Terraform interview questions and answers in 2025

Terraform providers are plugins that enable Terraform to interact with specific infrastructure platforms, such as AWS, Azure, or GCP. They provide a way for Terraform to manage and provision resources on these platforms. Each provider implements resource types and APIs to create, update, and delete resources.

By configuring a provider in Terraform, you can leverage its resource types to define infrastructure as code and manage the lifecycle of resources on the target platform.

Terraform modules are self-contained packages of Terraform configurations that encapsulate a specific set of resources and their associated dependencies. Modules promote reusability by allowing you to define and share infrastructure components across projects and teams.

With modules, you can abstract complex configurations into reusable building blocks, reducing duplication, and improving maintainability. Modules can be published and consumed internally or through public module registries, enabling code sharing and collaboration.

Terraform uses remote state management to store and share the state file, which contains the current state of the infrastructure. Remote state management involves storing the state file in a remote backend, such as AWS S3, Azure Blob Storage, or HashiCorp Terraform Cloud.

Storing the state remotely allows for collaboration, locking, and centralized management. By using a remote state, multiple users or teams can work on the same infrastructure and maintain a consistent and up-to-date view of the infrastructure state.

Terraform workspaces provide a way to manage multiple environments within a single Terraform configuration. Each workspace represents a distinct state and set of variables. By creating separate workspaces for different environments (e.g., development, staging, production), you can maintain environment-specific configurations without duplicating the entire codebase.

Workspaces allow you to switch between environments easily, ensuring isolation and maintaining separate states and variable values for each environment.

Terraform provides built-in mechanisms to handle resource failures and retries. When a resource creation or update fails, Terraform detects the failure and automatically rolls back changes for that resource. It can then retry the operation based on the specified retry settings.

Terraform also supports various error-handling techniques, such as using count and conditional expressions to conditionally create or destroy resources based on the success or failure of other resources. These features help ensure the reliability and resilience of infrastructure deployments.

Terraform backends define where Terraform stores the state file and how it interacts with it. Backends can be local or remote. Local backends store the state file on the local disk, which is suitable for individual use or small teams. Remote backends store the state file remotely, allowing for collaboration, locking, and centralized management.

Terraform provides several remote backend options, including AWS S3, Azure Blob Storage, and HashiCorp Terraform Cloud. Each backend option has its configuration settings and benefits, enabling flexibility and scalability in state management.

To organize a Terraform codebase for better maintainability, you can adopt various practices:

Modularization: Break down the codebase into reusable modules, encapsulating related resources and configurations.

Folder Structure: Organize files into logical folders based on resource types, environments, or modules.

Naming Conventions: Use consistent and descriptive naming conventions for resources, variables, and modules.

Documentation: Include comments, README files, or documentation to provide guidance and context for the codebase.

Version Control: Utilize a version control system like Git to track changes, collaborate, and roll back if needed.

By following these practices, you can enhance code readability, reusability, and collaboration, making the codebase easier to maintain and evolve.

Terraform interpolation syntax allows you to reference and combine values within Terraform configurations. It uses the ${} syntax to interpolate variables, attribute references, or functions. Interpolation enables dynamic configuration generation and composition.

You can use interpolation to reference variables, access resource attributes, concatenate strings, perform arithmetic operations, and more. It provides flexibility and programmability to define configurations that adapt to changing requirements or conditions.

Terraform handles drift detection by comparing the state stored in the state file with the current state of the infrastructure. During a Terraform application, it detects any differences between the two states and identifies resources that have drifted.

To reconcile the drift, Terraform determines the necessary actions (create, update, or delete) to bring the infrastructure back to the desired state defined in the configuration. By applying the changes, Terraform ensures that the infrastructure aligns with the intended configuration and resolves any discrepancies.

During a Terraform application, Terraform examines the configuration and state to determine the changes needed to reach the desired state. It creates an execution plan that outlines the actions required for resource creation, modification, or deletion. After confirming the execution plan, Terraform applies the changes by invoking the respective provider APIs.

It provisions or updates resources according to the plan and updates the state file with the new infrastructure state. Terraform captures the output values of the resources, which can be used for further configuration or reference in subsequent Terraform runs.

The "apply" command in Terraform is used to create or update the infrastructure based on the current configuration. It compares the configuration with the current state, determines the necessary changes, and applies them to the infrastructure.

