Cisco 300-620 Exam Dumps & Practice Test Questions
Question 1:
When setting up an alert action in Splunk to trigger a custom script (like a Python or shell script), it is important that Splunk can locate and execute the specified script. In which directory does Splunk by default look for custom alert action scripts?
A. $SPLUNK_HOME/bin/custom-scripts
B. $SPLUNK_HOME/etc/alert-scripts
C. $SPLUNK_HOME/bin/etc/scripts
D. $SPLUNK_HOME/etc/scripts/alert
Answer: B
Explanation:
When you configure an alert action in Splunk to execute a custom script (for example, a Python script or a shell script), the platform needs to know where to find that script in order to run it. By default, Splunk looks for custom alert action scripts in the $SPLUNK_HOME/etc/alert-scripts directory.
This default path is where Splunk expects to locate scripts that are executed as part of a triggered alert. When you define a scripted alert action, Splunk doesn't just run any arbitrary script from any path—it uses this predesignated folder to ensure controlled, secure, and organized execution of these custom scripts.
For example, if you're setting up an alert and you configure it to run alert_handler.py, you need to make sure this script is saved in the directory:
$SPLUNK_HOME/etc/alert-scripts/
When the alert is triggered, Splunk checks this folder and executes the script if it exists and is executable.
Let’s review why the other options are incorrect:
A. $SPLUNK_HOME/bin/custom-scripts: This path does not exist by default in a standard Splunk installation. While the bin/ directory does contain some Splunk executable files, it is not where custom alert scripts should be placed.
C. $SPLUNK_HOME/bin/etc/scripts: This is not a valid directory structure in Splunk. The correct folder is within the etc/ directory, but not under bin/etc.
D. $SPLUNK_HOME/etc/scripts/alert: This directory also does not exist by default. There may be app-specific script folders, but this is not the default location for alert scripts that are triggered by custom alert actions.
In summary, if you’re developing or configuring a custom script alert action in Splunk, you must place the script in the $SPLUNK_HOME/etc/alert-scripts directory so that Splunk can properly locate and execute it. This makes B the correct answer.
Question 2:
In most search engines or databases, if you enter multiple keywords without specifying a Boolean operator, the system applies a default operator to combine them. Which Boolean operator is used by default to connect the keywords unless otherwise specified?
A. OR
B. NOT
C. AND
D. XOR
Answer: C
Explanation:
When you enter multiple keywords into most search engines or databases without explicitly including a Boolean operator (like AND, OR, or NOT), the system typically uses AND as the default operator. This means it returns only those results that include all of the specified keywords.
For example, if you type server failure into a search engine or database query field, it is usually interpreted as:
server AND failure
This implies that the search results must contain both the word “server” and the word “failure” in order to be included.
This default behavior makes sense for many use cases, especially when precision is desired. By returning only the results that include all search terms, the query ensures a more targeted result set, which is often what users expect when entering multiple terms.
Let’s break down why the other options are incorrect:
A. OR: If the system used OR as the default, then a search for server failure would return results that contain either “server” or “failure”, which would produce a much broader and potentially less relevant set of results. This is not the standard default behavior in most systems.
B. NOT: The NOT operator is used to exclude certain terms from search results. For example, server NOT failure would exclude any result containing the word “failure.” Using NOT as a default operator would drastically reduce the result set and is generally not practical or expected as a default behavior.
D. XOR: The XOR (exclusive OR) operator would return results that contain either one term or the other, but not both. This is a specialized operator and is rarely used, let alone as a default. It is also counterintuitive in most search scenarios.
Therefore, the default behavior in most search engines and structured query environments is to use the AND operator when multiple terms are entered without an explicit Boolean operator. This makes C the correct answer.
Question 3:
When using the stats command in Splunk, what is the function of the values() operator?
A. It lists every occurrence of a given field, including duplicates.
B. It only lists distinct occurrences of a given field.
C. It counts the unique values of a specified field.
D. It totals the number of events that match the search query.
Answer: B
Explanation:
The values() function in Splunk’s stats command is specifically designed to return a list of distinct (unique) values for a specified field across the matching events. When this function is applied, it aggregates data by removing duplicates and showing only the unique entries from the selected field.
