SPLK-1002 Timed Practice Test 1 — 10 Questions

Question 1

During a data model acceleration build, the following error appears in splunkd.log: 'Data model acceleration: not enough memory to complete summary build.' Which best practice should the administrator implement to prevent this error?

Accepted Answer

Increase the memory allocation for the data model acceleration process.. Option B is correct because the error 'not enough memory to complete summary build' indicates that the data model acceleration process has exhausted its allocated memory. Increasing the memory allocation for the data model acceleration process (via the limits.conf or the data model acceleration settings) directly addresses this resource constraint, allowing the summary to build successfully.

Answer

Remove unnecessary fields from the data model to reduce complexity.

Answer

Reduce the summary range to less than 7 days.

Answer

Use tstats instead of data model acceleration for queries.

Question 2

Which TWO are best practices for creating data models in Splunk? (Choose two.)

Accepted Answer

Use data model acceleration to improve query performance on large datasets.. Option A is correct because data model acceleration pre-computes and stores aggregated data in the form of summaries (TSIDX files), which dramatically reduces query latency on large datasets by avoiding full scan of raw events. This is a best practice for optimizing performance when using data models in Splunk.

Answer

Base data models on indexed fields rather than search-time extracted fields.

Answer

Create many-to-many relationships between root events and child datasets.

Answer

Include all available fields to ensure maximum flexibility.

Question 3

A user reports that a data model acceleration is consuming excessive disk space on the indexer. The data model has a summary range of 90 days. Which action is best to reduce disk space usage while maintaining acceptable query performance?

Accepted Answer

Reduce the summary range to 30 days.. Reducing the summary range from 90 days to 30 days directly decreases the amount of data that the acceleration precomputes and stores on the indexer. This minimizes disk space consumption while still accelerating queries for the most recent, commonly accessed data. Maintaining a shorter summary range ensures acceptable performance for recent queries without the overhead of storing summaries for older, less frequently accessed time periods.

Answer

Increase the acceleration frequency to rebuild summaries more often.

Answer

Disable acceleration for the data model.

Answer

Delete old indexed data that is not frequently queried.

Question 4

A security analyst wants to investigate a suspicious IP address that appeared in multiple log sources. Which Splunk feature is best suited to quickly find all events containing that IP across all indexed data?

Accepted Answer

Search bar in the Search & Reporting app. The Search bar in the Search & Reporting app is the primary interface for running ad-hoc searches across all indexed data. By entering the suspicious IP address directly into the search bar, the analyst can quickly retrieve all events containing that IP from any log source, leveraging Splunk's search-time field extraction and index-time data ingestion.

Answer

Data Summary

Answer

Dashboard panel

Answer

Alert action

Question 5

A search includes the command '| stats dc(user) by host'. What does this command return?

Accepted Answer

The number of distinct users per host. The `dc(user)` function in the `stats` command calculates the distinct count of the `user` field values. When combined with `by host`, it returns the number of unique users for each host. This is why option D is correct.

Answer

The number of unique hosts per user

Answer

The count of events per host

Answer

The sum of user values per host

Question 6

A large e-commerce company uses Splunk to monitor their web application. The operations team has noticed that the search for tracking user sessions is taking too long and consuming excessive resources. The current search is:

index=web sourcetype=access_combined
| stats count by clientip, sessionid, productid
| sort - count

The index contains over 10 billion events per day. The team wants to reduce the search time while still being able to identify the top 10 most active sessions (combinations of clientip and sessionid) that involve more than 5 product views. They also need to exclude any sessions that originated from internal IPs (10.0.0.0/8). Which approach would achieve this most efficiently?

