields src_ip | search index=net sourcetype=netflow | stats count by src_ip, dest_ip

Question 4

A medium-sized company uses Splunk to monitor its e-commerce platform. The platform generates around 10 million events per day from web servers, application logs, and databases. The security team wants to identify the top 10 IP addresses that trigger the most 403 Forbidden errors in the last 24 hours. However, when they run the search: index=ecom sourcetype=web status=403 | top src_ip, the search takes over 5 minutes to complete and sometimes times out. The team needs a faster approach that still accurately identifies the top IPs. The team's Splunk environment uses indexers and a search head. The data is not accelerated. What should the team do to improve search performance?

Accepted Answer

Create a report acceleration summary for the search or implement summary indexing. Option D is correct because report acceleration or summary indexing pre-computes and stores the results of the search, allowing subsequent runs to retrieve the aggregated data almost instantly. Given the environment has indexers and a search head but no acceleration, enabling report acceleration on the search creates a summary that updates periodically, bypassing the need to scan all raw events each time. This directly addresses the timeout issue by reducing the per-search data volume to the pre-built summary.

Answer

Replace 'top' with 'rare' to reduce the amount of data processed

Answer

Use 'stats count by src_ip | sort - count | head 10' instead of top

Answer

Add 'fields src_ip' before top to remove other fields

Question 5

A small business uses Splunk to monitor their point-of-sale (POS) system. They have a data model named 'POS_Transactions' that is not accelerated. The owner wants to create a simple dashboard showing daily sales totals. They write a search using |tstats against the data model, but it returns 'No events found'. A plain search over the same index returns expected results. What should the owner do to resolve this?

Accepted Answer

Modify the search to use |tstats summariesonly=t or switch to using |datamodel or |search.. The `|tstats` command requires an accelerated data model by default; without acceleration, it returns no results because it queries the summary database, not the raw events. Option A correctly resolves this by either using `summariesonly=t` to force `|tstats` to search raw data or switching to `|datamodel` or `|search` which operate directly on the index. This aligns with Splunk's behavior where `|tstats` is optimized for accelerated summaries but can be overridden to access raw events.

Answer

Enable acceleration on the data model and wait for the summary to build.

Answer

Add a constraint to the root event to match POS logs.

Answer

Change the time range to include the current day only.

Question 6

You are a Splunk administrator for a large e-commerce company. The company ingests approximately 500 GB of web server logs per day into a single index named 'web_logs'. A data model named 'Web_Transactions' has been created to analyze user browsing behavior. The data model has a root event with no constraints, and three child objects: 'Page_Views', 'Searches', and 'Purchases'. Each child object has a constraint based on a key-value pair in the logs: e.g., 'action=view', 'action=search', 'action=purchase'. The data model is accelerated with a 7-day summary, but reports that query specific child objects are taking over 10 minutes to return. The reports use |tstats and filter on common fields like 'user_id' and 'session_id'. The admin suspects the acceleration summary is too large. Which of the following actions will most effectively reduce report latency while maintaining the ability to analyze all three transaction types?

Accepted Answer

Add a constraint to the root event to include only events that match the action field values (view, search, purchase).. Option D is correct because adding a constraint to the root event to filter only events with action=view, action=search, or action=purchase reduces the size of the acceleration summary. The root event currently has no constraints, so the acceleration summary includes all 500 GB of daily web logs, even though only three action types are needed. By constraining the root event, the summary stores only relevant data, making |tstats queries on child objects much faster.

Answer

Use the |datamodel command with the 'search' parameter instead of |tstats.

Answer

Remove the child objects and use only the root event for all reports.

Answer

Increase the acceleration summary time range to 30 days to capture more data in one summary.

Question 7

You are working as a Splunk consultant for a financial services firm. They have multiple data sources: application logs, database audit logs, and network firewall logs. The security team needs to correlate events across these sources to detect potential fraud. You decide to create a data model named 'Security_Events'. The data model will be used with tstats for real-time dashboards. The logs vary in volume: application logs are 200 GB/day, audit logs are 50 GB/day, and firewall logs are 100 GB/day. The firm wants to optimize performance and storage. The data model currently has one root event with no constraints and three child objects with constraints based on sourcetype. The admin is concerned about acceleration storage costs. Which of the following is the best approach to balance performance and storage?

Accepted Answer

Enable acceleration only on the child objects that are used in the most critical dashboards, and leave others unaccelerated.. Option B is correct because enabling acceleration only on the child objects used in the most critical dashboards balances performance and storage. The tstats command can leverage accelerated child objects for fast queries, while unaccelerated objects avoid unnecessary storage overhead. Given the high volume of logs (350 GB/day total), selective acceleration minimizes storage costs while still providing real-time performance for key fraud detection dashboards.

