Quick Answer
The correct answer is that both the old and new resources remain in the Terraform state. This happens because when you set create_before_destroy = true in a lifecycle block, Terraform first provisions the replacement resource, and only then attempts to destroy the original. If the destroy step fails after the new resource is successfully created, Terraform cannot remove the old resource from state—the destroy operation must complete fully for that removal to occur. As a result, the state file holds both resource instances at the same address, which is a key scenario tested on the HashiCorp Terraform Associate TF-003 exam. This question often traps candidates who assume a failed destroy means the old resource is automatically cleaned up; instead, Terraform preserves state to avoid data loss. A useful memory tip: “Create first, then destroy—if destroy fails, both stay.”
This TF-003 practice question tests your understanding of read, generate and modify configuration. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A Terraform configuration includes a resource block with a 'lifecycle' block that has 'create_before_destroy = true'. During an apply, the create step succeeds but the destroy step fails. What is the resulting state?
Only the new resource remains in state, old resource is destroyed.
Why wrong: Destroy failed, so old resource still exists.
The state is empty for that resource address.
Why wrong: Both resources are in state.
Only the old resource remains in state.
Why wrong: New resource was created and added to state.
Both the old and new resources are in state.
New resource created; old resource not destroyed.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Both the old and new resources are in state.
When `create_before_destroy = true` is set, Terraform creates the new resource first, then destroys the old one. If the destroy step fails after the new resource is created, both resources exist in the state file because Terraform does not remove the old resource from state until the destroy operation completes successfully. The state retains both resource instances at the same address, which is why option D is correct.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Only the new resource remains in state, old resource is destroyed.
Why it's wrong here
Destroy failed, so old resource still exists.
The state is empty for that resource address.
Why it's wrong here
Both resources are in state.
Only the old resource remains in state.
Why it's wrong here
New resource was created and added to state.
Both the old and new resources are in state.
Why this is correct
New resource created; old resource not destroyed.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
HashiCorp often tests the misconception that a failed destroy step automatically removes the old resource from state or that Terraform rolls back the entire operation, but in reality, Terraform only removes a resource from state after a successful destroy.
Detailed technical explanation
Under the hood, Terraform's state management uses a graph-based dependency resolver; with `create_before_destroy`, the new resource is provisioned and its state is written before the old resource's destroy is initiated. If the destroy fails (e.g., due to an API timeout or dependency violation), Terraform does not automatically roll back the creation, leaving both resources in state. This behavior is critical in real-world scenarios like AWS Auto Scaling groups, where failing to destroy the old instance after creating a replacement can lead to duplicate resources and unexpected costs.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the TF-003 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
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FAQ
Read, generate and modify configuration — This question tests Read, generate and modify configuration — Read the scenario before looking for a memorised answer..
The correct answer is: Both the old and new resources are in state. — When `create_before_destroy = true` is set, Terraform creates the new resource first, then destroys the old one. If the destroy step fails after the new resource is created, both resources exist in the state file because Terraform does not remove the old resource from state until the destroy operation completes successfully. The state retains both resource instances at the same address, which is why option D is correct.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This TF-003 practice question is part of Courseiva's free HashiCorp certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the TF-003 exam.
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