The Auto Club Group Software Engineer Interview Guide

The 60-second brief

A Software Engineer at The Auto Club Group (ACG) plays a critical role in driving the digital transformation of one of the largest AAA clubs in North America. In this role, you are responsible for building, maintaining, and modernizing the core software systems that power essential services for millions of members. These services span roadside assistance dispatch, insurance portals, travel planning tools, and financial services. The impact of your work in this position is immediate and widespread. Whether you are optimizing a dispatch algorithm that helps a stranded motorist or securing a transaction platform for insurance policyholders, your code directly affects the safety, security, and peace of mind of AAA members.

This guide is scoped to a Software Engineer candidate at The Auto Club Group.

Focus areas

• Algorithm Fundamentals
• Time Complexity Analysis
• Coding Interviews

The The Auto Club Group interview process

The Auto Club Group candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

Screening by the recruitment team to align on background, experience, and salary expectations.

What to demonstrate

• Screening by the recruitment team to align on background, experience, and salary expectations
• Depth in Algorithm Fundamentals

How to prepare

• Be able to walk your CV end to end in two minutes, and say why this company specifically.
• Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.

Technical Evaluations

Includes an aptitude test or an initial technical screen focusing on computer science fundamentals.

What to demonstrate

• Includes an aptitude test or an initial technical screen focusing on computer science fundamentals
• Depth in Algorithm Fundamentals

How to prepare

• Answer aloud and timed: What is the time complexity of an algorithm, and why is it crucial when designing systems for high-volume transactions?
• Answer aloud and timed: Mention and explain the different types of notation used for Time Complexity (Big O, Big Theta, Big Omega).

Virtual Onsite Loop

Comprehensive evaluation featuring algorithmic coding, system design, behavioral interviews, and collaborative coding.

What to demonstrate

• Comprehensive evaluation featuring algorithmic coding, system design, behavioral interviews, and collaborative coding
• Depth in Algorithm Fundamentals

How to prepare

• Answer aloud and timed: Explain how a binary search works. Is it possible to use a binary search for linked lists? Why or why not?
• Answer aloud and timed: How would you design a highly available API gateway that handles sudden spikes in traffic, such as during a regional weather emergency?

Where candidates lose points

Over-communicate during coding rounds

Do not code in silence. Talk through your logic, explain why you are choosing a specific data structure, and state your assumptions clearly. This is especially critical during the collaborative pairing session.

Prepare for legacy and modern discussions

Be ready to discuss how you bridge the gap between legacy systems and modern cloud architectures. Demonstrating respect for existing systems while showing enthusiasm for modernization is highly valued.

Some candidates have reported inconsistent interviewer behaviors during technical rounds

If you encounter a challenging or dismissive interviewer, remain calm, focus on your technical explanations, and guide the conversation back to your structured, professional problem-solving approach.

Questions & practice

Explain what an algorithm is in computing and how you measure its efficiency.

Medium · Computer Science Fundamentals & Algorith

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Explain the Quick Sort algorithm, including its average and worst-case time complexities.

Medium · Computer Science Fundamentals & Algorith

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

What is the time complexity of an algorithm, and why is it crucial when designing systems for high-volume tran

Medium · Computer Science Fundamentals & Algorith

What is the time complexity of an algorithm, and why is it crucial when designing systems for high-volume transactions?

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Mention and explain the different types of notation used for Time Complexity (Big O, Big Theta, Big Omega).

Medium · Computer Science Fundamentals & Algorith

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Explain how a binary search works. Is it possible to use a binary search for linked lists? Why or why not?

Medium · Computer Science Fundamentals & Algorith

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.

Approach

1. Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
2. Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
3. Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
4. State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.

Follow-up

• A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
• The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

1. Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
2. Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
3. For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
4. State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

• A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
• How do you honour an erasure request while resource_revision.actor_user_id still references this table?

How would you design a highly available API gateway that handles sudden spikes in traffic, such as during a re

Medium · Systems & Architecture

How would you design a highly available API gateway that handles sudden spikes in traffic, such as during a regional weather emergency?

