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Ntt Data Europe & Latam Software Engineer Interview Guide

Ntt Data Europe & Latam Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

A Software Engineer at Ntt Data Europe & Latam plays a pivotal role in driving digital transformation and building scalable, enterprise-grade technology solutions for global clients. As one of the world's leading IT services and consulting providers, Ntt Data Europe & Latam bridges complex enterprise requirements with modern engineering solutions. Engineers work directly on high-impact initiatives spanning cloud transformations, full-stack application development, modern microservice architectures, and data engineering pipelines across sectors like banking, telecom, healthcare, and retail. In this role, your work directly influences the speed, security, and stability of critical software environments.

This guide is scoped to a Software Engineer candidate at Ntt Data Europe & Latam.

Focus areas

  • Java
  • Version Control (Git)
  • .NET (Full-stack.NET)

The Ntt Data Europe & Latam interview process

Ntt Data Europe & Latam candidates report 5 rounds over 4-6 weeks. The stages below are what candidates describe, not a published process.

HR Screen

Initial screening to verify core skills, experience level, language fluency, and compensation alignment.

What to demonstrate

  • Initial screening to verify core skills, experience level, language fluency, and compensation alignment
  • Depth in Java

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.

Assessment Day

Candidates may participate in group discussions, collaborative problem-solving scenarios, or online assessments.

What to demonstrate

  • Candidates may participate in group discussions, collaborative problem-solving scenarios, or online assessments
  • Depth in Java

How to prepare

  • Answer aloud and timed: How do multithreading and asynchronous execution work in backend systems, and how do you handle concurrency issues like race conditions?
  • Answer aloud and timed: What are custom annotations and functional programming approaches in frameworks like Spring Core or.NET Core, and when should you use them?

Technical Evaluation

One or two rounds of deep discussions, live coding, system design exercises, and scenario-based technical questions.

What to demonstrate

  • One or two rounds of deep discussions, live coding, system design exercises, and scenario-based technical questions
  • Depth in Java

How to prepare

  • Answer aloud and timed: How does memory management, garbage collection, or event loop processing function in runtime environments?
  • Answer aloud and timed: What are the core trade-offs between monolithic and microservice architectures, and how do you evolve a legacy application toward microservices?

Managerial Round

Final discussions focused on culture fit, project placement, and contractual terms.

What to demonstrate

  • Final discussions focused on culture fit, project placement, and contractual terms
  • Depth in Java

How to prepare

  • Answer aloud and timed: Explain hexagonal architecture, domain-driven design (DDD), and how you maintain layer separation within complex projects.
  • Answer aloud and timed: How do you implement secure and scalable communication between microservices using REST APIs, gRPC, or message brokers?

Offer Negotiation

Discussion of offer details, including culture fit and project placement.

What to demonstrate

  • Discussion of offer details
  • Including culture fit and project placement

How to prepare

  • Answer aloud and timed: Describe strategies for managing distributed transactions, custom payload validations, and error handling across multiple downstream services.
  • Answer aloud and timed: How do you structure database storage, blob storage integration, and persistent caching strategies for high-throughput enterprise systems?

Where candidates lose points

When answering architectural or design questions, avoid stating a single absolute solution

Instead, explain why you chose a specific technology or pattern, mentioning the trade-offs regarding performance, complexity, and maintainability.

Pay close attention to the details on your CV

Interviewers frequently anchor their questions directly on the tools, versions, and frameworks listed on your resume.

A queue or buffer with no bound

Every producer-consumer boundary needs a capacity and a policy for reaching it: block the producer, shed load, or drop the oldest entry. Unbounded buffering converts a temporary slowdown into memory exhaustion and hides the backpressure signal that would have revealed the consumer was falling behind.

Questions & practice

What are the key differences across recent language release versions (e.g., Java 8 through modern LTS releases

Medium · Backend Core & Language Fundamentals

What are the key differences across recent language release versions (e.g., Java 8 through modern LTS releases), and how do feature additions like Streams and Records impact performance?

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

Explain the core concepts of Object-Oriented Programming (OOP) including inheritance, polymorphism, abstractio

Medium · Backend Core & Language Fundamentals

Explain the core concepts of Object-Oriented Programming (OOP) including inheritance, polymorphism, abstraction, and encapsulation, along with practical execution examples.

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

How do multithreading and asynchronous execution work in backend systems, and how do you handle concurrency is

Medium · Backend Core & Language Fundamentals

How do multithreading and asynchronous execution work in backend systems, and how do you handle concurrency issues like race conditions?

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

What are custom annotations and functional programming approaches in frameworks like Spring Core or.NET Core,

Medium · Backend Core & Language Fundamentals

What are custom annotations and functional programming approaches in frameworks like Spring Core or.NET Core, and when should you use them?