On the other hand, the "refresh" command is used to reconcile the state with the actual resources in the infrastructure. It updates the state file by querying the infrastructure provider APIs and refreshing the resource information in the state. The "refresh" command does not make any changes to the infrastructure; it only updates the state file to reflect the current state accurately.

Terraform provisioners are used to execute scripts or commands on a remote resource during the provisioning process. They are typically used to perform configuration tasks, such as software installations, package updates, or service configurations.

Terraform provides several types of provisioners, including "local-exec," "remote-exec," and "file." By configuring provisioners within resource blocks, you can define the necessary actions to be executed on the provisioned resources, ensuring they are properly configured after creation or update.

Terraform integrates seamlessly with various IaaS providers, such as AWS, Azure, and Google Cloud Platform. To use Terraform with an IaaS provider, you need to configure the provider credentials and connection settings in the Terraform configuration.

Once configured, you can define resource blocks using the provider-specific resource types and configure them according to the desired infrastructure specifications. When running Terraform commands, such as "init," "plan," and "apply," Terraform communicates with the IaaS provider APIs to provision, update, or delete the specified resources.

The "null_resource" in Terraform represents a resource that doesn't directly correspond to a physical or virtual infrastructure object. It is a placeholder resource that can be used to execute provisioners or perform actions that are not tied to a specific resource type.

The "null_resource" can be helpful when you need to perform tasks like running local-exec provisioners, calling external scripts, or executing commands that are not related to a particular resource. It provides flexibility and allows for custom actions within the Terraform workflow.

Managing secrets and sensitive data in Terraform require careful consideration to ensure security. Best practices include:

• Storing secrets outside of version-controlled files, using tools like HashiCorp Vault or cloud-specific secret management services.
• Utilizing Terraform input variables or environment variables to pass sensitive values securely during runtime.
• Encrypting sensitive data using tools like Terraform Vault provider or native encryption mechanisms provided by the infrastructure platform.
• Avoid writing secrets in plain text within Terraform configurations or logs.

By following these practices, you can protect sensitive information and minimize the risk of exposing secrets unintentionally.

Terraform workspaces allow you to define environment-specific variable values by associating different values with each workspace. You can create separate variable files for each workspace or use conditional expressions within the variable definitions to set environment-specific values.

When switching between workspaces, Terraform loads the corresponding variable values, ensuring that each environment uses the appropriate configuration settings. This flexibility enables you to manage variables and configurations specific to different environments, facilitating efficient infrastructure deployment across multiple stages.

The "locals" block in Terraform allows you to define reusable expressions within the configuration. By defining variables or expressions within the "locals" block, you can assign them values based on other variables, attribute references, or calculations.

These local values can be used within the configuration to simplify and centralize complex expressions, avoiding duplication. The "locals" block promotes code reuse, improves readability, and provides a way to encapsulate logic or calculations that are used across multiple resource definitions.

Terraform modules are self-contained packages of Terraform configurations that encapsulate a specific set of resources and their associated dependencies. They promote code reusability and modularity by allowing you to define and share infrastructure components across projects and teams.

Remote modules, on the other hand, refer to modules hosted remotely, typically in a version control repository or a module registry. Remote modules provide a way to retrieve and use pre-configured modules directly from a remote source. They enable code sharing, versioning, and collaboration by allowing users to consume modules without manually copying or managing the module code locally.

In summary, modules are the concept of reusable infrastructure configurations, while remote modules are a way to access and consume those reusable configurations from remote sources.

Targeted resource deployment in Terraform can be achieved by using the "-target" flag with the "apply" or "plan" command. By specifying the target resource's address or name, Terraform focuses only on that particular resource and its dependencies during the deployment or planning process.

Targeted deployment is useful while isolating changes to specific resources without affecting the rest of the infrastructure.

Terraform's "data" blocks allow the retrieval and use of data from remote systems or APIs during the Terraform execution. Data blocks can fetch information such as AWS AMI IDs, VPC details, or external configuration settings. They enable Terraform to incorporate external data into the configuration, making it dynamic and adaptable to the current state of the infrastructure or external services.

Using the "remote-exec" provisioner with Windows instances involves configuring the provisioner block in Terraform to execute commands or scripts on the remote Windows machine.

The provisioner typically requires authentication details, such as SSH or WinRM credentials, to establish a remote connection. Once connected, the provisioner can run PowerShell commands or scripts to perform desired configurations on the Windows instance.

