Troubleshooting practice questions

If a data model is accelerated, where are the summary files stored on the indexer?

Trap 1: In the main index

Accelerated summaries are stored in a separate tsidx summary directory under the indexer's dispatch area, not inside the main index buckets. Storing them in the main index would be tempting since raw events live there, but the acceleration process writes outside the index.

Trap 2: In the user's home directory

Accelerated data model summaries are written to the indexer's dispatch directory under $SPLUNK_DB, not a user home directory, so the search head cannot find them. A home directory holds user-specific files such as search artefacts, which is why it appears plausible for per-user storage.

Trap 3: In the /var/lib/splunk/storage directory

Summary files live under the indexer's dispatch directory within $SPLUNK_DB, not /var/lib/splunk/storage, which holds other Splunk artefacts. The path sounds like a plausible Splunk data location, making it tempting, but it is not where accelerated summaries are written.

A
In the main index
Why it fails: Accelerated summaries are stored in a separate tsidx summary directory under the indexer's dispatch area, not inside the main index buckets. Storing them in the main index would be tempting since raw events live there, but the acceleration process writes outside the index.
B
In the user's home directory
Why it fails: Accelerated data model summaries are written to the indexer's dispatch directory under $SPLUNK_DB, not a user home directory, so the search head cannot find them. A home directory holds user-specific files such as search artefacts, which is why it appears plausible for per-user storage.
C
In the _datamodel_summary index
Accelerated data models persist their precomputed summaries in the _datamodel_summary index, a dedicated internal index on the indexer. This satisfies the stem's constraint of identifying the exact storage location for acceleration artefacts, rather than the original source index or a separate filesystem path.
D
In the /var/lib/splunk/storage directory
Why it fails: Summary files live under the indexer's dispatch directory within $SPLUNK_DB, not /var/lib/splunk/storage, which holds other Splunk artefacts. The path sounds like a plausible Splunk data location, making it tempting, but it is not where accelerated summaries are written.

Question 2hardmultiple choice

A Splunk power user is troubleshooting a search that uses a calculated field named 'response_time' defined as 'duration_ms / 1000'. The field 'duration_ms' is extracted from raw events, but the calculated field returns null values for some events. Upon investigation, the user finds that 'duration_ms' is sometimes missing or contains non-numeric characters. Which of the following is the most likely reason for the null values in 'response_time'?

Trap 1: The field 'duration_ms' is not indexed, so it is not available for…

Calculated fields operate on search-time extracted fields, which do not need to be indexed. Indexed fields are a separate concept; search-time fields are extracted from raw data during search. The availability of 'duration_ms' for calculation depends on successful extraction, not indexing. Thus, this is not the reason for null values.

Trap 2: The calculated field expression is evaluated before field…

Calculated fields are evaluated after field extraction during the search-time field resolution phase. The order of operations ensures that extracted fields are available before calculated fields are computed. Therefore, the timing of evaluation is not the cause of null values; the issue lies with the data itself or the expression's handling of non-numeric values.

Trap 3: The calculated field is defined with the wrong data type, causing…

Calculated fields in Splunk do not have explicit data types; they are dynamically evaluated. Splunk does not discard results based on data type mismatches; instead, it returns null when operations cannot be performed. The problem is not a misconfigured data type but the presence of non-numeric data in the source field.

A
The field 'duration_ms' is not indexed, so it is not available for calculated fields.
Why it fails: Calculated fields operate on search-time extracted fields, which do not need to be indexed. Indexed fields are a separate concept; search-time fields are extracted from raw data during search. The availability of 'duration_ms' for calculation depends on successful extraction, not indexing. Thus, this is not the reason for null values.
B
The eval expression fails to handle non-numeric or missing values, resulting in null for those events.
In Splunk, arithmetic operations on non-numeric or null values yield null. Since 'duration_ms' sometimes contains non-numeric characters or is missing, the division operation cannot produce a numeric result, leading to null for 'response_time'. To avoid this, the expression should include conditional logic, such as using the tonumber() function or a coalesce() to provide a default value.
C
The calculated field expression is evaluated before field extraction, so 'duration_ms' is not yet available.
Why it fails: Calculated fields are evaluated after field extraction during the search-time field resolution phase. The order of operations ensures that extracted fields are available before calculated fields are computed. Therefore, the timing of evaluation is not the cause of null values; the issue lies with the data itself or the expression's handling of non-numeric values.
D
The calculated field is defined with the wrong data type, causing Splunk to discard invalid results.
Why it fails: Calculated fields in Splunk do not have explicit data types; they are dynamically evaluated. Splunk does not discard results based on data type mismatches; instead, it returns null when operations cannot be performed. The problem is not a misconfigured data type but the presence of non-numeric data in the source field.

