Dentsu International Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Dentsu International, you sit at the intersection of cutting-edge technology and global marketing transformation. You are not just writing code; you are building the digital infrastructure that enables some of the world’s most recognizable brands to engage with their audiences at scale. Your work directly influences how data-driven insights are converted into personalized consumer experiences. The role demands a blend of technical precision and strategic adaptability. Because Dentsu International operates in a fast-paced, client-facing environment, you will often find yourself working on complex, high-visibility projects that require both robust backend architecture and intuitive frontend implementation.

This guide is scoped to a Software Engineer candidate at Dentsu International.

Focus areas

• React
• React Hooks
• useEffect

The Dentsu International interview process

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

Screening Call

Initial call focused on your background and alignment with the role.

What to demonstrate

• Initial call focused on your background and alignment with the role
• Depth in React

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 Assessments

One or more technical evaluations that may vary in format and rigor.

What to demonstrate

• One or more technical evaluations that may vary in format and rigor
• Depth in React

How to prepare

• Answer aloud and timed: Why does React re-render, and what strategies do you use to prevent unnecessary re-renders?
• Answer aloud and timed: How does the Virtual DOM function, and how does React leverage it for performance?

Final Interviews

Interviews with team members or leadership to assess fit and capabilities.

What to demonstrate

• Interviews with team members or leadership to assess fit and capabilities
• Depth in React

How to prepare

• Answer aloud and timed: What are the core differences between various Node.js event loop phases?
• Answer aloud and timed: How would you design a scalable system using AWS services?

Where candidates lose points

Prioritize Communication

If you are stuck on a coding problem, talk through your thought process. Interviewers are often looking for how you approach a problem more than if you reach the perfect solution immediately.

Master Your Resume

Expect deep dives into your previous projects. Be prepared to explain the technical decisions you made and the impact those decisions had on the project’s success.

Be Ready for Aptitude

In certain regions, Dentsu International uses aptitude tests (speed, distance, verbal) as a filter. Brush up on these skills early in the process.

Clean Code Matters

Even in a whiteboard setting, prioritize writing readable, modular code. Demonstrate that you care about code quality.

Questions & practice

How do you optimize for time and space complexity when solving algorithmic challenges?

Medium · System Design & Architecture

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?

Collapse a redelivered event batch into per-aggregate high-water marks

Easy · hashing · at-least-once · aggregation

You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.

Approach

1. One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
2. Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
3. Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
4. If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.

Follow-up

• The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
• How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?

Track a rolling failure rate per destination for circuit decisions

Easy · sliding window · ring buffer · circuit breaker

The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.

Approach

1. Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
2. Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
3. State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
4. Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.

Follow-up

• The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
• A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?

Explain the logic behind choosing a specific database schema for a high-traffic application.

Medium · System Design & Architecture

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. Handle the rows that do not match: that is usually the actual question.

Follow-up

• How does the query change if that join becomes one-to-many?
• What happens to this when the table is ten times larger?

What criteria do you use to select between SQL and NoSQL databases for a new project?

Medium · System Design & Architecture

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. Handle the rows that do not match: that is usually the actual question.

Follow-up

• How does the query change if that join becomes one-to-many?
• What happens to this when the table is ten times larger?

What is the difference between props and state in React.js?

Medium · Technical & Framework Proficiency

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

Can you explain React hooks, specifically useEffect and useMemo, with practical examples?

Medium · Technical & Framework Proficiency

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

Why does React re-render, and what strategies do you use to prevent unnecessary re-renders?

Medium · Technical & Framework Proficiency

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

How does the Virtual DOM function, and how does React leverage it for performance?

Medium · Technical & Framework Proficiency

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

What are the core differences between various Node.js event loop phases?

Medium · Technical & Framework Proficiency

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

How would you design a scalable system using AWS services?

