Amazon LPs: A Developer Interview Guide

Amazon LPs are Amazon’s 16 Leadership Principles, which developer candidates use to prepare evidence-based answers about past work. Start by mapping truthful engineering stories to the worksheet below.

Amazon LP interview preparation works best when each principle is connected to a real engineering story, a clear STAR answer, defensible evidence, and practiced follow-up questions.

Start With the Story-Coverage Worksheet

Mark each principle supported by real evidence in a story. Blank columns reveal where another example may be useful; the principle summaries below serve as reference material.

Engineering story	CO	Own	I&S	R&R	L&C	H&B	HS	TB	BfA	Fru	ET	DD	HD	DR	BE	SS
__________________	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
__________________	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
__________________	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐

CO = Customer Obsession; Own = Ownership; I&S = Invent and Simplify; R&R = Are Right, A Lot; L&C = Learn and Be Curious; H&B = Hire and Develop the Best; HS = Insist on the Highest Standards; TB = Think Big; BfA = Bias for Action; Fru = Frugality; ET = Earn Trust; DD = Dive Deep; HD = Have Backbone; Disagree and Commit; DR = Deliver Results; BE = Strive to be Earth’s Best Employer; SS = Success and Scale Bring Broad Responsibility.

What Does “Amazon LPs” Mean?

In career and interview searches, “Amazon LPs” usually means Amazon’s Leadership Principles. The initials can also refer to Littlest Pet Shop, but that is not the intended meaning in a developer interview.

Amazon describes the principles as part of how its employees discuss new projects and decide how to solve problems. The official Leadership Principles page currently lists 16 principles. They cover customer focus, ownership, judgment, learning, quality, speed, trust, technical detail, disagreement, results, employment practices, and wider responsibility.

For a developer, an LP answer should come from concrete engineering work. Useful material includes a bug traced through several services, a migration with competing risks, a design disagreement, a delayed launch, a performance problem, or a mistake that changed how later work was handled.

The principles are not substitutes for technical preparation. They sit beside coding, data structures, algorithms, design, databases, and other technical topics in a complete coding interview roadmap.

The 16 Amazon Leadership Principles

The summaries below paraphrase Amazon’s published definitions. The evidence suggestions and practice prompts are preparation tools, not an official Amazon scoring rubric.

1. Customer Obsession

Customer Obsession means starting with customer needs and working backward while earning and preserving customer trust. For developers, the customer may be an external user or an internal team that depends on a service.

Developer evidence: A usability problem, reliability issue, support pattern, or performance bottleneck that changed the implementation plan.

Practice prompt: Tell me about a time customer evidence caused you to change a technical priority.

2. Ownership

Ownership means accepting responsibility beyond the narrow boundary of an assigned ticket. It includes considering lasting consequences and acting when a problem needs an owner.

Developer evidence: An incident, neglected service, risky dependency, or incomplete migration that you took responsibility for resolving.

Practice prompt: Describe a technical problem you owned even though it fell outside your normal responsibilities.

3. Invent and Simplify

Invent and Simplify combines experimentation with a preference for reducing needless complexity. A good story explains the problem, the simpler approach, and why that approach remained safe.

Developer evidence: A repeated manual process replaced by automation, a confusing API reduced to a clearer interface, or an overcomplicated design simplified before launch.

Practice prompt: Tell me about a system or process you made simpler without losing necessary behavior.

4. Are Right, A Lot

Are Right, A Lot concerns judgment. Strong judgment includes seeking different perspectives, testing assumptions, and changing direction when evidence contradicts the initial view.

Developer evidence: An architecture or debugging decision in which logs, experiments, peer feedback, or production behavior changed your conclusion.

Practice prompt: Describe a time your first technical assumption was wrong and how you corrected it.

5. Learn and Be Curious

Learn and Be Curious means continuing to develop skills and investigating unfamiliar problems. The strongest evidence shows how learning affected a decision or result.

Developer evidence: Learning an unfamiliar subsystem, tool, language feature, or failure mode to complete important work.

Practice prompt: Tell me about something you had to learn quickly to solve an engineering problem.

6. Hire and Develop the Best

Hire and Develop the Best covers recognizing ability, improving team capability, and helping people grow. Individual contributors can demonstrate it through interviews, mentoring, feedback, onboarding, and knowledge sharing.

Developer evidence: Mentoring a developer through a difficult project, improving onboarding, or giving feedback that led to stronger work.

Practice prompt: Describe how you helped another engineer develop a specific skill.

7. Insist on the Highest Standards

Insist on the Highest Standards means refusing to normalize preventable defects or weak operational practices. The standard should be tied to a real risk rather than personal preference.

Developer evidence: Adding tests, strengthening release checks, correcting unreliable monitoring, or stopping a launch because a serious defect remained.