For example, consider the search:
| stats values(user)
This would return a list of all unique usernames found in the user field from the events returned by the search. If the user field had 10 entries and 3 of them were duplicates (e.g., user1 appearing three times), the output of values(user) would only include user1 once, along with other distinct users.
This function is very useful in cases where you need to identify unique entries, such as:
A list of distinct IP addresses accessing a system
Unique URLs accessed within a time frame
Different status codes returned by a web server
Here’s why the other options are incorrect:
A. It lists every occurrence of a given field, including duplicates.
This describes the behavior of the list() function, not values(). The list() function collects all values, including duplicates, and does not enforce uniqueness.C. It counts the unique values of a specified field.
This is the role of the dc() function in Splunk. dc(field) stands for distinct count and returns the number of unique values in the specified field, not the list of those values.D. It totals the number of events that match the search query.
This is done using the count() function, which simply returns the number of matching events.
In conclusion, if you want to extract all the unique values of a field from your Splunk data, values() is the correct function to use. It filters out duplicates and provides a clean set of entries that appear only once in the output. Therefore, the correct answer is B.
Question 4:
In Splunk, if you're using the stats command and want to find out how many unique values there are for a particular field, which function would you use?
A. dc(field)
B. count(field)
C. count-by(field)
D. distinct-count(field)
Answer: A
Explanation:
In Splunk, when using the stats command and you want to determine how many unique values exist for a given field, you use the dc() function. The abbreviation dc stands for distinct count. This function counts the number of unique values present in a particular field across the set of matching events.
For instance, consider this SPL command:
| stats dc(user)
This would return the number of unique usernames found in the user field across all events that match the search criteria. If 100 events are returned and the user field contains 10 unique usernames, dc(user) will return 10.
Here’s a breakdown of what the other options do and why they are incorrect:
B. count(field):
The count() function in Splunk counts the number of events or values. However, it does not count only the unique values unless combined with further logic. It simply returns the total number of occurrences, not a distinct count.
Example:
| stats count(user)
This will give the total number of events where the user field appears, including duplicates.
C. count-by(field):
This is not a valid Splunk function. Splunk’s stats command does not support a count-by() function. To group and count by a field, the correct syntax would be:
| stats count by field
This syntax groups the data by the field and counts the number of events in each group, but there’s no function named count-by().
D. distinct-count(field):
While this option seems descriptive and might look correct at first glance, it is not a valid Splunk function. The correct built-in function is dc(). Splunk uses shortened function names for performance and standardization, so distinct-count() is not recognized by the stats processor.
To summarize:
If you want the number of unique entries in a field, use dc(field).
count() gives you the total number of occurrences.
list() or values() can give you lists of values, but not counts.
Invalid or non-existent functions like distinct-count() or count-by() will result in errors.
Therefore, to get the number of unique values for a particular field in Splunk, you should use dc(field), making A the correct answer.
Question 5:
In platforms like Splunk, a comprehensive collection of components such as data inputs, user interface elements, and knowledge objects (e.g., saved searches, reports, and dashboards) is often used to create functionality within the platform. What term describes this collection of components?
A. A module
B. A package
C. An app
D. A feature set
Answer: C
Explanation:
In Splunk, the term app refers to a self-contained bundle of components that together deliver specific functionality. This includes data inputs, user interface elements, saved searches, reports, dashboards, event types, field extractions, and other knowledge objects. An app in Splunk acts like a mini-environment or workspace tailored to a particular purpose—such as monitoring web traffic, analyzing system logs, or managing security events.
Apps serve multiple purposes in Splunk, including:
Modularizing functionality: Apps can encapsulate a set of configurations, allowing different teams or purposes to be served independently.
Custom interfaces: They may come with custom views, forms, and dashboards designed to meet a specific use case.
Reusability and sharing: Many apps are published on Splunkbase, the community and vendor portal for finding and distributing Splunk apps. Users can download, install, and configure apps to extend their Splunk environment.