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

How does memory management, garbage collection, or event loop processing function in runtime environments?

Medium · Backend Core & Language Fundamentals

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

Explain reactive state management and asynchronous data handling in modern frameworks using libraries such as

Medium · Web Technologies & Front-End Integration

Explain reactive state management and asynchronous data handling in modern frameworks using libraries such as RxJS in Angular or Hooks in React.

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

  1. Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
  2. Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
  3. Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
  4. Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.

Follow-up

  • The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
  • Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?

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?

What are the core trade-offs between monolithic and microservice architectures, and how do you evolve a legacy

Medium · Architecture & Microservices Design

What are the core trade-offs between monolithic and microservice architectures, and how do you evolve a legacy application toward 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?

Explain hexagonal architecture, domain-driven design (DDD), and how you maintain layer separation within compl

Medium · Architecture & Microservices Design

Explain hexagonal architecture, domain-driven design (DDD), and how you maintain layer separation within complex projects.

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 implement secure and scalable communication between microservices using REST APIs, gRPC, or message

Medium · Architecture & Microservices Design

How do you implement secure and scalable communication between microservices using REST APIs, gRPC, or message brokers?

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?

Describe strategies for managing distributed transactions, custom payload validations, and error handling acro

Medium · Architecture & Microservices Design

Describe strategies for managing distributed transactions, custom payload validations, and error handling across multiple downstream services.

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 structure database storage, blob storage integration, and persistent caching strategies for high-th

Medium · Architecture & Microservices Design

How do you structure database storage, blob storage integration, and persistent caching strategies for high-throughput enterprise systems?

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 web browsers process rendering, event loops, and DOM manipulation, and how do you optimize frontend bun

Medium · Web Technologies & Front-End Integration

How do web browsers process rendering, event loops, and DOM manipulation, and how do you optimize frontend bundle performance?

Approach

  1. Say who the caller is and what they do when the call fails halfway.
  2. Define the identity of a request so a retry cannot double-apply it.
  3. Separate accepted, pending, failed and confirmed; they are different facts.
  4. Design the error taxonomy before the success shape; callers branch on it.

Follow-up

  • What happens if the caller retries after a timeout?
  • How does a client discover it is on an old version of this contract?

Describe how frontend applications handle secure authentication state, tokens, and authorization guards with b

Medium · Web Technologies & Front-End Integration

Describe how frontend applications handle secure authentication state, tokens, and authorization guards with backend APIs.

Approach

  1. Say who the caller is and what they do when the call fails halfway.
  2. Define the identity of a request so a retry cannot double-apply it.
  3. Separate accepted, pending, failed and confirmed; they are different facts.
  4. Design the error taxonomy before the success shape; callers branch on it.

Follow-up

  • What happens if the caller retries after a timeout?
  • How does a client discover it is on an old version of this contract?

What are the differences between client-side rendering (CSR) and server-side rendering (SSR) regarding perform

Medium · Web Technologies & Front-End Integration

What are the differences between client-side rendering (CSR) and server-side rendering (SSR) regarding performance and caching?

Approach

  1. Say who the caller is and what they do when the call fails halfway.
  2. Define the identity of a request so a retry cannot double-apply it.
  3. Separate accepted, pending, failed and confirmed; they are different facts.
  4. Design the error taxonomy before the success shape; callers branch on it.

Follow-up

  • What happens if the caller retries after a timeout?
  • How does a client discover it is on an old version of this contract?

How do you structure multi-stage Docker builds to keep deployment artifacts lightweight and secure?

Medium · DevOps, Data Engineering & SQL

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?

Explain basic Kubernetes architecture, pod lifecycle, deployment YAML syntax, and service mesh management.

Medium · DevOps, Data Engineering & SQL

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 optimize complex relational database queries involving joins, indexes, and aggregation logic?

Medium · DevOps, Data Engineering & SQL

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?

Explain Infrastructure as Code (IaC) principles using tools like Terraform and Azure DevOps pipelines.

Medium · DevOps, Data Engineering & SQL

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 do analytical cloud platforms and data warehouse technologies (such as Snowflake or cloud blob storage) ha

Medium · DevOps, Data Engineering & SQL

How do analytical cloud platforms and data warehouse technologies (such as Snowflake or cloud blob storage) handle data extraction and processing?

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?

Read latency spikes on a sixty-second sawtooth

Medium · cache stampede · single-flight · ttl

The cached listing read path serves about 14k reads/second at an 85% hit rate. p99 sits at 35 ms for 57 seconds, jumps to 900 ms for 3, and repeats. During each spike the primary shows several hundred identical listing queries starting within the same millisecond, all carrying one large tenant's id. Cache entries use a 60-second TTL. Give the mechanism, the ordered checks, the fix, and the correctness hazard your fix must not introduce.