Question 3hardmultiple choice

A Splunk administrator is troubleshooting a data model that has been accelerated for several months. Users report that recent data is missing from Pivot reports, but older data is present. The acceleration summary files exist and are not corrupt. What should the administrator check first?

Trap 1: The indexer's disk space, because if the disk is full, new events…

If the disk were full, new events would not be indexed at all, affecting raw searches as well. The scenario specifies that only the accelerated data model is missing recent data, implying that raw events are present. Therefore, disk space is not the primary suspect.

Trap 2: The data model's acceleration schedule, to ensure it runs…

The acceleration schedule determines how often the summary is updated. If it runs infrequently, there might be a delay, but it would eventually catch up. The scenario describes a persistent lack of recent data, which points to a time range limit rather than a schedule issue.

Trap 3: The search head's clock synchronization, as time skew can cause…

While clock skew can cause indexing issues, it would affect all data, not just recent data in an accelerated data model. Moreover, the scenario states that older data is present, which suggests the acceleration is working for older periods. Clock skew is less likely than an acceleration time range misconfiguration.

A
The data model's acceleration time range setting, to ensure it covers the current time period.
The acceleration time range setting defines how far back the summary covers. If it is set to a fixed historical window (e.g., last 30 days) and not updated, recent data beyond that window will not be included. Checking this setting is the first step to resolve missing recent data.
B
The indexer's disk space, because if the disk is full, new events are not indexed and thus not accelerated.
Why it fails: If the disk were full, new events would not be indexed at all, affecting raw searches as well. The scenario specifies that only the accelerated data model is missing recent data, implying that raw events are present. Therefore, disk space is not the primary suspect.
C
The data model's acceleration schedule, to ensure it runs frequently enough to capture new data.
Why it fails: The acceleration schedule determines how often the summary is updated. If it runs infrequently, there might be a delay, but it would eventually catch up. The scenario describes a persistent lack of recent data, which points to a time range limit rather than a schedule issue.
D
The search head's clock synchronization, as time skew can cause recent events to be indexed with future timestamps.
Why it fails: While clock skew can cause indexing issues, it would affect all data, not just recent data in an accelerated data model. Moreover, the scenario states that older data is present, which suggests the acceleration is working for older periods. Clock skew is less likely than an acceleration time range misconfiguration.

Question 4hardmultiple choice

A Splunk administrator is troubleshooting why a data model acceleration summary for the 'Network_Traffic' data model is not returning results for a specific time range, even though the data model is accelerated and the search head is healthy. The administrator checks the acceleration settings and sees that the acceleration is enabled with a summary range of 7 days. The search is for the last 30 days. What is the most likely cause of the missing results?

Trap 1: The acceleration summary files are corrupted and need to be rebuilt.

Corrupted summary files would typically affect all searches using the accelerated data model, not just those beyond a certain time range. The administrator would see errors or inconsistent results across time ranges. The fact that results are missing only for data older than 7 days points to the summary range limit, not corruption.

Trap 2: The 'Network_Traffic' data model does not support acceleration for…

There are no licensing restrictions that limit data model acceleration to 7 days. The summary range is configurable and can be set to any value, subject to available storage and performance considerations. The limitation here is the configured summary range, not a license restriction.

Trap 3: The data model acceleration is not working because the search head…

If the search head were not connected to the indexers, the administrator would likely see broader failures, not just missing data for a specific time range. The scenario states the search head is healthy, so connectivity is not the issue. The problem is specifically about the time range exceeding the acceleration summary range.