Accepted Answer

Add 'clientip!=10.0.0.0/8' in the base search, then use 'stats count by clientip, sessionid', then 'where count>5', then 'sort - count | head 10'.. Option D is correct because it filters out internal IPs early in the base search using `clientip!=10.0.0.0/8`, which reduces the dataset before any transformation. It then uses `stats count by clientip, sessionid` to aggregate sessions, applies `where count>5` to enforce the minimum product views, and finally sorts and limits to the top 10. This approach minimizes resource consumption by pushing filtering as early as possible and avoids unnecessary fields like `productid`.

Answer

Use 'eventstats count by clientip, sessionid' and then filter where count > 5, then sort and head.

Answer

Use the 'transaction' command to group events by clientip and sessionid, then filter by duration.

Answer

Add a 'where' command after stats to filter out internal IPs and use 'head 10' at the end.

Question 7

You are a Splunk admin for a large enterprise with multiple distributed Splunk components. The security team frequently runs searches that use a large CSV lookup file (500MB) containing threat intelligence indicators. They report that searches are slow and sometimes time out. The lookup file is updated hourly via an automated script. The team currently uses the 'lookup' command in every search. You need to improve performance without sacrificing data freshness. Your environment has a search head cluster and indexer cluster. The lookup file is stored on a shared filesystem accessible to all search heads. Which single approach will best improve search performance while maintaining hourly updates?

Accepted Answer

Configure the lookup as a time-based lookup with a filter condition to only apply to events with matching IP fields, and use automatic lookup to avoid manual command.. Option A is correct because configuring the lookup as a time-based lookup with a filter condition reduces the number of events that need to be matched against the 500MB CSV, and using an automatic lookup eliminates the need for the manual 'lookup' command in every search. This approach improves performance by limiting the lookup scope to relevant events (e.g., only those with matching IP fields) while still allowing the hourly script to update the CSV file, maintaining data freshness.

Answer

Increase the search concurrency limit on the search head to allow more parallel lookups.

Answer

Convert the CSV to a KV Store collection and use the 'lookup' command with the KV Store lookup.

Answer

Move the CSV file to each indexer and use index-time field lookup.

Question 8

A security team wants to create a data model to analyze authentication events from multiple sources (Windows Event Log, Linux syslog, and VPN logs). The data model should normalize the fields for user, source IP, and action (success/failure). Which Splunk best practice should be applied when designing this data model?

Accepted Answer

Define a single data model that maps fields to the Common Information Model (CIM).. Option C is correct because the Common Information Model (CIM) provides a standardized, normalized schema for security events. By mapping fields like user, src_ip, and action to CIM field names, the data model ensures consistent searching and correlation across heterogeneous sources (Windows Event Log, Linux syslog, VPN logs) without per-source customizations. This approach leverages Splunk's built-in CIM add-on to accelerate data model design and maintain interoperability with security analytics apps.

Answer

Use event types to categorize authentication events and then create a data model based on event types.

Answer

Create separate data models for each data source to avoid field conflicts.

Answer

Create field aliases in props.conf for each source to rename fields to a common name, then use a simple data model.

Question 9

What is the purpose of this search?

Accepted Answer

To display the 5 most common HTTP status codes in the web index.. The search uses `top` to find the most common values in the `status` field, limited to 5 results. The `top` command counts occurrences and sorts by count descending, so it returns the 5 most frequent HTTP status codes from the web index. Option C correctly describes this behavior.

Answer

To list the first 5 status codes in alphabetical order.

Answer

To filter events with status codes that appear less than 5 times.

Answer

To show the 5 most recent events sorted by status code.

Question 10

A user notices that a calculated field defined in props.conf is not appearing in search results. Which of the following is the most likely cause?

Accepted Answer

The source fields used in the calculation are not extracted.. Calculated fields in Splunk are evaluated at search time based on existing extracted source fields. If the source fields referenced in the calculation are not extracted (e.g., due to missing or incorrect field extraction configurations), the calculated field will not appear in search results. Option B correctly identifies this dependency.

Answer

The calculated field requires index-time field extraction.

Answer

The calculated field is defined in a field alias configuration.

Answer

The indexer is not configured to apply calculated fields.