A good example of an app is Splunk App for Windows Infrastructure, which is designed to help monitor Windows environments. Another example is Splunk Enterprise Security, a premium app providing security-focused dashboards and workflows.
Here’s why the other options are incorrect:
A. A module:
The term “module” is not used in Splunk in this context. While modules may exist in the context of scripting or plugin development in other platforms, Splunk does not define this collection as a “module.”B. A package:
“Package” might sound similar, but in Splunk terminology, it’s not the official term for what is essentially a deployable and functional set of configurations and knowledge objects. A package might refer more generically to bundled files, but in Splunk, the correct term is “app.”D. A feature set:
“Feature set” is a vague term and usually refers to a group of features or capabilities offered by a product. It does not refer to a deployable unit that contains saved searches, dashboards, and configurations in the Splunk environment.
In conclusion, the appropriate term used in Splunk for a collection of data inputs, knowledge objects, and user interface elements bundled together to provide specific functionality is an app. Therefore, the correct answer is C.
Question 6:
Which of the following best describes the behavior of alerts in Splunk?
A. Alerts in Splunk can be triggered by searches that run on a schedule or in real-time, depending on how they are configured.
B. Splunk alerts only send email notifications when specific conditions are met.
C. Alerts in Splunk require cron jobs for scheduling and execution.
D. Alerts in Splunk can only be triggered by real-time searches and are not schedulable.
Answer: A
Explanation:
In Splunk, alerts are a powerful and flexible feature designed to monitor data and trigger actions when specific conditions are met. The correct and complete description of their behavior is captured in option A, which states that alerts can be triggered by scheduled or real-time searches, depending on how they are configured.
There are two main types of alerts in Splunk:
Scheduled Alerts: These run periodically based on a defined interval or a cron schedule. They are used when it’s sufficient to check conditions at regular intervals (e.g., every 5 minutes, hourly, daily). A common example is checking for unusual login patterns every hour.
Real-Time Alerts: These are continuously running searches that check for specific conditions as new data arrives. They are useful when immediate action is required—such as detecting brute-force attacks or unauthorized access attempts as they occur.
Splunk also allows configuring alert actions, such as:
Sending an email notification
Triggering a script
Creating a ticket in an incident management system
Sending a webhook
Executing custom alert actions defined within Splunk apps
Now let’s review why the other options are incorrect:
B. Splunk alerts only send email notifications when specific conditions are met.
This is partially true, but misleading. While email is one common alert action, Splunk alerts are not limited to sending emails. They can perform various other actions, including triggering scripts or sending data to external systems.C. Alerts in Splunk require cron jobs for scheduling and execution.
This is incorrect. While cron expressions can be used to define custom schedules, they are not required. Splunk provides built-in scheduling options (like “every 5 minutes” or “hourly”) and handles the scheduling internally, without relying on external cron jobs.D. Alerts in Splunk can only be triggered by real-time searches and are not schedulable.
This is completely incorrect. Scheduled alerts are one of the most commonly used types in Splunk. In many environments, real-time alerts are used sparingly due to performance considerations, whereas scheduled alerts are used widely for routine monitoring.
In summary, alerts in Splunk offer both real-time and scheduled execution options, making them versatile tools for automating responses to patterns or anomalies in data. Therefore, the most accurate and complete description of their behavior is found in A.
Question 7:
In the context of using the stats command in Splunk, what does the by clause do when added to the command?
A. It groups the search results by one or more specified fields.
B. It calculates statistics for each individual field separately.
C. It defines the separator for values within a multi-value field.
D. It separates the input data into multiple result tables based on field values.
Answer: A
Explanation:
In Splunk, the stats command is one of the most commonly used commands for performing aggregations, such as counting events, finding averages, summing values, and more. The by clause plays a critical role in grouping these results by one or more fields so that you can calculate statistics per group rather than across the entire dataset.
For example:
| stats count by status
This search will return the count of events grouped by each unique value in the status field. So, if your events have status values like 200, 404, and 500, you'll see a table with each of those values and the corresponding event count for each one.
