Splunk Core Certified Consultant Interview Questions

Well, interview preparation for any exam is an essential step towards achieving the desired goal. Preparing for an interview like that of Splunk Core Certified Consultant makes it necessary to not only have expertise in the technical field but also the capability to demonstrate the answers well. In order to boost your preparation for the interview, our experts have compiled the set of very frequently asked and the best possible Splunk Core Certified Consultant interview questions. These will help you gain a fair idea about how the interview actually goes but before starting with the questions let’s see the domains that are tested for this profile.

• Splunk Validated Architectures
• Authentication Protocols
• Monitoring Console configuration
• Splunk to Splunk Communication
• Search Head Clustering
• Data Input and more.

Now, let’s move towards the Splunk Core Certified Consultant interview questions.

Advanced Interview Questions

What is the difference between the Free, Light and Enterprise versions of Splunk?

Splunk is available in three editions: Free, Light and Enterprise.

1. Free edition: This is a limited version of Splunk that can be used for non-commercial purposes. It has a maximum daily indexing volume of 500 MB and does not include many of the advanced features of the paid editions.
2. Light edition: This is a paid version of Splunk designed for small to medium-sized organizations. It has a larger indexing volume compared to the Free edition, and includes additional features such as real-time search and the ability to scale to multiple users.
3. Enterprise edition: This is the full-featured version of Splunk, with unlimited indexing volume and a wide range of advanced features, such as distributed search, role-based access control, and high availability. It is designed for large enterprises and service providers.

The key difference between the three editions is the indexing volume and the availability of advanced features. The Free edition is limited and is suitable for small-scale use cases, while the Light and Enterprise editions offer more robust capabilities and are suitable for larger organizations.

How does Splunk collect, index and process data?

Splunk is a software platform that allows users to collect, index, and analyze large sets of machine-generated data, such as log files and network data.

1. Data Collection: Splunk can collect data from a variety of sources, including log files, network data, and APIs. Users can configure Splunk to automatically pull in data from these sources, or they can manually upload data.
2. Data Indexing: Once the data is collected, it is indexed so that it can be searched and analyzed. Splunk uses a proprietary indexing engine that allows for fast and efficient searching of the data.
3. Data Processing: After the data is indexed, it can be analyzed and processed using a variety of tools and techniques. Splunk provides a query language called SPL (Search Processing Language) which allows users to search and analyze their data. Users can also create visualizations and reports using Splunk’s built-in analytics and reporting tools.
4. Data Stream Processing: Splunk also provides real-time data processing capabilities using the Streaming Processing Language (SPL). This allows the user to process and analyze data as it is being collected in real-time, rather than waiting for the data to be indexed before performing analysis.
5. Data Machine Learning: Splunk also provides machine learning capabilities using the Machine Learning Toolkit (MLTK), which allows users to perform advanced analytics and predictive modeling on their data.

Overall, Splunk is a powerful platform for collecting, indexing, and analyzing large sets of machine-generated data, and it provides a wide range of tools and features to help users make sense of their data and gain insights from it.

What are the various ways to input data into Splunk?

Splunk supports several methods for inputting data into the platform, including:

1. File and Directory Monitoring: Files and directories can be monitored in real-time, and their contents can be automatically indexed into Splunk. This method supports a wide range of file types and formats, including log files, CSV, XML, and JSON.
2. Network Inputs: Data can be received over the network using protocols such as TCP, UDP, or HTTP. This method is useful for collecting data from remote sources and is often used for log collection from remote servers or devices.
3. Scripted Inputs: Custom scripts can be written to collect data from various sources and feed it into Splunk. This method can be used to extract data from APIs, databases, and other sources.
4. Third-Party Integrations: Splunk integrates with many third-party tools, such as infrastructure monitoring tools, security information and event management (SIEM) systems, and cloud services, to collect and index data.
5. Universal Forwarder: The Splunk Universal Forwarder is a lightweight version of the Splunk software that can be installed on remote systems to collect and forward data to a central Splunk instance.
6. REST API: The Splunk REST API can be used to programmatically index data into the platform. This method is useful for integrating with other systems and automating data ingestion.

These are some of the main methods for inputting data into Splunk, and the appropriate method depends on the specific requirements of the use case.

Can you explain Splunk search processing language (SPL)?

Splunk Search Processing Language (SPL) is a query language that allows users to search, analyze, and visualize data in Splunk. It is used to extract meaningful insights from large sets of data and is designed to be easy to use, yet powerful enough to handle complex data analysis tasks.