Terraform's "count.index" and "count.indexes" allow for resource customization based on the count of a resource. The "count.index" variable represents the current index of a resource within a count block.

It can be used to generate unique resource names or perform conditional logic based on the index. The "count.indexes" variable provides a list of all indices within a counted block, allowing for more advanced resource customization based on multiple indices.

Terraform workspaces are used to manage multiple instances of the same infrastructure within a single Terraform configuration. Each workspace represents a distinct state and set of variables. Workspaces provide a convenient way to deploy and manage different environments, such as development, staging, and production, without the need for separate configurations or projects.

In contrast, Terraform environments refer to separate copies of the entire Terraform codebase with their isolated state and resources.

The "sensitive" argument in Terraform is used to hide sensitive output values when displaying the Terraform plan or applying the output. By marking an output variable as sensitive, its value will be masked in the output, preventing accidental exposure of sensitive information. This is useful when dealing with outputs that contain passwords, private keys, or other confidential data that should not be visible to everyone.

Terraform's "dynamic" blocks allow for the dynamic creation of repeated nested blocks within resource configurations. They provide a flexible way to define variable-length blocks, where the number of blocks depends on the values of other variables or data sources.

Dynamic block attributes can be generated based on lists, maps, or other complex data structures, enabling dynamic resource creation and configuration.

The "locals" block in Terraform can be used to define conditional expressions by leveraging Terraform's built-in functions and conditionals. By combining conditional logic with locals, you can create reusable expressions that adapt to different scenarios or conditions.

This helps simplify complex expressions, make code more readable, and improve maintainability.

Terraform's "lifecycle" block allows the specification of lifecycle-specific configuration for resources. It provides control over resource behavior during creation, update, and deletion. The lifecycle block supports configuration options like resource creation and update timeouts, prevent destruction, ignore changes, and more.

It is useful when you need fine-grained control over resource management and want to define specific behavior for certain resources.

Terraform can manage non-cloud infrastructure resources by utilizing various provisioners and external tools. Provisioners like "local-exec" or "remote-exec" can execute scripts or commands on local or remote instances, enabling the configuration of non-cloud resources.

Additionally, Terraform can be integrated with infrastructure deployment tools like Ansible or Chef to handle the provisioning and configuration of non-cloud resources, leveraging their respective capabilities.

Terraform handles drift detection and resource reconciliation by comparing the current state stored in the state file with the desired state defined in the configuration. During a Terraform application, it detects any differences between the two states and determines the necessary actions to converge the infrastructure to the desired state.

Terraform can create, update, or delete resources as needed to reconcile the differences and bring the infrastructure into the desired state.

The "plan-out" flag in Terraform is used to preserve the planned output to a file for later use. By specifying the "-out" flag followed by a filename, Terraform saves the execution plan to that file. The preserved plan file can be used to apply the exact planned changes at a later time, ensuring consistency between the planned and applied changes.

Terraform workspaces are used to handle environment-specific configurations by creating separate workspaces for each environment (e.g., development, staging, production). Each workspace can have its own set of variables, allowing for environment-specific configuration values.

By switching between workspaces, you can target and manage different environments without modifying the underlying configuration files, simplifying environment-specific deployments.

Conditional resource creation based on variables in Terraform can be achieved using the "count" parameter or the "for_each" argument. By defining a conditional expression with these constructs, you can control whether a resource is created or not based on the value of a variable or condition. This allows for dynamic resource creation, enabling the infrastructure to adapt based on the provided input.

Terraform can be used in conjunction with infrastructure deployment tools like Ansible or Chef to manage infrastructure provisioning and configuration. Ansible or Chef can handle tasks such as installing software, configuring servers, and managing services, while Terraform focuses on infrastructure provisioning and orchestration.

By combining Terraform with these tools, you can achieve a comprehensive infrastructure automation solution.

Terraform can be used with infrastructure orchestration tools like Kubernetes or Docker Swarm to manage the deployment and configuration of containerized applications. Terraform can provision the underlying infrastructure resources (e.g., virtual machines, networking), while Kubernetes or Docker Swarm can handle the container orchestration and application deployment. Together, they provide a powerful solution for managing container-based workloads at scale.

Terraform data sources allow you to fetch and reference information from external systems or existing resources. They provide a way to query and import data into your Terraform configurations.