A
The acceleration summary files are corrupted and need to be rebuilt.
Why it fails: Corrupted summary files would typically affect all searches using the accelerated data model, not just those beyond a certain time range. The administrator would see errors or inconsistent results across time ranges. The fact that results are missing only for data older than 7 days points to the summary range limit, not corruption.
B
The 'Network_Traffic' data model does not support acceleration for time ranges longer than 7 days due to licensing restrictions.
Why it fails: There are no licensing restrictions that limit data model acceleration to 7 days. The summary range is configurable and can be set to any value, subject to available storage and performance considerations. The limitation here is the configured summary range, not a license restriction.
C
The data model acceleration is not working because the search head is not connected to the indexers.
Why it fails: If the search head were not connected to the indexers, the administrator would likely see broader failures, not just missing data for a specific time range. The scenario states the search head is healthy, so connectivity is not the issue. The problem is specifically about the time range exceeding the acceleration summary range.
D
The acceleration summary only covers the last 7 days, so data older than that is not summarized and must be searched from raw data.
Data model acceleration creates summary files for a specified summary range. If the summary range is 7 days, only the last 7 days of data are accelerated. Searches that span beyond that range will not find accelerated data for the older period, and Splunk will fall back to raw data if the search mode allows, or return incomplete results if the search is restricted to accelerated data. This is the most likely cause.

Question 5hardmultiple choice

You are troubleshooting a dashboard that uses a base search and several post-process searches. The base search is `index=app | stats count by user, action`. One post-process search is `| search action=login | top user`. Users report that the top user panel sometimes shows incorrect counts compared to a direct search `index=app action=login | top user`. What is the most likely cause of the discrepancy?

Trap 1: The post-process search is missing a time range, so it uses a…

In Splunk, post-process searches inherit the time range from the base search. They do not have their own time range unless explicitly overridden. Therefore, a missing time range is not the cause. The discrepancy arises from the aggregation in the base search, not from time range differences.

Trap 2: The base search is not using `| fields` to limit the fields passed…

Not using `| fields` can affect performance and may cause post-process searches to have access to more fields than needed, but it does not cause incorrect counts. The base search already aggregates by user and action, so the post-process search only sees those fields. The discrepancy is not due to field filtering but rather to how the base search aggregates data.

Trap 3: The base search is not accelerated, so it returns incomplete…

Acceleration is a feature for data models and does not apply to ad-hoc base searches. A non-accelerated base search still returns complete results for the time range. The issue is not about acceleration but about the level of aggregation. The base search returns aggregated counts, which changes the semantics for post-process searches.

A
The post-process search is missing a time range, so it uses a different time range than the base search.
Why it fails: In Splunk, post-process searches inherit the time range from the base search. They do not have their own time range unless explicitly overridden. Therefore, a missing time range is not the cause. The discrepancy arises from the aggregation in the base search, not from time range differences.
B
The base search is not using `| fields` to limit the fields passed to post-process searches.
Why it fails: Not using `| fields` can affect performance and may cause post-process searches to have access to more fields than needed, but it does not cause incorrect counts. The base search already aggregates by user and action, so the post-process search only sees those fields. The discrepancy is not due to field filtering but rather to how the base search aggregates data.
C
The base search uses `stats count by user, action`, which aggregates events, so the post-process search counts aggregated rows rather than raw events.
The base search aggregates events into counts per user and action. When the post-process search runs `| search action=login | top user`, it operates on the aggregated rows, not raw events. The `top` command then counts the number of aggregated rows per user, which may not equal the sum of counts. For example, if a user has multiple actions, the counts are separate rows. This leads to incorrect top user counts compared to a direct search on raw events.
D
The base search is not accelerated, so it returns incomplete results.
Why it fails: Acceleration is a feature for data models and does not apply to ad-hoc base searches. A non-accelerated base search still returns complete results for the time range. The issue is not about acceleration but about the level of aggregation. The base search returns aggregated counts, which changes the semantics for post-process searches.

Question 6hardmulti select

A Splunk power user is troubleshooting a data model acceleration that has stopped generating new summary data. The user checks the Data Model Acceleration settings and the model is enabled and set to accelerate. Which two actions should the user take to resolve the issue? (Choose two.)

Trap 1: Restart the search head to clear any stuck acceleration jobs.

Restarting the search head is a heavy-handed action that may temporarily clear a stuck job but does not fix the underlying cause. It also disrupts other users and searches. Without checking logs and permissions first, the issue is likely to recur after restart.