SPL is based on a simple command-line interface, and it is used to perform a wide range of search and data manipulation operations. Some of the most commonly used SPL commands include:

• Search: This command is used to retrieve data from the index and is the foundation of all other SPL commands. It allows users to specify the data they want to retrieve and the conditions under which it should be retrieved.
• Filter: This command is used to narrow down the data retrieved by the search command. It allows users to specify conditions that must be met for a record to be included in the results.
• Aggregate: This command is used to group and summarize data based on specific criteria. It allows users to calculate statistics such as count, sum, and average for different groups of data.
• Sort: This command is used to order the data retrieved by the search command. It allows users to specify the order in which the data should be displayed based on specific fields.
• Translate: This command is used to replace the values of specific fields with new values. It allows users to change the format of data or to map it to different values.
• Eval: This command is used to create new fields based on calculations or expressions. It allows users to manipulate data in various ways, such as concatenating fields or calculating running totals.
• Output: This command is used to format the data retrieved by the search command. It allows users to specify the fields that should be included in the results and how they should be displayed.

Overall, SPL is a powerful tool that allows users to extract insights from their data in a flexible and efficient way. It is widely used in industries such as IT, security, and finance, to make data-driven decisions and identify patterns and trends that would be difficult to detect otherwise.

How do you use fields in Splunk searches?

Fields in Splunk are a way to organize and extract specific information from your data. They allow you to search for specific pieces of information within your data, such as a specific IP address or user.

To use fields in a Splunk search, you first need to define the fields in your data. This can be done by using the “fields” command in your search. For example, if you wanted to extract the IP address and user from your data, you would use the following search:

| fields IP, user

This search will extract the IP and user fields from your data and display them in separate columns.

Once the fields have been defined, you can use them in your search to filter or aggregate the data. For example, if you wanted to find all events with a specific IP address, you could use the following search:

| search IP=192.168.1.1

This search will return all events in your data that have the IP address of 192.168.1.1.

You can also use fields in combination with other commands to aggregate or analyze the data. For example, if you wanted to find the number of events for each user, you could use the following search:

| stats count by user

This search will return the number of events for each user in your data.

In summary, fields in Splunk are a powerful tool for extracting and analyzing specific information from your data. They allow you to filter, aggregate, and analyze your data in a variety of ways, making it easier to find the information you need.

What are some common use cases for Splunk in an organization?

Some common use cases for Splunk in an organization are:

1. Log Management: Collecting, analyzing and storing log data from various sources such as servers, network devices, applications, and more.
2. Security Information and Event Management (SIEM): Detecting and responding to security threats by analyzing log data and providing alerts.
3. IT Operations: Monitoring and troubleshooting IT infrastructure and application performance.
4. Compliance: Archiving and searching logs to meet regulatory requirements and auditing.
5. Business Intelligence: Collecting and analyzing business data to make informed decisions.
6. Website Analytics: Analyzing website traffic and user behavior to optimize website performance and user experience.
7. Application Performance Management (APM): Monitoring and analyzing the performance of applications to improve efficiency and resolve issues.
8. Fraud Detection: Monitoring transactions for suspicious activity and detecting fraud.
9. Network Analytics: Analyzing network traffic to optimize network performance and troubleshoot issues.
10. Big Data Analytics: Processing and analyzing large amounts of data to uncover trends and insights.

These are some of the common use cases for Splunk in an organization, but the tool can also be used for many other purposes based on the specific needs of the organization.

Can you describe how to create alerts and reports in Splunk?

Creating alerts and reports in Splunk involves several steps, including setting up the data inputs, creating the search query, and configuring the alert or report settings.

1. Set up data inputs: Before creating alerts or reports, you need to ensure that the data you want to analyze is being ingested into Splunk. This can be done by adding data inputs, such as log files, network traffic, or API calls.
2. Create the search query: Once the data is being ingested, you can create a search query to extract the relevant information. This can be done using the Splunk search language, which allows you to filter, aggregate, and analyze the data.
3. Configure alert settings: To create an alert, you need to go to the “Alerts” menu and select “Create Alert”. Here, you can specify the search query, the conditions that trigger the alert, and the actions to take when the alert is triggered.
4. Create a report: To create a report, you need to go to the “Reports” menu and select “Create Report”. Here, you can specify the search query, the visualization type, and any other report settings.
5. Schedule alerts and reports: You can schedule alerts and reports to run at specific intervals or on a specific schedule. This can be done by going to the “Alerts” or “Reports” menu, selecting the alert or report, and then selecting “Schedule”.
6. View and manage alerts and reports: Once the alerts and reports are created, you can view and manage them by going to the “Alerts” or “Reports” menu. You can view the status, the results, and make any necessary adjustments.