You can use data sources to retrieve attributes or metadata from various sources such as cloud providers, databases, or APIs. This data can then be used to make decisions, configure resources, or establish dependencies within your infrastructure.

Blue-green deployments involve creating two identical environments (blue and green) and switching traffic from one to the other. In Terraform, you can achieve this by creating two sets of infrastructure resources with slight differences, such as different AWS Auto Scaling Groups or Azure Virtual Machine Scale Sets.

Once the new environment (green) is provisioned and tested, you can update the load balancer or DNS records to direct traffic to the green environment. Terraform enables you to manage the infrastructure changes required for blue-green deployments in a declarative and automated manner.

Immutable infrastructure refers to the practice of creating and deploying infrastructure components as immutable artifacts. With Terraform, you can achieve an immutable infrastructure by treating infrastructure as code and rebuilding the entire infrastructure stack whenever changes are required.

The process involves defining all resources and configurations in Terraform code, executing Terraform application to create a new set of infrastructure, and replacing the existing infrastructure with the new one. This approach promotes reliability, consistency, and easier rollbacks since the infrastructure is never modified in place.

Terraform can be integrated with CI/CD pipelines to automate the deployment and management of infrastructure. Here's the typical process:

• Commit the Terraform configurations to a version control system (e.g., Git).
• Set up a CI/CD pipeline that monitors changes to the Terraform code repository.
• In the pipeline, execute Terraform commands such as init, validate, and plan to ensure the configurations are valid and generate an execution plan.
• Use Terraform's apply command to create or modify infrastructure based on the approved changes.
• Optionally, leverage infrastructure testing and verification tools to validate the deployed infrastructure.
• Finally, trigger additional pipeline stages for application deployment, testing, and release.

Remote execution in Terraform involves running the Terraform commands remotely on a dedicated infrastructure or service. This approach offers several benefits, including:

Isolation: The remote execution environment is separate from the local development environment, minimizing the impact of local issues on Terraform operations.

Consistency: By executing Terraform commands in a controlled environment, you ensure consistent tooling versions, dependencies, and configurations across the team.

Collaboration: Multiple team members can access and execute Terraform operations in a shared environment, facilitating collaboration and knowledge sharing.

Scalability: Remote execution can leverage infrastructure resources optimized for large-scale Terraform operations, such as executing plans or applying changes in parallel.

Terraform state locking is crucial for preventing concurrent modifications to the same infrastructure. To manage state locking for team collaboration, you can use a remote backend with built-in state locking support.

Terraform supports various remote backends like Amazon S3, Azure Storage, or HashiCorp Consul. By configuring a remote backend, Terraform automatically handles the state locking, ensuring that only one user or process can modify the state at a time.

This prevents conflicts and data corruption when multiple team members are working on the same infrastructure concurrently.

Terraform allows conditional resource creation through the use of the count meta-argument. By specifying a condition within the count block of a resource, you can control whether the resource should be created or not based on the evaluation of the condition.

For example, you can use count with an input variable to conditionally create resources based on user input or other dynamic factors. Terraform evaluates the condition during the planning phase and creates the resource instances accordingly.

This feature enables you to dynamically control the creation of resources based on various conditions.

Dynamic nested blocks in Terraform allow you to generate complex nested configurations based on input data or dynamic conditions. By using dynamic blocks, you can create repeated blocks of configurations without hardcoding them.

For example, you can dynamically generate multiple AWS security group rules based on an input variable containing a list of rules. Dynamic blocks provide flexibility and reduce repetition in your Terraform code, making it easier to manage and scale configurations that require dynamic or variable-sized nested blocks.

Rolling updates or zero-downtime deployments involve updating infrastructure components without causing service disruptions. With Terraform, you can achieve this by utilizing features such as rolling deployment strategies and lifecycle hooks.

For example, you can define an AWS Auto Scaling Group with a rolling update policy to gradually replace instances while ensuring the overall availability of the application. Terraform allows you to specify update policies, health checks, and other parameters to control the pace and behavior of updates, minimizing downtime and ensuring smooth transitions.

Terraform workspaces provide a way to manage multiple distinct sets of infrastructure configurations within a single root module. In a team environment, each team member can create their workspace, allowing them to work independently without interfering with each other's state files. The process involves:

• Creating a workspace using the Terraform workspace new command.
• Switching to the desired workspace using Terraform workspace select.
• Applying changes to the selected workspace with Terraform apply.
• Each workspace maintains its state file, isolating the infrastructure for each team member or environment.