Practice prompt: Tell me about a time you raised the quality bar despite schedule pressure.

8. Think Big

Think Big means looking beyond a local patch when a broader approach can create more value. A good answer still explains an achievable path from the current system to the larger idea.

Developer evidence: A platform capability, shared service, migration plan, or reusable architecture that replaced repeated local solutions.

Practice prompt: Describe a proposal that changed the scope or direction of an engineering project.

9. Bias for Action

Amazon’s Bias for Action principle says speed matters, many decisions are reversible, and calculated risk can be appropriate. The developer skill is separating reversible choices from changes that demand more caution.

Developer evidence: A contained incident mitigation, limited experiment, feature flag, or reversible rollout decision made with incomplete information.

Practice prompt: Tell me about a time you acted quickly while controlling technical risk.

10. Frugality

Frugality means working effectively within real constraints. Developer evidence should show thoughtful use of time, infrastructure, people, or existing systems without presenting damaging shortcuts as efficiency.

Developer evidence: Reusing a suitable internal capability, reducing unnecessary infrastructure use, or narrowing a project while preserving its required outcome.

Practice prompt: Describe how a constraint led you to a simpler or more efficient technical solution.

11. Earn Trust

Earn Trust includes listening carefully, communicating honestly, treating colleagues with respect, and acknowledging mistakes. Trust stories need observable behavior, especially when news was unwelcome.

Developer evidence: Disclosing a missed assumption, repairing a strained working relationship, or communicating incident status without hiding uncertainty.

Practice prompt: Tell me about a time you had to rebuild trust after a technical mistake or disagreement.

12. Dive Deep

Amazon describes Dive Deep as staying connected to details, checking work, and investigating conflicts between metrics and anecdotes. A developer answer should show the path from symptom to evidence to cause.

Developer evidence: Tracing a production failure across logs, queries, traces, configuration, and code until the suspected cause was verified.

Practice prompt: Walk me through a difficult technical problem that required detailed investigation.

13. Have Backbone; Disagree and Commit

This principle calls for respectful challenge when you disagree, followed by wholehearted commitment once a decision is made. The story should show evidence, professional disagreement, and reliable execution after the decision.

Developer evidence: Challenging an unsafe migration, questionable design, or unrealistic delivery plan, then supporting the final decision.

Practice prompt: Tell me about a technical decision you disagreed with and what you did next.

14. Deliver Results

Deliver Results means focusing on the work that matters, meeting the required quality level, and continuing through setbacks. A useful answer explains what shipped or improved and what the candidate personally did.

Developer evidence: A launch, recovery, migration, or performance improvement completed despite a meaningful obstacle.

Practice prompt: Describe a difficult engineering result you delivered and the obstacle that threatened it.

15. Strive to Be Earth’s Best Employer

This principle concerns creating a safer, more productive, fairer, and more supportive workplace where people can develop. Developer evidence can come from daily team practices rather than formal management authority.

Developer evidence: Improving on-call safety, making onboarding more accessible, correcting harmful team behavior, or supporting sustainable workloads.

Practice prompt: Tell me about a change you made that improved how engineers worked or developed.

16. Success and Scale Bring Broad Responsibility

This principle asks leaders to consider the wider effects of their systems and decisions. For developers, that can include security, accessibility, operational load, environmental cost, or unintended effects on users and teams.

Developer evidence: Identifying a harmful side effect of growth and changing a design, rollout, retention policy, or operating process.

Practice prompt: Describe a time you found a wider consequence that the original project plan had missed.

How Amazon Describes LPs in Student and Graduate Front End Engineer Interviews

Amazon’s guidance for student and graduate Front End Engineer candidates says a significant portion of behavioral discussion focuses on how candidates demonstrated the Leadership Principles in past jobs. It also says interviewers focus on the what, how, and why of past experiences rather than brain teasers.

The same student and graduate Front End Engineer guidance says each interviewer will typically ask two or three behavioral questions about successes or challenges and how the candidate handled them using the principles. That statement describes Amazon’s published Front End Engineer guidance. The exact format can differ by role and interview process, so preparation should support flexible answers instead of assuming that every candidate receives an identical sequence.

A first answer is rarely the end of the discussion. Prepare to explain:

• What the situation was and why it mattered.
• What responsibility belonged specifically to you.
• How you made the technical decision.
• Why you rejected or deferred alternatives.
• What evidence supported the result.
• What failed, surprised you, or created conflict.
• What you learned and would change.

This is close to the work of a good interviewer. The panelist interview guide for software engineers helps show why a credible answer must give the interviewer enough detail to separate team context from individual judgment.