In summary, creating alerts and reports in Splunk involves setting up data inputs, creating search queries, configuring alert and report settings, scheduling them, and managing them. With these steps, you can easily monitor your data and get insights that are relevant to your business.

Can you explain the role of forwarders in Splunk deployment?

A forwarder is a key component in a Splunk deployment. It is responsible for forwarding data from different sources to the indexer(s) for indexing and storage. Forwarders play a crucial role in making sure that data is collected, transported and stored securely.

The role of forwarders in Splunk deployment is:

1. Data Collection: Forwarders collect data from various sources such as log files, network devices, databases, etc. and send them to the indexer(s) for indexing.
2. Load Balancing: Forwarders distribute the incoming data to multiple indexers for indexing, which helps in load balancing and improving the overall performance of the deployment.
3. Data Filtering: Forwarders have the ability to filter data before forwarding it to the indexer. This helps in reducing the amount of data that needs to be indexed, which in turn reduces the load on the indexer and increases its efficiency.
4. Data Compression: Forwarders can compress data before forwarding it to the indexer, which helps in reducing the bandwidth required for data transfer and improving the overall performance of the deployment.
5. Data Security: Forwarders can encrypt data before forwarding it to the indexer, which helps in ensuring the confidentiality and security of sensitive data.
6. Reliability: Forwarders can store data locally before forwarding it to the indexer, which helps in ensuring data reliability in case of network connectivity issues.

In summary, forwarders play a crucial role in Splunk deployment by collecting, transporting, and processing data from different sources to the indexer. They help in improving the performance, reliability, security, and efficiency of the deployment.

How do you manage and maintain a Splunk cluster?

Managing and maintaining a Splunk cluster involves several key steps, including monitoring and troubleshooting, managing indexing and search performance, and ensuring data security and compliance.

Monitoring and Troubleshooting:

• Regularly monitoring the performance and health of the cluster is crucial to ensure that it is functioning properly and to quickly identify and resolve any issues that may arise.
• To monitor the cluster, it is essential to set up alerts and dashboards that provide real-time visibility into the cluster’s performance and resource utilization.
• Additionally, it is important to regularly review log files and system metrics to identify potential issues and to troubleshoot any problems that may arise.

Managing Indexing and Search Performance:

• To ensure that the cluster is performing at optimal levels, it is essential to manage indexing and search performance.
• This includes monitoring and tuning the indexing pipeline, optimizing the search performance, and managing the retention of data.
• It is also important to regularly review and adjust the cluster’s configuration settings to ensure that they are optimized for the specific data and use case.

Ensuring Data Security and Compliance:

• Ensuring that the data stored in the cluster is secure and compliant with relevant regulations is crucial to maintaining the integrity of the cluster.
• This includes implementing security measures such as encryption, access controls, and data retention policies.
• Additionally, it is important to regularly review and update the cluster’s security and compliance settings to ensure that they are up-to-date and in compliance with any changes in regulations.

Overall, managing and maintaining a Splunk cluster requires ongoing monitoring and troubleshooting, managing indexing and search performance, and ensuring data security and compliance. By taking a proactive approach and regularly reviewing and adjusting the cluster’s settings, you can ensure that the cluster is functioning at optimal levels and that the data stored in it is secure and compliant.

Can you walk through a real-world example of how you have used Splunk to solve a problem?

In a previous organization, I was tasked with solving an issue with their website’s login page. Users were complaining about slow page load times and intermittent login failures. The traditional logging systems in place were not able to provide enough insights to troubleshoot the issue effectively.

I used Splunk to gather and analyze the log data from various components of the website’s infrastructure, including the web servers, database servers, and network devices. I also set up custom logs for the login page to track key performance metrics such as response times and error rates.

Using Splunk’s search processing language (SPL), I was able to create real-time dashboards and alerts to monitor the website’s performance. This helped me to quickly identify the root cause of the issue, which was a problem with the database server. By isolating the issue, I was able to resolve it and improve the overall performance of the website’s login page.

The implementation of Splunk not only helped me to resolve the specific issue but also gave the organization a centralized logging solution that could be used to monitor and troubleshoot other problems in the future. This improved the overall efficiency and effectiveness of their IT operations.

Basic Interview Questions

Q1. What is a search head?

