SPLK-1001 Splunk Practice Test Questions and Exam Dumps
Question No 1:
When configuring an alert action in Splunk to execute a custom script (such as a Python or shell script), it is essential that Splunk is able to locate and run the specified script file. Splunk searches in specific directories for these custom scripts.
Which of the following directories is the correct default path where Splunk searches for custom alert action scripts?
A. $SPLUNK_HOME/bin/scripts
B. $SPLUNK_HOME/etc/scripts
C. $SPLUNK_HOME/bin/etc/scripts
D. $SPLUNK_HOME/etc/scripts/bin
Correct Answer: A. $SPLUNK_HOME/bin/scripts
Explanation:
In Splunk, alert actions are automated responses triggered by specific search results or conditions. One powerful feature of Splunk alerting is the ability to execute custom scripts. These scripts can perform additional processing, send notifications, or integrate with external systems.
When an alert is configured to run a script, Splunk must be able to locate and execute the script file reliably. To do this, Splunk searches for custom alert scripts in a specific default directory: $SPLUNK_HOME/bin/scripts. The $SPLUNK_HOME environment variable points to the root of your Splunk installation, and within this directory, the bin/scripts folder is the designated location for such scripts.
By placing your custom script (e.g., my_alert_script.py or send_notification.sh) inside the bin/scripts directory, you ensure that Splunk can locate and run it when the alert is triggered. You must also ensure the script has appropriate permissions (e.g., executable permission on Unix systems) and is written to handle the input Splunk provides when it calls the script, typically via standard input or environment variables.
The other options listed in the question are either non-existent or incorrect paths:
$SPLUNK_HOME/etc/scripts – Incorrect; this directory is not used for scripts.
$SPLUNK_HOME/bin/etc/scripts – Invalid path structure.
$SPLUNK_HOME/etc/scripts/bin – Also incorrect; not a valid default script location.
Correctly placing and configuring your script is vital to ensuring alerts function as expected and integrate smoothly with your operational workflows.
Question No 2:
When performing searches in most search engines or databases, if you enter two or more keywords without specifying a Boolean operator, the system assumes a default Boolean logic to connect the terms.
Which Boolean operator is automatically applied between the keywords unless a different operator is explicitly used?
A. OR
B. NOT
C. AND
D. XOR
Correct Answer: C. AND
Explanation:
In the context of information retrieval and search engine queries, Boolean operators are used to combine search terms to either broaden or narrow down the results. The most common Boolean operators are AND, OR, and NOT.
When no Boolean operator is explicitly used between two or more search terms, most modern search engines and databases default to using the "AND" operator. This means that the search engine will return results that include all the specified terms.
For example, if a user types:
nginx
climate change
The search engine interprets this as:
nginx
climate AND change
As a result, the search engine will retrieve documents or web pages that contain both "climate" and "change," not just one or the other.
This default behavior helps ensure that the search results are more relevant and focused. If the default were "OR," users might get a much broader and potentially irrelevant set of results, including documents that mention "climate" but not "change," or vice versa.
The "AND" operator refines the search by intersecting the sets of documents containing each term, thereby narrowing down the results. This is particularly useful in academic research, library databases, and professional search tools, where precision is important.
In contrast:
"OR" broadens the search.
"NOT" excludes certain terms.
"XOR" (exclusive OR) is rarely used in standard searches and is more common in computing or logic circuits.
Therefore, unless otherwise specified, "AND" is the Boolean operator that is implied between search terms.
Question No 3:
In the context of using the stats command in Splunk, what is the purpose of the values() function?
A. Displays all instances (including duplicates) of a specified field.
B. Displays only unique instances of a specified field.
C. Calculates the number of distinct values for a specified field.
D. Computes the total number of matching events from the search results.
Correct Answer: B. Displays only unique instances of a specified field.
Explanation:
In Splunk, the stats command is used to compute statistical summaries over search results. It allows users to apply a variety of functions—such as count(), sum(), avg(), and values()—to fields in the data. One particularly useful function is values(), which plays a critical role in data analysis.
The values() function is used to return unique values of a specified field from the events in your search results. This means that even if a value appears multiple times across different events, it will only be listed once in the output. The function helps in identifying the diversity or variety within a dataset without counting duplicates.
For example, suppose you have a field called status in your events that includes multiple entries like "200", "404", "500", and some of these appear repeatedly. When you use the command:
java
| stats values(status)
The output will be a list of the unique status codes (e.g., 200, 404, 500), regardless of how many times each appeared.
This is particularly useful for generating reports or dashboards where you only need to know what different values exist within a field, rather than how often they occur.
It’s important not to confuse values() with other similar functions like count() (which counts all events), or dc() (which stands for "distinct count" and returns the number of unique values). The values() function is about listing, not counting.
In summary, values() helps you quickly identify the range of values present in your data, offering a clear view of field variability.