Trap 2: Increase the Max Time setting to 1 year to force a full rebuild of…

Changing Max Time affects how far back the acceleration summarizes, not why it stopped. A full rebuild might occur, but if the underlying problem is permissions or disk space, it will fail again. This action does not diagnose the cause and can increase resource usage unnecessarily.

Trap 3: Delete the data model and recreate it from scratch to reset the…

Deleting and recreating the data model is a destructive step that loses custom configurations and does not address the root cause. The issue could be permissions, disk space, or scheduler conflicts. Recreating the model without diagnosing the cause will likely result in the same failure and unnecessary downtime.

A
Check the _internal index for messages from the DataModelAcceleration and search scheduler to identify errors or skipped runs.
The _internal index contains logs from the data model acceleration process, including errors, warnings, and scheduler messages. Searching _internal for DataModelAcceleration or the model name reveals why acceleration failed, such as missing permissions, full disk, or search head issues.
B
Restart the search head to clear any stuck acceleration jobs.
Why it fails: Restarting the search head is a heavy-handed action that may temporarily clear a stuck job but does not fix the underlying cause. It also disrupts other users and searches. Without checking logs and permissions first, the issue is likely to recur after restart.
C
Verify that the search head's role has the accelerate_datamodel capability and that the user has permission to manage acceleration.
Data model acceleration requires the accelerate_datamodel capability on the search head. If the role lacks this capability, the acceleration cannot run even if the model is enabled. Verifying role permissions ensures the scheduler can execute the acceleration searches and write summary files.
D
Increase the Max Time setting to 1 year to force a full rebuild of the summary.
Why it fails: Changing Max Time affects how far back the acceleration summarizes, not why it stopped. A full rebuild might occur, but if the underlying problem is permissions or disk space, it will fail again. This action does not diagnose the cause and can increase resource usage unnecessarily.
E
Delete the data model and recreate it from scratch to reset the acceleration state.
Why it fails: Deleting and recreating the data model is a destructive step that loses custom configurations and does not address the root cause. The issue could be permissions, disk space, or scheduler conflicts. Recreating the model without diagnosing the cause will likely result in the same failure and unnecessary downtime.

Question 7hardmulti select

A Splunk administrator is troubleshooting a data model acceleration issue. The administrator notices that the acceleration summary for the 'Network_Traffic' data model is not up to date, and searches using tstats return stale results. Which two actions should the administrator take to resolve this issue? (Choose two.)

Trap 1: Restart the Splunk indexer to force a full re-acceleration of all…

Restarting the indexer is a disruptive action and does not guarantee a full re-acceleration. Acceleration is managed by the search head and runs as scheduled searches. Restarting the indexer might interrupt searches but will not fix misconfigurations or errors. It is not a targeted solution for stale acceleration summaries and could cause additional issues.

Trap 2: Disable and re-enable acceleration for the data model to trigger a…

Disabling and re-enabling acceleration will indeed trigger a rebuild, but it is a heavy-handed approach that can take a long time and consume significant resources. It does not address the underlying cause, such as a misconfiguration or error. It is better to first check the configuration and logs to identify and fix the root cause, rather than forcing a rebuild blindly.

Trap 3: Check the size of the acceleration summary and increase the disk…

While disk space can affect acceleration if the summary index is full, it is less likely to be the primary cause of stale results. The summary index typically has a retention policy, and if it runs out of space, it may drop old data, but that would not cause the summary to be stale; it would cause missing data. Stale results usually indicate that acceleration is not running or is failing, not a disk space issue.