The syntax works as follows:
| stats <aggregator>(<field>) by <grouping_field>
So, when you use:
| stats avg(response_time) by host
You're asking Splunk to return the average response time per host.
Let’s now consider why the other options are incorrect:
B. It calculates statistics for each individual field separately.
This is misleading. While the stats command can compute separate statistics for different fields, this is not what the by clause does. The by clause groups the events first, and then the statistics are calculated per group, not per field.C. It defines the separator for values within a multi-value field.
This is incorrect. The by clause has nothing to do with formatting output or defining separators. Formatting or manipulating multi-value fields would typically be handled by other commands like mvjoin or eval.D. It separates the input data into multiple result tables based on field values.
This is a misinterpretation. Splunk does not create multiple result tables. Instead, the by clause results in a single table where each row represents a unique value or combination of values from the specified by field(s), along with the computed statistics for those groups.
To illustrate further, if you have:
| stats sum(bytes) by source, sourcetype
Splunk will group events based on each unique combination of source and sourcetype, and then return the sum of bytes for each group.
In summary, the by clause in the stats command is used to group data, so that statistical operations can be performed within each group separately. It does not affect the formatting or number of tables but rather structures the data by field values. Hence, the correct answer is A.
Question 8:
When refining your search results in Splunk's Search Processing Language (SPL), which syntax allows you to add or remove specific fields from your search output?
A. Use field + to include and field - to exclude
B. Use table + to add and table - to remove
C. Use fields + to include and fields - to exclude
D. Use fields Plus to add and fields Minus to remove
Answer: C
Explanation:
In Splunk's Search Processing Language (SPL), the fields command is used to control which fields are included or excluded from your search results. This is an important part of refining searches to show only relevant data, improve readability, and reduce the amount of information displayed.
The correct syntax uses the fields command followed by either:
A positive list of fields to include, or
A negative list of fields to exclude, using a minus sign (-) before the field name.
Including fields:
To include specific fields in the search result, you simply list them:
| fields host, source, sourcetype
This command will retain only the fields host, source, and sourcetype, and exclude all others from the results.
Excluding fields:
To exclude specific fields from the search results, use the - operator:
| fields - _raw
This command removes the _raw field, which normally contains the unparsed log event, from the output.
You can also combine multiple exclusions:
| fields - _raw, index, splunk_server
This selective field filtering improves the efficiency of your results display, especially when working with large datasets or creating dashboards that should only show relevant fields.
Now, let’s examine why the other options are incorrect:
A. Use field + to include and field - to exclude
Incorrect. The command should be fields, not field, and there is no use of + for inclusion. Inclusion is done by listing the fields without any prefix. Only the - sign is used for exclusion.B. Use table + to add and table - to remove
Incorrect. The table command is used to format results into a table view with only the specified fields. It does not support + or - operators, nor does it offer removal functionality.D. Use fields Plus to add and fields Minus to remove
Incorrect. These are not valid SPL syntax. SPL commands and options do not use keywords like "Plus" or "Minus"—this would result in a syntax error.
In conclusion, the fields command in Splunk SPL is the correct way to add or remove fields from your search output. Inclusion is achieved by listing field names, and exclusion is done by prefixing fields with -. Thus, the correct answer is C.
Question 9:
After running a search in Splunk, a particular field appears in the results but isn't visible in the "Fields" sidebar under "Interesting Fields" or "Selected Fields." To make this field more easily accessible, what should you do?
A. Click on All Fields, find the field, and manually add it to the Selected Fields list.
B. Click on Interesting Fields and move the desired field to the Selected Fields list.
C. Move the field from Selected Fields to Interesting Fields.
D. Fields returned by a search are always shown in the Fields sidebar, so this action isn't necessary.
Answer: A
Explanation:
In Splunk, when you execute a search, the user interface provides a Fields sidebar that helps you quickly navigate and interact with fields present in your search results. These fields are categorized into Selected Fields, Interesting Fields, and can also be browsed through All Fields.