Answer

Disable acceleration on all objects and rely on the base search for queries.

Answer

Enable acceleration on the root event only and disable acceleration on child objects.

Answer

Remove the child objects and merge all constraints into the root event.

Question 8

A large enterprise uses Splunk to monitor network traffic from thousands of devices. The events contain a field 'dest_ip' that you want to enrich with a company-specific asset owner and department. The asset data is stored in an SQL database that is updated daily. The Splunk administrator has set up a DB Connect app to query the database. However, the performance of the search is very slow when using dbquery to lookup asset information for each event. The team needs to improve performance while still maintaining daily updates. Which approach should the team take?

Accepted Answer

Use DB Connect to export the asset data to a CSV file or KV Store collection on a daily schedule, then use a CSV or KV Store lookup in searches. Option C is correct because exporting the asset data from the SQL database to a CSV file or KV Store collection on a daily schedule leverages the DB Connect app for bulk data transfer rather than per-event queries. This approach avoids the overhead of repeated dbquery calls during search time, which cause performance degradation. Using a CSV or KV Store lookup then provides fast, indexed lookups that can be refreshed daily to maintain data freshness.

Answer

Create a search-time field extraction that parses the dest_ip to derive owner and department

Answer

Increase the number of indexers to parallelize the lookup operations

Answer

Use the 'lookup' command with a KV Store lookup that is populated from the database in real time

Question 9

A company uses Splunk to monitor web server logs. They have a lookup table that maps IP addresses to geographic locations (city, country). The lookup is defined as a CSV file with fields: ip, city, country. The lookup definition is named 'geo'. The team wants to automatically add city and country to every web event at index time, so that all future searches have this enrichment without adding the lookup command. The team tries to set up an automatic lookup in props.conf for the sourcetype 'web_access', but the city and country fields still do not appear in the events. They verify that the lookup file exists and that the lookup definition works when used manually with the lookup command. What is the most likely cause of the automatic lookup not working?

Accepted Answer

The automatic lookup definition in props.conf is not correctly configured for the intended sourcetype. Option D is correct because the automatic lookup definition in props.conf must be correctly configured for the intended sourcetype. The most common misconfiguration is specifying the wrong sourcetype or incorrect syntax in the TRANSFORMS directive, which prevents the lookup from being applied at index time. Since the lookup works manually, the issue is with the props.conf configuration, not the lookup file or definition itself.

Answer

Index-time lookups are not supported; enrichment can only be done at search time

Answer

The lookup file must be in KV Store format for index-time lookups

Answer

The lookup must be invoked with the 'lookup' command in the search to populate the fields

Question 10

A Splunk user needs to perform a lookup that matches events based on a field 'userid' to a lookup table that contains 'userid', 'full_name', and 'email'. The lookup table is a CSV file named 'users.csv' located in the default lookup directory. The user runs the search: index=main | lookup users.csv userid OUTPUT full_name, email. However, the search returns an error that the lookup table 'users.csv' was not found. What is the most likely reason for this error?

Accepted Answer

The lookup command must reference the lookup definition name, not the CSV file name directly. The `lookup` command in Splunk requires a lookup definition name, not the raw CSV filename. The lookup definition is created in Splunk's configuration (e.g., transforms.conf) and maps a logical name to the actual CSV file. Using the filename directly bypasses this definition, causing Splunk to report the file as not found because it searches for a definition, not the file path.

Answer

The CSV file must have a .csv extension, but the command should omit the extension

Answer

The fields 'full_name' and 'email' are not present in the CSV file

Answer

The CSV file is not in the correct format; it should have a header row

Question 11

A large enterprise Splunk environment has a heavy forwarder sending 2 TB of log data per day. An operator builds a dashboard that displays a real-time chart of events per second across all data sources, using the search 'index=* | stats count by sourcetype' with a real-time window of last 10 minutes. The dashboard is extremely slow, often timing out. The operator suspects the search is too broad. Which optimization strategy should be implemented first?

Accepted Answer

Implement a summary index that aggregates events per second by sourcetype every minute, and have the dashboard search the summary index. Option B is correct because a summary index pre-aggregates the events-per-second data by sourcetype at a regular interval (e.g., every minute), drastically reducing the data volume the dashboard must scan. Instead of processing 2 TB of raw logs in real time, the dashboard queries a small, precomputed table, eliminating the performance bottleneck caused by the heavy forwarder's high ingestion rate.