A search head is a Splunk Enterprise instance that distributes searches to indexers called “search peers”. The search heads can either be dedicated or not, depending on whether they perform the indexing also. Moreover, dedicated search heads don’t have any indexes of their own, other than the usual internal indexes. Instead, they consolidate and display results that are originated from remote search peers.

Q2. What do you mean by SHC?

SHC stands for Search Head Clustering. This adds horizontal scalability and eliminates the single point of failure from the search tier. Besides, a minimum of three search heads is needed so as to implement an SHC. In order to manage the SHC configuration, an extra Splunk component known as the Search Head Cluster Deployer is needed for each SHC.

Q3. What is the role of captain?

The captain is basically a cluster member with extra roles and responsibilities that are beyond the search activities common to all cluster members. It helps in coordinating the activities of the cluster. Though any member can perform the role of captain, the cluster can have only one captain at any time. However, if failures occur over time then the captain gets changed and a new member gets elected for the position.

Q4. What does load balancing do?

Load balancing eases the process of distributing data across different indexers so as to handle considerations like high data volume, fault tolerance, and horizontal scaling for enhanced search performance. Moreover, in load balancing, the forwarder routes data sequentially to different indexers at specified intervals.

Q5. What is a server class?

A server class is generally a group of deployment clients sharing one or more defined characteristics. For instance, all Windows clients can be grouped into one server class and all Linux clients into some other server class. Moreover, server classes are used to map a group of deployment clients to one or more deployment apps.

Q6. What functions are performed by Splunk Enterprise?

The following functions are performed by the Splunk Enterprise:

• Ingests data from files, networks, or other sources.
• Parses and indexes the data.
• Runs searches on the data that is indexed.

Q7. What are the main processing components?

The main types of processing components are:

• Forwarders
• Search heads
• Indexers

Q8. What do you understands by an indexer cluster?

An indexer cluster is basically a group of indexers configured to replicate the data of one another so that the system keeps multiple copies of all data. Thus, this is known as the index replication process. Consequently, indexer clusters prevent data loss while promoting data availability for searching by maintaining multiple, identical copies of data.

Q9. What is indexing?

Indexing is a process of speeding up the search process by giving numeric addresses to the piece of data being searched. Moreover, Splunk indexing is somewhat similar to the concept of indexing in databases.

Q10. What are pipelines and processors?

• Pipelines are single threads that are inside the splunkd process, configured with a single snippet of XML.
• Processors are individual, C, or C++ reusable functions acting on the stream of IT data that passes via pipeline. Pipelines can pass data to each other via queues.

Q11. What is the use of monitoring console tool.

The monitoring console is the monitoring tool of Splunk Enterprise. It allows viewing the detailed topology and performance information about Splunk Enterprise deployment. Moreover, the monitoring console was called the Distributed Management Console before Splunk Enterprise version 6.5.0.

Q12. What is data routing?

A forwarder basically routes events to particular hosts on the basis of criteria like source, source type, or patterns in the events themselves in data routing. Moreover, routing requires a heavy forwarder at the event level.

Q13. What is execution cost?

The execution cost is meant for listing information about the components of the search and the level of impact of each component on the holistic performance of the search.

Q14. What is SmartStore?

SmartStore is basically an indexer capability that gives a way to make use of remote object stores, like Amazon S3 to store indexed data. So, as a deployment’s data volume enhances, demand for storage outpaces the demand for computing resources. Moreover, SmartStore lets you manage indexer storage and compute resources in a cost-effective manner by separately scaling those resources.

Q15. Mention the advantages of SmartStore.

SmartStore has the following benefits:

• Bootstrapping capability.
• Reduced cost of storage.
• Access to high availability and data resiliency features.
• Simple and flexible configuration with per-index settings.
• The ability to scale compute and storage resources separately
• Ensuring efficient use of resources.

Q16. Define an index?

An index is basically the store for data processed by Splunk Enterprise. It constitutes a collection of subdirectories that are called buckets. These buckets mainly have two types of files: raw-data files and index files.

Q17. What are raw event searches?

Raw event searches are those searches that retrieve events from indexes and are used typically when we wish to analyze a problem. Some instances of these searches are: checking error codes, investigating security issues, correlating events, and analyzing failures.

Q18. What is the Splunk software?

Well, Splunk software performs various tasks, including ingesting data, indexing events, processing data into events, and searching the indexed events. All these tasks, and the steps in-between, create data that is recorded into log files by the Splunk software.

Q19. What is an intermediate forwarder?