To manage multiple environments with separate Terraform state files, you can leverage Terraform workspaces or separate directories for each environment.

Each environment's infrastructure can be defined in its own set of Terraform configurations. By using workspaces, you can keep the infrastructure definitions within the same root module but maintain separate state files for each environment.

Alternatively, you can use separate directories for each environment and maintain separate state files manually. Managing separate state files ensures the isolation and independent management of each environment's infrastructure.

Creating custom Terraform providers involves implementing a provider plugin that conforms to the Terraform Provider Protocol. The process generally includes the following steps:

• Set up a development environment with the appropriate tooling, including the Terraform SDK and language-specific dependencies.
• Implement the necessary CRUD (Create, Read, Update, Delete) operations for managing resources within the target system or API.
• Define the provider's schema to map Terraform resource types to their corresponding API endpoints or actions.
• Compile and package the provider into a plugin binary compatible with Terraform.
• Install the custom provider plugin in the Terraform environment where it will be used.
• Use the provider's resources and data sources in Terraform configurations to manage resources within the target system.

The "remote-exec" provisioner in Terraform allows executing commands on remote resources after they have been created or updated. Some benefits of using this provisioner include:

Configuration management: The provisioner enables running scripts or commands to configure newly provisioned resources with custom settings or dependencies.

Bootstrapping: You can automate the initial setup and configuration of resources, making them ready for use as soon as they are provisioned.

Flexibility: The provisioner supports running arbitrary commands, making it versatile for various use cases.

Integration: By combining the "remote-exec" provisioner with other Terraform resources, you can create powerful and automated configuration management workflows.

The Terraform Graph command generates a visual representation of resource dependencies within a Terraform configuration. By running Terraform graphs, you can obtain a graph in the DOT format. This graph can be converted to various visual formats, such as PNG or SVG, using graph visualization tools.

Leveraging the Terraform Graph command helps you understand the relationships and dependencies between resources, identify potential issues, and gain insights into the overall structure of your infrastructure.

The Terraform "taint" command is used to mark a resource as tainted, which indicates that it needs to be recreated on the next Terraform run. Tainting a resource effectively forces Terraform to destroy and recreate it, even if no changes are detected. This can be useful when troubleshooting or when a resource becomes corrupted and needs to be recreated.

The "untaint" command, on the other hand, removes the tainted state from a resource. It allows you to revert the taint status of a resource so that subsequent Terraform runs will only recreate it if there are actual changes detected.

The Terraform "for" expression allows dynamic resource creation based on a list or map variable. The process involves:

• Defining a list or map variable that contains the necessary input values for resource creation.
• Using the "for" expression within a resource block to iterate over the variable and generate multiple instances of the resource.
• Within the "for" expression, define the resource attributes or arguments that vary based on the iteration.
• Terraform will create the specified number of resource instances based on the length of the variable, with each instance having the desired attributes or arguments as defined within the "for" expression.

Sentinel is a policy-as-code framework integrated with Terraform to enforce compliance and security requirements. Sentinel policies are rules defined in a high-level language that govern the behavior of Terraform configurations and actions.

These policies enable you to enforce access controls, security practices, naming conventions, and other governance rules. By integrating Sentinel policies, you can ensure that infrastructure changes conform to organizational policies and prevent the deployment of non-compliant or insecure configurations.

The "Terraform.tfvars" file is used to assign values to variables declared within Terraform configurations. Instead of defining variables directly within the configuration files, you can store them in the "Terraform.tfvars" file, which Terraform automatically loads.

This approach simplifies the management of variable values, especially when working with sensitive or environment-specific information. By separating variable assignments from the configuration files, you can provide different "Terraform.tfvars" files for different environments or teams, allowing for more flexible and reusable configurations.

The automation of Terraform commands can be achieved by leveraging scripting languages such as Bash, Python, or PowerShell. Scripting languages provide the ability to orchestrate and execute Terraform commands programmatically.

For example, you can write scripts that automate the execution of Terraform init, plan, and apply commands, passing in the necessary arguments and handling any required input or authentication.

These scripts can be integrated into CI/CD pipelines, scheduled jobs, or other automation frameworks to enable the continuous and automated management of infrastructure.