Approach

  1. Match the period to a configured number before theorising about load. A spike every 60 seconds against a 60-second TTL is an entry expiring, and you confirm it by correlating spike timestamps with the entry's write time rather than with the traffic curve. If the period had matched a cron or a GC interval instead, this is a different investigation.
  2. Establish the concurrency of the miss. Several hundred identical queries in one millisecond means the miss path has no coalescing: every request that arrives between expiry and repopulation recomputes. The herd size is that key's arrival rate times its recompute time, so at 1.2k reads/second for the hot key and a 250 ms recompute you expect about 300 concurrent misses, which matches what is observed.
  3. Add single-flight on the miss path so one caller per key recomputes under a short-lived lock while the rest wait for its result. Prefer stale-while-revalidate where the read tolerates it: return the expired value immediately and refresh asynchronously, which removes the latency spike rather than serialising it into a queue of waiters.
  4. De-synchronise the keys. Write TTLs with jitter, for example 60 seconds plus or minus 10%, so a deploy or a mass invalidation does not align every key on the same second and turn a per-key herd into a fleet-wide one.

Follow-up

  • The same sawtooth appears on a key that is invalidated on write rather than expired. Is that the same bug?
  • How does your answer change if the recompute takes 4 seconds instead of 250 ms?

Suggested practice checklist

Built from the rounds and topics Ntt Data Europe & Latam candidates report.

Day 1 — Map the Ntt Data Europe & Latam loop

  • Write out the reported sequence: HR Screen, Assessment Day, Technical Evaluation, Managerial Round, Offer Negotiation.
  • 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 5 reported rounds, with the weakest marked.

Day 2 — Work Java

  • Spend the session on Java, which Ntt Data Europe & Latam candidates report being tested on.
  • Write one worked example in Java and time yourself on it.

Deliverable: One timed worked example in Java.

Day 3 — Work Version Control (Git)

  • Spend the session on Version Control (Git), which Ntt Data Europe & Latam candidates report being tested on.
  • Write one worked example in Version Control (Git) and time yourself on it.

Deliverable: One timed worked example in Version Control (Git).

Day 4 — Work .NET (Full-stack.NET)

  • Spend the session on .NET (Full-stack.NET), which Ntt Data Europe & Latam candidates report being tested on.
  • Write one worked example in .NET (Full-stack.NET) and time yourself on it.

Deliverable: One timed worked example in .NET (Full-stack.NET).

Day 5 — Answer out loud: Backend Core & Language Fundamentals

  • Answer aloud, timed: What are the key differences across recent language release versions (e.g., Java 8 through modern LTS releases), and how do feature additions like Streams and Records impact performance?
  • Answer aloud, timed: Explain the core concepts of Object-Oriented Programming (OOP) including inheritance, polymorphism, abstraction, and encapsulation, along with practical execution examples.

Deliverable: Spoken answers to 2 reported Backend Core & Language Fundamentals question(s), under time.

Day 6 — Answer out loud: Architecture & Microservices Design

  • Answer aloud, timed: What are the core trade-offs between monolithic and microservice architectures, and how do you evolve a legacy application toward microservices?
  • Answer aloud, timed: Explain hexagonal architecture, domain-driven design (DDD), and how you maintain layer separation within complex projects.

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

Day 7 — Answer out loud: Web Technologies & Front-End Integration

  • Answer aloud, timed: Explain reactive state management and asynchronous data handling in modern frameworks using libraries such as RxJS in Angular or Hooks in React.
  • Answer aloud, timed: How do web browsers process rendering, event loops, and DOM manipulation, and how do you optimize frontend bundle performance?

Deliverable: Spoken answers to 2 reported Web Technologies & Front-End Integration question(s), under time.

Behavioural preparation

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

Describe a challenging bug or performance issue you encountered in a production environment and the exact step

Medium · Behavioral & Team Dynamics

Describe a challenging bug or performance issue you encountered in a production environment and the exact steps you took to diagnose and resolve it.

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 changing client requirements or ambiguous project specifications mid-sprint?

Medium · Behavioral & Team Dynamics

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 an experience where you had to collaborate closely with non-technical stakeholders or external c

Medium · Behavioral & Team Dynamics

Tell me about an experience where you had to collaborate closely with non-technical stakeholders or external client managers.

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 prioritize technical debt against urgent feature delivery when working against tight project deadli

Medium · Behavioral & Team Dynamics

How do you prioritize technical debt against urgent feature delivery when working against tight project deadlines?

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?