We use an intermediate forwarder between a group of forwarders and the indexer in order to handle some advanced use cases. In this case, the originating forwarders send data to a consolidating forwarder, which forwards the data further on to an indexer. In some other cases, the intermediate forwarders also do data indexing.

Q20. What are the types of indexing?

Types of indexing are:

• Main 
• Internal 
• Audit 

Q21. What are transforming searches?

Transforming searches are those searches that perform a kind of statistical calculation with a set of results. These are the searches where we retrieve the events first from an index and subsequently pass the events into one or more search commands. 

Q22. What is a valid cluster?

A valid cluster consists of primary copies of all its buckets and hence is capable of handling search requests across the entire set of data. A valid cluster also has primary copies for every site with search affinity in the case of a multisite cluster.

Q23. What is indexer acknowledgement ?

Indexer acknowledgment is a feature that helps in preventing the loss of data when data is sent to an indexer by the forwarders. Indexer acknowledgment is controlled by the Boolean useACK setting in inputs. conf and outputs. conf.

Q24. Name some deployment methods.

Some methods of deployment are:

• Deployment architecture
• Location 
• Cluster types

Q25. What do you mean by an out-of-sync member?

An out-of-sync member is a member that can’t sync its own set of replicated configurations with the common baseline set of replicated configurations that are maintained by the current captain. However, one does not want an out-of-sync member in order to become captain.

Q26. What does GDO stand for?

GDO stands for Guided Data Onboarding that gives end-to-end guidance for getting specific data sources into particular Splunk platform deployments. However, one needs to have a Splunk deployment up and running and have an admin or equivalent role so as to install add-ons.

Q27. What does distributed data access provide?

Well, distributed data access gives the best control over data access for a huge infrastructure. Further, we can install Splunk on any source host and then configure it so as to use any Splunk input module in order to access data from FIFO queues, files, and network ports on that host.

Q28. What does multiple datastore peering mean?

Multiple datastore peering provides data-level access controls in big, multi-application, or multi-datacenter environments. Hence, multiple datastore peering gives additional data indexing capacity similar to multiple datastore clustering.

Q29. How can a user access a cluster?

A user can access the cluster in the same way as that of accessing any search head. They have to point their browser at any search head that is a member of the cluster. Moreover, it does not matter which search head a user accesses as cluster members share jobs, search artifacts, and configurations. The user has the access to the same set of dashboards, searches, and more.

Q30. What is ProxySSO?

ProxySSO allows configuration of Single-Sign On for Splunk instances via a reverse proxy server. Besides, a user logged in using ProxySSO can access Splunk Web smoothly.

Q31. What are the components of Splunk Enterprise?

The various types of Splunk Enterprise components are:

• Management components
• Processing components

Q32. How do we delete indexed data?

We should use the CLI clean command if we wish to delete our test index and start over on Splunk Enterprise.

Q33. What is the purpose of Search Job Inspector?

Well, the Search Job Inspector is a tool that is used to take a closer look at what your search is doing and see where is the Splunk software spending maximum of its time.

Q34. When does license warning occur?

Well, license warnings occur when one exceeds the maximum daily indexing volume that is allowed for their license. However, if one has multiple license warnings and has exceeded the license warning limit for the license then that person will get a license violation.

Q35. What do you mean by sparse searches?

Sparse searches are the searches looking for a single event or events that appear infrequently within a large data set. Also, some instances of these searches are- searching for a particular and unique IP address or error code.

Q36. What are the performance factors in Splunk enterprise deployment?

The performance factors in a Splunk Enterprise deployment are:

• The number of concurrent users.
• Types of searches used.
• The number of concurrent searches.

Q37. What is a deployment server?

A deployment server is an instance of Splunk Enterprise that behaves like a centralized configuration manager for any number of other instances, known as deployment clients. Moreover, any full Splunk Enterprise instance can act as a deployment server. However, a deployment server cannot be a client of itself.

Q38. What do you mean by a deployer?

The deployer is a Splunk Enterprise instance used to distribute apps and other configuration updates so as to search head cluster members. Subsequently, the set of updates that the deployer distributes is known as the configuration bundle.

Q39. Which type of files are there in a bucket?

The two key types of files in a bucket are:

• The processed external data in compressed form
• Indexes pointing to the raw data.

Q40. What does the master node do?

The master node is used in managing the cluster. Alongside, it coordinates the replicating activities of the peer nodes and tells the search head about where the data has to be found. Moreover, it helps in managing the configuration of peer nodes and also orchestrating the remedial activities in case a peer goes down.

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