Medium · System Design & Architecture

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 customer endpoint stalls deliveries to every other destination

Medium · bulkhead · timeouts · saturation

The egress service delivers about 1.5k webhooks/second across 40,000 destinations, with a per-destination concurrency cap of 4 and a 10-second connect-plus-read timeout. Throughput falls to 300/second, queue depth climbs, and p99 delivery latency for unaffected destinations goes from 200 ms to minutes, while the error rate barely moves. One tenant holds 900 destination rows whose URLs share a hostname that now answers in 9.5 seconds. Explain the mechanism with the arithmetic, then give the containment in the order you would apply it.

Approach

1. Look at saturation before errors. A flat error rate with collapsing throughput says nothing is failing, things are waiting, so the first signal to pull is in-flight request count or pool wait time rather than the error counter. This is the distinction that decides the whole investigation.
2. Group in-flight work by resolved host, not by destination id. The cap is keyed per destination row, so 900 rows sharing one hostname buy 3,600 concurrent slots against a single host, each held for 9.5 seconds. The bulkhead was never a bulkhead for that host, and grouping by the wrong dimension is why the dashboard looked healthy.
3. Do the arithmetic in both directions. Required concurrency is arrival rate times latency, so 1.5k/second at 200 ms needs about 300 in flight, which is entirely consumed by 3,600 slow slots; conversely whatever concurrency is left sustains rate equals concurrency divided by 9.5 seconds, which is the 300/second you are seeing. Matching both numbers is what promotes this from a plausible story to the mechanism.
4. Explain why the circuit breaker never helped. It opens on consecutive failures, and a 9.5-second response inside a 10-second timeout is a success. Slow is not failing, so an error-rate breaker cannot see this; you need a slow-call ratio, a deadline propagated from the caller's remaining budget, or a concurrency limiter.

Follow-up

• The host recovers to 80 ms. How long does the queue take to drain, and what does the drain do to the recovered host?
• Where should the 10-second timeout number actually come from?

Suggested practice checklist

Built from the rounds and topics Dentsu International candidates report.

Day 1 — Map the Dentsu International loop

• Write out the reported sequence: Screening Call, Technical Assessments, Final Interviews.
• 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 React

• Spend the session on React, which Dentsu International candidates report being tested on.
• Write one worked example in React and time yourself on it.

Deliverable: One timed worked example in React.

Day 3 — Work React Hooks

• Spend the session on React Hooks, which Dentsu International candidates report being tested on.
• Write one worked example in React Hooks and time yourself on it.

Deliverable: One timed worked example in React Hooks.

Day 4 — Work useEffect

• Spend the session on useEffect, which Dentsu International candidates report being tested on.
• Write one worked example in useEffect and time yourself on it.

Deliverable: One timed worked example in useEffect.

Day 5 — Answer out loud: Technical & Framework Proficiency

• Answer aloud, timed: What is the difference between props and state in React.js?
• Answer aloud, timed: Can you explain React hooks, specifically useEffect and useMemo, with practical examples?

Deliverable: Spoken answers to 2 reported Technical & Framework Proficiency question(s), under time.

Day 6 — Answer out loud: System Design & Architecture

• Answer aloud, timed: How would you design a scalable system using AWS services?
• Answer aloud, timed: Describe your experience with CI/CD pipelines and how you ensure deployment reliability.

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

Day 7 — Answer out loud: Behavioral & Problem Solving

• Answer aloud, timed: Tell me about a time you faced a difficult technical challenge and how you resolved it.
• Answer aloud, timed: How do you handle situations where your technical approach differs from your manager's or team's?

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

Behavioural preparation

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

Describe your experience with CI/CD pipelines and how you ensure deployment reliability.

Medium · System Design & Architecture

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 you faced a difficult technical challenge and how you resolved it.

Medium · Behavioral & Problem Solving

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 situations where your technical approach differs from your manager's or team's?

Medium · Behavioral & Problem Solving

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 project where you had to learn a new technology under a tight deadline.

Medium · Behavioral & Problem Solving

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 ensure your code remains clean and maintainable for other team members?

Medium · Behavioral & Problem Solving

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?

Can you walk me through a project from your resume and explain the most complex technical hurdle you overcame?

Medium · Behavioral & Problem Solving

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?