If a field appears in your search results table (i.e., it's included in events or returned by commands like stats, eval, or table) but is not visible in the Fields sidebar, you can manually add it to the sidebar using the All Fields menu. This makes the field easier to find and reuse in your search workflow.
Here’s how the process works:
In the search results screen, click on the “All Fields” option usually found below the list of Interesting Fields.
This opens a dialog showing all available fields, even those that haven't been automatically categorized as "Interesting."
Locate the field you're looking for.
Use the checkbox next to that field to add it to the Selected Fields list.
Once selected, the field becomes visible in the sidebar under Selected Fields, where it will remain for your session or until removed.
Now, let's evaluate the incorrect options:
B. Click on Interesting Fields and move the desired field to the Selected Fields list.
Incorrect. There is no built-in mechanism to “move” a field from Interesting to Selected. Fields are automatically placed in the Interesting category based on frequency, but manual control over this is only available via the All Fields interface.C. Move the field from Selected Fields to Interesting Fields.
Incorrect. You cannot manually move fields to the Interesting category. That classification is automatically determined by Splunk based on statistical relevance (e.g., how often the field appears in events).D. Fields returned by a search are always shown in the Fields sidebar, so this action isn't necessary.
Incorrect. Just because a field is returned in search results does not mean it will appear in the Fields sidebar. Many fields—especially those that are calculated or appear sporadically—must be manually added via All Fields to be visible and easily accessible.
In summary, if you want to promote a field to the sidebar for easier access, the proper and most effective method is to navigate to All Fields, locate the field, and manually check it to include it under Selected Fields. This process enhances your visibility into specific fields without relying solely on Splunk's automated field categorization. Therefore, the correct answer is A.
Question 10:
Which Splunk feature allows you to automate the process of running searches and sending notifications based on specific conditions?
A. Scheduled Search
B. Data Model
C. Indexing Engine
D. Alert Actions
Answer: D
Explanation:
In Splunk, the feature that specifically allows users to automate search execution and trigger notifications or actions based on search results is known as Alert Actions.
Alert Actions are configured in the context of alerts, which are automated searches that run either on a schedule or in real-time, and evaluate whether specific conditions are met (e.g., a spike in failed login attempts, a drop in web traffic, or a specific log entry appearing). If the condition is satisfied, an alert triggers one or more actions, which may include:
Sending an email notification
Running a script (e.g., a remediation script or external system trigger)
Webhook calls (to integrate with tools like Slack, ServiceNow, or PagerDuty)
Logging events to another index
Creating a notable event (in environments using Enterprise Security)
Webhook or integration with REST APIs
Here’s a brief overview of why D. Alert Actions is the correct answer and the others are not:
A. Scheduled Search
Scheduled searches are part of how an alert operates (alerts are built on scheduled searches or real-time searches), but they alone do not trigger actions. A scheduled search just runs regularly—it doesn’t define what happens after the search returns results. The automation and notification come from the alert action, not the schedule.B. Data Model
Data models are used primarily for accelerating and organizing data for Pivot and knowledge object creation. They help build structured, hierarchical datasets that improve the performance of reports and dashboards. Data models are not used for triggering notifications or automating responses.C. Indexing Engine
The indexing engine is a core Splunk component responsible for storing and retrieving data efficiently. While it's critical to how Splunk operates under the hood, it has no role in triggering alerts or sending notifications. It processes incoming data and supports fast search performance but is not related to automation workflows.D. Alert Actions
This is the only option that directly refers to the mechanism that executes actions (like sending notifications) when conditions in a search are met. Alert actions are what give alerts their power, transforming them from passive search results into automated responses to significant events in the data.
To set up an alert in Splunk:
You write a search query.
Set conditions under which the alert should trigger (e.g., if count > 100).
Define the alert schedule (real-time or scheduled).
Configure the alert action(s) — such as email, script execution, or webhook.
In conclusion, Alert Actions are essential to implementing real-time operational intelligence with Splunk, as they automate responses based on incoming or existing data patterns. Thus, the correct answer is D.