A
Restart the Splunk indexer to force a full re-acceleration of all data models.
Why it fails: Restarting the indexer is a disruptive action and does not guarantee a full re-acceleration. Acceleration is managed by the search head and runs as scheduled searches. Restarting the indexer might interrupt searches but will not fix misconfigurations or errors. It is not a targeted solution for stale acceleration summaries and could cause additional issues.
B
Verify that the data model is enabled for acceleration and that the acceleration schedule is properly configured.
If the data model is not enabled for acceleration or the schedule is misconfigured (e.g., set to run at infrequent intervals or disabled), the summary files will not be updated. Checking these settings is a fundamental troubleshooting step. Ensuring that acceleration is enabled and the schedule matches the desired freshness is essential for timely updates.
C
Disable and re-enable acceleration for the data model to trigger a rebuild.
Why it fails: Disabling and re-enabling acceleration will indeed trigger a rebuild, but it is a heavy-handed approach that can take a long time and consume significant resources. It does not address the underlying cause, such as a misconfiguration or error. It is better to first check the configuration and logs to identify and fix the root cause, rather than forcing a rebuild blindly.
D
Check the size of the acceleration summary and increase the disk space if necessary.
Why it fails: While disk space can affect acceleration if the summary index is full, it is less likely to be the primary cause of stale results. The summary index typically has a retention policy, and if it runs out of space, it may drop old data, but that would not cause the summary to be stale; it would cause missing data. Stale results usually indicate that acceleration is not running or is failing, not a disk space issue.
E
Review the acceleration logs for errors, such as search failures or permission issues.
Acceleration runs as a scheduled search, and if it fails due to errors (e.g., search timeouts, permission problems, or missing fields), the summary will not be updated. Checking the logs, such as the _internal index for messages related to the data model acceleration, can reveal the root cause. This is a critical step in diagnosing why acceleration is not completing successfully.

Question 8hardmultiple choice

A Splunk administrator is troubleshooting a calculated field that is not appearing in search results. The calculated field is defined in `props.conf` as `EVAL-total = price * quantity`. The administrator confirms that the `price` and `quantity` fields are present in the events and that the `props.conf` file is correctly placed in the app's `local` directory. However, when searching for `total`, no results are returned. Which of the following is the most likely cause?

Trap 1: The calculated field is not referenced in the search, so it is not…

Calculated fields are evaluated at search time only when they are referenced. If the administrator searches for `total`, that is a reference, so it should be evaluated. However, if the search does not explicitly reference `total` (e.g., just `index=main`), then it won't appear. The scenario says the administrator searches for `total`, so this is not the cause. The issue is that even when referenced, it's not appearing.

Trap 2: The calculated field is defined in the wrong app context; it must…

Calculated fields defined in an app's `local` directory are available to users of that app. If the search is performed within the same app, the field should be available. Placing it in the `system` app would make it global, but that is not required for it to work in the app. This is not the most likely cause unless the user is searching in a different app.

Trap 3: The `EVAL-` expression must be enclosed in double quotes to be…

In `props.conf`, `EVAL-` expressions are not enclosed in quotes unless the expression itself contains special characters that require quoting. For a simple arithmetic expression like `price * quantity`, quotes are not necessary. Adding quotes would actually become part of the expression and cause errors. So this is incorrect.

A
The calculated field is not referenced in the search, so it is not evaluated; the administrator must explicitly include it in the search terms.
Why it fails: Calculated fields are evaluated at search time only when they are referenced. If the administrator searches for `total`, that is a reference, so it should be evaluated. However, if the search does not explicitly reference `total` (e.g., just `index=main`), then it won't appear. The scenario says the administrator searches for `total`, so this is not the cause. The issue is that even when referenced, it's not appearing.
B
The `price` or `quantity` field is extracted as a string, causing the multiplication to fail or return null.
If `price` or `quantity` are extracted as strings, Splunk may not automatically convert them to numbers for arithmetic. The multiplication could result in null or an error. Splunk typically auto-converts numeric strings, but if the fields contain non-numeric characters (e.g., "$10"), the conversion fails. This is a common cause for calculated fields not producing expected results. The administrator should check field types or use functions like `tonumber()`.
C
The calculated field is defined in the wrong app context; it must be in the `system` app to be globally available.
Why it fails: Calculated fields defined in an app's `local` directory are available to users of that app. If the search is performed within the same app, the field should be available. Placing it in the `system` app would make it global, but that is not required for it to work in the app. This is not the most likely cause unless the user is searching in a different app.
D
The `EVAL-` expression must be enclosed in double quotes to be valid.
Why it fails: In `props.conf`, `EVAL-` expressions are not enclosed in quotes unless the expression itself contains special characters that require quoting. For a simple arithmetic expression like `price * quantity`, quotes are not necessary. Adding quotes would actually become part of the expression and cause errors. So this is incorrect.