Answer

Replace the real-time search with a base search that runs every 10 minutes

Answer

Change the real-time window to a 10-minute historical search and enable report acceleration

Answer

Create a data model for all sourcetypes and use acceleration on the data model

Question 12

A junior analyst creates a dashboard to monitor server CPU usage. The dashboard contains a single panel with a line chart showing CPU percentage over the last 24 hours. The analyst then adds a second panel that displays the same data but as a single value showing the average CPU. Both panels use the exact same search string. The dashboard loads slowly, and users complain of wait times. The analyst wants to improve performance without changing the displayed data. Which course of action is best?

Accepted Answer

Convert the dashboard to use a base search and post-process the panels. Option B is correct because using a base search with post-process searches allows both panels to share a single dataset, reducing redundant search execution. Instead of running the same heavy search twice, the base search runs once, and each panel applies lightweight post-processing (e.g., timechart vs. stats) on the cached results. This directly addresses the slow load time without altering the displayed data.

Answer

Keep both panels but add more resources to the Splunk server

Answer

Remove the single value panel and embed its value in the line chart title

Answer

Enable report acceleration on one of the panels

Question 13

A large organization's security team has a dashboard that displays the top 10 source IPs by number of failed login attempts. The dashboard uses a search: index=security sourcetype=login action=failure | top limit=10 src_ip. Lately, the dashboard shows incorrect data (e.g., IPs that are not actually top). However, when the same search is run in the Search app, it shows correct results. The dashboard is scheduled to refresh every 10 minutes. What is the most likely cause?

Accepted Answer

The dashboard uses a summary index with stale data. Option C is correct because the dashboard is likely configured to use a summary index that is populated by a scheduled search. If the summary index is not updated frequently enough or contains stale data, the dashboard will display incorrect results even though the live search in the Search app returns accurate data. The 10-minute refresh interval does not repopulate the summary index; it only re-queries the already stale summary data.

Answer

The dashboard's time range is different from the manual search

Answer

The dashboard is using a real-time search that causes data inconsistency

Answer

The user has incorrect field permissions

Question 14

You are a Splunk administrator at a large e-commerce company. The operations team has created a real-time dashboard to monitor website performance. The dashboard includes multiple panels: a line chart showing page load times over the last 60 minutes, a single value showing the number of active users, and a table listing the top 10 slowest pages. The dashboard refreshes every 30 seconds. Recently, users have reported that the dashboard is very slow to load and sometimes times out. The underlying searches are not accelerated. The dashboard uses a shared time range picker set to 'Last 60 minutes'. The index for web logs receives about 2 GB of data per hour. The team wants to improve performance without losing real-time capability. Which approach best addresses the problem?

Accepted Answer

Reduce the time range to 'Last 15 minutes' and keep the 30-second refresh.. Option A is correct because reducing the time range to 'Last 15 minutes' significantly decreases the volume of data each search must scan, which directly reduces search execution time and dashboard load time. Since the dashboard refreshes every 30 seconds, a shorter time range still provides near-real-time visibility while preventing timeouts. The underlying searches are not accelerated, so limiting the data scanned is the most effective immediate performance improvement.

Answer

Increase the refresh interval to 5 minutes to reduce search frequency.

Answer

Implement summary indexing for the searches and run them every 5 minutes.

Answer

Remove the single value panel to reduce the number of searches.

Question 15

You are a Splunk analyst for a financial services firm. You need to create a weekly report for management showing the total transaction value and number of transactions per day, broken down by transaction type (credit, debit, transfer). The data is in index=transactions with fields: trans_date, trans_type, amount. The report should be sent via email every Monday morning at 8 AM. You have created a report with the search: `index=transactions | timechart sum(amount) by trans_type`. However, the timechart shows only one series because the trans_type field has multiple values. You need to fix the search so that it correctly separates by trans_type. Additionally, you need to schedule the report. What should you do?

Accepted Answer

Use `| eval trans_type=split(trans_type, ",") | mvexpand trans_type | timechart sum(amount) by trans_type`.. Option B is correct because the trans_type field contains multiple values in a single field (e.g., 'credit,debit,transfer'), so you must first split the values using `split()` and then expand them with `mvexpand` before using `timechart` to correctly separate the series. This ensures each transaction type is treated as an individual event for the aggregation.

Answer

Add `| stats sum(amount) by _time, trans_type` before timechart.

Answer

Use `| chart sum(amount) by trans_type` instead of timechart.

Answer

Use `| makemv trans_type | timechart sum(amount) by trans_type`.