Build an Engineering Story-Coverage Matrix

A story bank is a small set of truthful past experiences prepared for reuse across behavioral questions. In the coverage worksheet above, stories appear in rows and the 16 LPs appear in individually markable columns, so gaps become visible before the interview.

The matrix is a preparation worksheet, not an Amazon rubric. A story belongs under a principle only when the events provide real evidence for that principle.

Every row should contain five ingredients:

1. Ownership: What responsibility was personally yours?
2. Decision: What choice or judgment did you make?
3. Evidence: What observation, measurement, artifact, or feedback supports the result?
4. Lesson: What changed in your later behavior?
5. Probe preparation: Which detail would a skeptical interviewer ask you to explain?

After marking LP coverage above, use this companion table to develop each story:

Engineering story	Possible LP signals	Personal decision to prepare	Evidence to collect	Lesson to state	Likely follow-up probe	Weak-signal warning
Production incident	Ownership, Dive Deep, Bias for Action, Deliver Results	Which mitigation you chose and why its risk was acceptable	Timeline, error trend, recovery time, affected requests	What monitoring or release practice changed	What evidence ruled out the other suspected causes?	Saying “we fixed it” without naming your investigation
Service or data migration	Think Big, Insist on the Highest Standards, Frugality	How you selected the rollout and rollback plan	Compatibility checks, defect counts, resource use, delivery milestones	Which migration assumption you would test earlier	Why migrate instead of repairing the existing system?	Treating the migration itself as impact
Performance improvement	Customer Obsession, Invent and Simplify, Are Right, A Lot	Which bottleneck you prioritized after measurement	Latency, throughput, resource use, customer reports	How measurement changed your initial theory	How did you prove the change caused the improvement?	Claiming speed gains without a baseline
Product launch	Customer Obsession, Deliver Results, Success and Scale Bring Broad Responsibility	Which scope or reliability trade-off you made	Adoption evidence, defect reports, delivery date, support load	What wider effect appeared after release	What did you remove from scope, and who accepted that choice?	Presenting routine ticket completion as exceptional impact
Technical-debt work	Ownership, Insist on the Highest Standards, Learn and Be Curious	Why this debt deserved attention now	Incident history, change failure evidence, maintenance time	How you will prevent the same debt from returning	Why was this more valuable than feature work?	Calling disliked code “debt” without showing cost
Technical disagreement	Have Backbone; Disagree and Commit, Earn Trust, Are Right, A Lot	How you challenged the proposal and handled the final decision	Design notes, experiment results, review feedback	How you would make the disagreement more productive	What evidence would have changed your position?	Describing colleagues as obstacles
Mentoring or onboarding	Hire and Develop the Best, Strive to Be Earth’s Best Employer, Earn Trust	How you adjusted support to the person’s needs	Faster independent work, feedback, completed milestones	What you learned about teaching or feedback	What did the other engineer do, and what did you do?	Taking credit for another person’s achievement
Failed project or decision	Learn and Be Curious, Ownership, Success and Scale Bring Broad Responsibility	Which assumption or decision was yours	Missed target, defect, rework, feedback, post-incident action	The concrete behavior you changed afterward	When did you know the approach was failing?	Turning a disguised success into a “failure” story

This table covers all 16 principles across eight common engineering experiences. It does not mean eight stories are mandatory. A real migration might also contain conflict, mentoring, and failure, while an incident might offer little evidence for Think Big.

Start with the story, then identify the principles it honestly supports. Reversing that order encourages forced matches and vague claims. Architecture stories can also connect behavioral judgment with the trade-offs practiced in system design interview basics.

For each metric, keep its source in mind. If the evidence came from a dashboard, support ticket, test report, project record, or direct feedback, say so. If no numerical measurement exists, use defensible evidence such as a completed rollback test, approval from an affected team, removal of a manual step, or a documented change in operating procedure.

Turn One Developer Story Into a Strong STAR Answer

STAR stands for situation, task, action, and result. Amazon describes it as a structured way to cover those four parts. For developers, the action section usually needs the most space because it reveals debugging skill, trade-off judgment, and individual contribution.

The following production-incident answer is fictional. Its system, decisions, and numbers are practice material. Replace every detail with evidence from your own experience, and use only measurements you can explain and defend.