When using Terraform with Kubernetes, you can manage Kubernetes resources by utilizing the Kubernetes provider. The process involves:

• Configuring the Kubernetes provider in your Terraform code, specifying the necessary connection details to the Kubernetes cluster.
• Defining Kubernetes resources such as deployments, services, or config maps using Terraform resource blocks.
• Specifying the desired state of the Kubernetes resources in Terraform configurations.
• Running Terraform apply to create or update the defined Kubernetes resources in the cluster.
• Terraform will interact with the Kubernetes API server, creating, modifying, or deleting the specified resources based on the desired state.

Terraform workspaces can be created and managed programmatically using the Terraform CLI or Terraform SDK. The process involves:

• Using the Terraform CLI commands or SDK functions to create a new workspace.
• Assigning a name and any necessary variables to the workspace.
• Performing operations on the workspace, such as selecting or deleting it, using the appropriate CLI commands or SDK functions.
• You can write scripts or use programming languages like Python or Go to interact with the Terraform CLI or SDK and automate workspace creation and management as per your requirements.

The "tainted" attribute in Terraform indicates whether a resource has been manually marked as tainted or corrupted. When a resource is tainted, Terraform treats it as potentially needing replacement in the next Terraform run, regardless of whether there are any changes detected.

The "tainted" attribute is a way to manually trigger the destruction and recreation of a resource. This can be useful for troubleshooting or when you want to force the recreation of a resource to ensure its integrity.

Using Terraform's "partial configuration" for partial state import involves specifying a subset of resources in a configuration file that corresponds to the desired state to be imported. By using the -target option with the Terraform import command, you can import specific resources into Terraform's state file without importing the entire configuration.

This allows for selective management and updates of specific resources while keeping the rest of the state intact.

Terraform "backend configurations" define the settings for storing and retrieving the Terraform state file. They determine where the state is stored, such as a local file or a remote storage system like Amazon S3 or HashiCorp Consul. Backend configurations are specified in the Terraform configuration file using the backend block. The choice of backend affects how Terraform manages and retrieves state data, providing features like remote state locking and collaboration capabilities.

Automated testing for Terraform code can be implemented using tools such as Terratest or kitchen-Terraform. These tools allow you to write test cases and execute them against your Terraform codebase.

Automated testing helps validate the correctness of infrastructure changes, detect issues early, and ensure the desired state matches the actual state. It involves creating test fixtures, defining test scenarios, executing Terraform operations, and asserting the expected outcomes.

Using Terraform with external data sources involves utilizing data sources to fetch information from external systems or APIs and use that data in your Terraform configurations. The data block is used to define data sources, which can retrieve information like AMIs, security groups, or DNS records.

Terraform queries the external system during the planning phase and uses the fetched data to make decisions about resource creation and configuration.

Terraform's "trigger" block allows you to specify event-driven actions at the resource level. It can be used to trigger custom actions when certain events occur, such as the creation or deletion of a resource.

By defining a trigger block within a resource block, you can associate the resource with an external script or command to execute. This enables you to perform additional actions or integrations based on the occurrence of specific resource events.

Terraform's "module registry" is a central repository for sharing and discovering Terraform modules. The module registry allows users to publish their modules, which are reusable and shareable components of Terraform configurations.

By leveraging the module registry, you can easily discover existing modules that address your infrastructure needs, reducing duplication of effort. You can reference modules in your Terraform code using their registry URL and version.

"Remote state data" refers to storing Terraform's state in a remote location, such as a shared storage system or a remote backend. By storing the state remotely, different configurations and teams can share and access the same state data.

This enables cross-configuration communication, where one configuration can read data from another configuration's state. It helps facilitate infrastructure dependencies, coordination, and collaboration between different Terraform projects

The Terraform "destroy-timeouts" attribute allows you to control the destruction time limit for resources during the Terraform destroy operation. By specifying a timeout value for a resource in the configuration, you can set a maximum duration for Terraform to wait before forcefully destroying the resource if it takes longer than expected.

This attribute helps prevent resources from being stuck in the destruction process indefinitely and allows for better resource management during destruction.

Using Terraform's "count" feature provides advantages over resource duplication by allowing you to dynamically create multiple instances of a resource based on a given condition or variable. With "count," you can define a resource block with a count value that evaluates an expression, such as a variable or a conditional statement.

Terraform then creates the specified number of resource instances, reducing code duplication and enabling more efficient resource management and scalability.