Question No 4:
In Splunk, when analyzing data using the stats command, you may want to determine the number of distinct or unique values present for a specific field within the result set. This is useful for understanding data variability or tracking unique identifiers such as users, hosts, IP addresses, or error codes.
Which of the following functions used with the stats command correctly returns the count of unique values for a given field?
A. dc(field)
B. count(field)
C. count-by(field)
D. distinct-count(field)
Correct Answer: A. dc(field)
Explanation:
In Splunk, the stats command is widely used to perform aggregate functions on search results, allowing users to summarize and analyze large sets of data efficiently. Among the many functions it supports, understanding how to count unique values in a field is essential for various analysis tasks, such as determining the number of distinct users logging in or the number of different error codes encountered.
The correct function to achieve this is dc(field). Here, dc stands for distinct count. When used in the form stats dc(field), it calculates the number of unique (non-duplicate) values present in that specified field. For example:
spl
... | stats dc(user)
This would return the total number of unique users in the dataset.
Let’s briefly look at why the other options are incorrect:
B. count(field): This function is not valid syntax in Splunk. The correct usage is simply count, which returns the total number of events, not unique values.
C. count-by(field): This is not a valid function within stats. Instead, you can use stats count by field, which returns a count of events for each unique value, but it doesn’t return the count of how many unique values there are.
D. distinct-count(field): This might seem correct based on name alone, but it’s not a valid Splunk function. The actual function name is abbreviated to dc.
Understanding the correct use of dc(field) allows users to extract meaningful insights from large volumes of machine data, which is core to leveraging the power of Splunk for IT, security, and business analytics.
Question No 5:
In the context of digital solutions and platforms like Splunk or other enterprise-grade data management tools, there exists a container or structure that holds various components essential for building and delivering functionality. These components often include data inputs, user interface (UI) elements, and knowledge objects such as saved searches, reports, alerts, dashboards, and event types.
What is the term used to refer to this comprehensive collection of components that together enable functionality within a platform?
A. An app
B. JSON
C. A role
D. An enhanced solution
Correct Answer: A. An app
Explanation:
In platforms like Splunk, the term "app" refers to a modular and cohesive collection of various elements that work together to deliver a specific functionality or solution. These elements include:
Data inputs: Mechanisms to bring external data into the platform.
User interface (UI) elements: Custom dashboards, forms, and views for user interaction.
Knowledge objects: Saved searches, event types, macros, tags, alerts, and reports that help extract meaningful insights from raw data.
An app packages these elements into a unified, deployable unit that can be shared or installed across environments. For instance, a "Security Monitoring" app might include custom dashboards for visualizing threat data, alerts for suspicious activities, and data inputs configured to ingest firewall logs.
Other options provided in the question are not accurate in this context:
JSON (B) is a data format used to structure data, not a container of functionality.
A role (C) is related to user access control and permissions, not functional components.
An enhanced solution (D) is a vague term and not a standard or specific term used to describe this kind of collection.
Therefore, the most accurate and widely recognized term for a collection of components like data inputs, UI elements, and knowledge objects is “an app.”
Apps streamline the organization of platform functionality, making them crucial for scalability, reusability, and effective deployment in enterprise environments.
Question No 6:
Which of the following statements accurately describes how alerts function in Splunk?
A. Splunk alerts are generated from searches that can run either on a scheduled basis or in real-time, depending on user configuration.
B. Splunk alerts are triggered from searches, but can only send email notifications when conditions are met.
C. Splunk alerts require a cron job to schedule and execute searches that generate alerts.
D. Splunk alerts are exclusively triggered by real-time searches and cannot be scheduled.
Correct Answer: A. Splunk alerts are generated from searches that can run either on a scheduled basis or in real-time, depending on user configuration.
Explanation:
In Splunk, alerts are a powerful feature used to monitor data for specific conditions or patterns and automatically take action when those conditions are met. The core of any alert is a search query—this is what Splunk uses to analyze indexed data and determine whether an alert condition has been triggered.
Alerts in Splunk can be configured to run in two primary modes:
Scheduled Alerts: These run at specific intervals (e.g., every 5 minutes, hourly, daily) as defined by the user. You can define these intervals using a simple dropdown or even a cron expression for more complex scheduling. Scheduled alerts are useful for tracking periodic anomalies or events, such as logins outside business hours or sudden spikes in traffic.
Real-Time Alerts: These continuously monitor incoming data and trigger immediately when a condition is met. Real-time alerts are suitable for detecting critical or security-related events that require immediate attention, like system failures or unauthorized access attempts.
Once triggered, alerts can perform various actions—not just sending emails. Splunk allows alert actions such as triggering a webhook, running a script, creating a ticket, sending SNMP traps, or executing a custom workflow.
Therefore, Option A is the correct and most complete description. Options B, C, and D are incorrect or incomplete—B limits alerts to only email notifications, C incorrectly states that cron is required (it is only one scheduling option), and D falsely claims alerts only work in real-time.