Part	Annotated fictional answer	What the section proves
Situation	”After a routine release, checkout requests began failing intermittently. The alert showed a fictional error rate of 7.8%, while CPU and memory remained near their fictional normal ranges.”	Concise context, user impact, and the first conflicting signals
Task	”I was the on-call developer for the service. I was responsible for limiting customer impact, finding the cause, and keeping the incident channel updated while another engineer prepared a rollback.”	Personal responsibility and coordination boundaries
Action	”I compared failures by application version, region, and dependency call. The errors appeared only on instances using a new connection-pool setting. I first reduced traffic to those instances because that change was reversible. I chose that mitigation over an immediate database restart because healthy instances were using the same database. After errors fell, I reproduced pool exhaustion in a test environment, reverted the setting, and wrote a validation check for future configuration changes. I posted evidence and decision updates in the incident channel, including what I had ruled out.”	Debugging path, reversible action, rejected alternative, technical judgment, and clear communication
Result	”In this fictional example, the error rate fell from 7.8% to 0.6% after traffic shifted, then returned to the fictional baseline after the revert. The fictional recovery time was 18 minutes. The validation check was added to the release process before the next deployment.”	Hypothetical measurable result plus a prevention step
Reflection	”I initially treated configuration as lower risk than code because it had passed review. I changed my approach afterward by requiring behavior-level validation for connection settings, not only syntax validation.”	A failed assumption and a specific lesson

This answer can provide evidence for several principles without naming them in the response:

• The candidate accepts responsibility and stays with the problem.
• The investigation follows detailed evidence instead of guessing.
• The mitigation favors a reversible action while protecting customers.
• The result includes recovery and a concrete prevention step.
• The incident updates expose uncertainty instead of hiding it.

Those signals can support Ownership, Dive Deep, Earn Trust, Bias for Action, and Deliver Results. The candidate should still tailor the telling. A Dive Deep question calls for more detail about the diagnostic path. An Earn Trust question calls for more detail about communication, uncertainty, and coordination.

Notice what the answer does not claim. It does not turn a team incident into a solo rescue. It names the other engineer’s rollback work, then isolates the candidate’s own analysis and decision. That distinction makes the account easier to test through follow-up questions.

Prepare for Follow-Up Questions and Weak Signals

Follow-up preparation is where a memorized story becomes a defensible account. For each story, write one-line answers to the following practice probes:

• Technical detail: Which logs, tests, traces, queries, or code paths supported the conclusion?
• Alternatives: What other approaches were considered, and why were they rejected?
• Disagreement: Who challenged the plan, and how did the evidence or conversation affect it?
• Individual contribution: Which actions and decisions were yours?
• Metrics: Where did each number come from, and what was the baseline?
• Failure: Which assumption, decision, or execution step did not work?
• Change: What would you do differently with the same information now?

Do not manufacture detail to fill a blank. If a result was not measured, say what evidence was available. “The affected team confirmed that the manual reconciliation step was no longer required” can be stronger than a precise number with no trustworthy source.

Common mistakes in Amazon LP answers

• Using vague team language. “We investigated” hides the candidate’s decisions. Name the team context, then switch to “I” for personal actions.
• Inflating routine work. Completing an assigned endpoint is not automatically Think Big or Deliver Results. Explain the obstacle, judgment, or outcome that made the work meaningful.
• Blaming colleagues. Conflict stories should explain the competing goals and the evidence, not assign a villain.
• Repeating principle slogans. Saying “I showed Ownership” provides less evidence than describing what responsibility you accepted.
• Inventing numbers. Unsupported precision creates an easy credibility failure when the interviewer asks how the metric was measured.
• Ignoring the question. A polished launch story is still weak if the prompt asks about failure and the answer contains no real failure.
• Hiding lessons behind success. A reflection should identify a changed behavior, test, process, or decision rule.

Practice aloud with someone who will interrupt and probe. The goal is not a smooth monologue. It is accurate recall under questioning.

A Seven-Day Amazon LP Prep Plan

If you have one week, this optional schedule organizes preparation around a specific artifact each day.

Day	Preparation task	Finished artifact
1	Inventory incidents, migrations, launches, performance work, debt, disagreements, mentoring, and failures	A raw list of truthful stories
2	Complete the story-coverage matrix	Visible LP coverage and gaps
3	Fill gaps with additional real experiences	At least one credible option for each principle
4	Draft compact STAR notes	Situation, task, actions, result, and reflection for each main story
5	Add defensible evidence	Sources for metrics, decisions, outcomes, and lessons
6	Rehearse follow-up probes	Answers for alternatives, conflict, failure, and changed behavior
7	Conduct a timed mock	Flexible answers delivered without reading a script

Readiness means having truthful coverage and flexible recall. It does not mean memorizing sixteen separate speeches. If visual study notes help you review structure quickly, the EbookWale Sample shows the format before you choose a larger study resource.

Where the Books Fit

Amazon LP preparation works best alongside language, data-structure, and design practice. JavaScript in Three Months suits candidates building job-ready technical depth while preparing interview stories, while JavaScript for Staff Engineers fits senior candidates who need sharper judgment around architecture, incidents, trade-offs, and technical leadership. Build the story matrix now, then rehearse it beside the technical work each story claims you can do.