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

Explain the differences between microservices and monolithic architectures. What factors would influence your

Medium · Systems & Architecture

Explain the differences between microservices and monolithic architectures. What factors would influence your decision to migrate a legacy system to microservices?

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

How do you ensure data consistency across multiple distributed databases in an insurance or transaction-heavy

Medium · Systems & Architecture

How do you ensure data consistency across multiple distributed databases in an insurance or transaction-heavy application?

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

One log partition stops advancing while the others drain

Easy · poison message · head-of-line blocking · consumer lag

Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.

Approach

1. Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
2. Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
3. Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
4. Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.

Follow-up

• The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
• How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?

Suggested practice checklist

Built from the rounds and topics The Auto Club Group candidates report.

Day 1 — Map the The Auto Club Group loop

• Write out the reported sequence: Initial Screening, Technical Evaluations, Virtual Onsite Loop.
• For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.

Day 2 — Work Algorithm Fundamentals

• Spend the session on Algorithm Fundamentals, which The Auto Club Group candidates report being tested on.
• Write one worked example in Algorithm Fundamentals and time yourself on it.

Deliverable: One timed worked example in Algorithm Fundamentals.

Day 3 — Work Time Complexity Analysis

• Spend the session on Time Complexity Analysis, which The Auto Club Group candidates report being tested on.
• Write one worked example in Time Complexity Analysis and time yourself on it.

Deliverable: One timed worked example in Time Complexity Analysis.

Day 4 — Work Coding Interviews

• Spend the session on Coding Interviews, which The Auto Club Group candidates report being tested on.
• Write one worked example in Coding Interviews and time yourself on it.

Deliverable: One timed worked example in Coding Interviews.

Day 5 — Answer out loud: Computer Science Fundamentals & Algorithms

• Answer aloud, timed: Explain what an algorithm is in computing and how you measure its efficiency.
• Answer aloud, timed: Explain the Quick Sort algorithm, including its average and worst-case time complexities.

Deliverable: Spoken answers to 2 reported Computer Science Fundamentals & Algorithms question(s), under time.

Day 6 — Answer out loud: Systems & Architecture

• Answer aloud, timed: How would you design a highly available API gateway that handles sudden spikes in traffic, such as during a regional weather emergency?
• Answer aloud, timed: Explain the differences between microservices and monolithic architectures. What factors would influence your decision to migrate a legacy system to microservices?

Deliverable: Spoken answers to 2 reported Systems & Architecture question(s), under time.

Day 7 — Answer out loud: Behavioral & Collaboration

• Answer aloud, timed: Describe a situation where you had to work with a difficult teammate or stakeholder. How did you manage the relationship to ensure the project succeeded?
• Answer aloud, timed: Tell me about a time when you had to make a technical decision without complete information. What was your approach, and what was the outcome?

Deliverable: Spoken answers to 2 reported Behavioral & Collaboration question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Describe your experience with cloud migration. What strategies do you use to minimize downtime during a deploy

Medium · Systems & Architecture

Describe your experience with cloud migration. What strategies do you use to minimize downtime during a deployment?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Describe a situation where you had to work with a difficult teammate or stakeholder. How did you manage the re

Medium · Behavioral & Collaboration

Describe a situation where you had to work with a difficult teammate or stakeholder. How did you manage the relationship to ensure the project succeeded?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Tell me about a time when you had to make a technical decision without complete information. What was your app

Medium · Behavioral & Collaboration

Tell me about a time when you had to make a technical decision without complete information. What was your approach, and what was the outcome?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

How do you handle constructive criticism or dismissive feedback on your code or system design proposals?

Medium · Behavioral & Collaboration

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Describe a time when a project you were working on faced unexpected delays. How did you communicate this to le

Medium · Behavioral & Collaboration

Describe a time when a project you were working on faced unexpected delays. How did you communicate this to leadership, and how did you adjust your plan?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?