Understanding how alerts work is essential for effective monitoring, automation, and incident response within Splunk.
Question No 7:
In the context of using the stats command within a data analysis or search processing language (such as SPL in Splunk),
What is the primary function of appending a by clause to the command?
A. To group the results by one or more specified fields.
B. To compute numerical statistics on each individual field.
C. To define how values in a multi-value field are separated.
D. To split the input data into multiple tables based on field values.
Correct Answer: A. To group the results by one or more specified fields.
Explanation:
The stats command is widely used in search processing languages like Splunk Processing Language (SPL) to calculate aggregate statistics (e.g., count, sum, avg, max, min) from data sets. When analyzing large volumes of log or event data, it’s often necessary not only to compute statistics but to segment or categorize those statistics based on certain field values—this is where the by clause becomes essential.
When you use a by clause with the stats command, you're instructing the system to group the results based on one or more fields. For each distinct combination of the field(s) specified, the stats command computes separate statistical results. Essentially, it performs a "group by" operation similar to SQL.
For example:
python-repl
... | stats count by status_code
This command counts the number of events for each unique status_code. Without the by clause, you would get a single overall count for all events, with no breakdown.
This grouping capability is particularly powerful when you want insights like:
Number of login attempts per user
Average response time per endpoint
Total bytes transferred per host
In contrast:
Option B describes what stats does in general but ignores the grouping aspect.
Option C refers to multi-value field behavior, which is unrelated.
Option D misinterprets the function—stats by doesn't partition the input, it groups output.
In summary, the by clause is crucial for breaking down aggregated statistics across different categories or dimensions in your data.
Question No 8:
In the context of refining and customizing search results in SPL (Search Processing Language),
which syntax is used to add or remove specific fields from the results that are returned by a search command?
A. Use field + to add and field - to remove
B. Use table + to add and table - to remove
C. Use fields + to add and fields - to remove
D. Use fields Plus to add and fields Minus to remove
Correct Answer: C. Use fields + to add and fields - to remove
Explanation:
In Splunk’s Search Processing Language (SPL), the fields command is essential for controlling the visibility of fields in your search results. It allows you to either include (add) or exclude (remove) specific fields from your result set, which helps streamline the output and improve readability and performance.
The syntax is:
To include specific fields:
spl
... | fields + field1 field2
This keeps only field1 and field2 in the results and removes all other fields.
To exclude specific fields:
spl
... | fields - field3 field4
This removes field3 and field4 from the results but keeps everything else.
The + and - symbols are not always necessary (you can just use fields field1 field2 to include), but explicitly using + or - can clarify intent, especially when building or troubleshooting complex queries.
Using fields can reduce the amount of data being processed and transferred, especially in large datasets. This optimization is important in dashboards, reports, or alerts where performance and clarity matter. Unlike the table command, which also selects fields but changes the display format to a table, fields simply filters the fields in the dataset while preserving the raw event data structure.
Incorrect choices:
field is not a valid command (Option A).
table changes output format, not fields alone (Option B).
fields Plus and fields Minus are not valid SPL syntax (Option D).
Hence, Option C is the correct and syntactically valid choice.
Question No 9:
You have run a search in Splunk, and you notice that a particular field appears in the search results (i.e., the events), but it is not visible in the "Fields" sidebar under either "Interesting Fields" or "Selected Fields." You want to make this field more accessible for further analysis by having it appear in the sidebar.
What action should you take to add this field to the Fields sidebar so that it becomes easier to work with?
A. Click All Fields, find the desired field, and manually add it to Selected Fields.
B. Click Interesting Fields, then select the desired field to move it to Selected Fields.
C. Click Selected Fields, then choose the field to move it to Interesting Fields.
D. This scenario is not possible because all fields returned by a search are automatically shown in the Fields sidebar.
Correct Answer: A. Click All Fields and select the field to add it to Selected Fields.
Explanation:
In Splunk, when you perform a search, the search results may include a wide variety of fields—some that are extracted by default and others that are derived or indexed. However, not all these fields are immediately visible in the Fields sidebar on the left panel of the Search interface.
The Fields sidebar is divided into sections such as Selected Fields, Interesting Fields, and the full list accessible via All Fields. Selected Fields typically contain default fields like host, source, and sourcetype, as well as any fields you've manually pinned. Interesting Fields are fields that Splunk identifies as potentially useful based on event frequency.
Sometimes, a field may be present in the event data (as verified in the raw search results) but not automatically displayed in either section. This does not mean the field doesn't exist—it just hasn't been surfaced in the UI.
To add such a field to the sidebar:
Click on the All Fields link below the field list.
A dialog box will open, showing all the available fields extracted in your search.
Locate the desired field and click the checkbox next to it.
This action adds the field to Selected Fields, making it easily accessible in your sidebar.
Therefore, Option A is the correct answer. This process improves workflow efficiency by allowing faster filtering, sorting, and analysis of that field without having to manually look it up every time.
