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Microsoft Data Scientist Interview Guide 2026

This guide covers Microsoft’s Data Scientist interview process in 2026, detailing recruiter and technical screens, final interview loops, expected......

Topics: Microsoft, Data Scientist, interview guide, interview preparation, Microsoft interview

Author: PracHub

Published: 3/21/2026

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Microsoft Data Scientist Interview Guide 2026

This guide covers Microsoft’s Data Scientist interview process in 2026, detailing recruiter and technical screens, final interview loops, expected......

6 min readUpdated Jul 1, 202644+ practice questions
44+
Practice Questions
2
Rounds
7
Categories
6 min
Read
Contents
TL;DRSample QuestionsAbout the Interview ProcessWhat to expectInterview roundsRecruiter / HR screenHiring manager screenTechnical phone screenFinal loop: behavioral / competencies roundFinal loop: SQL / data manipulation roundFinal loop: statistics / experimentation roundFinal loop: machine learning / modeling roundFinal loop: product / business analytics or case roundFinal loop: system design / applied ML design roundWhat they testHow to stand outHow to Use This Page as a Prep PlanFAQWhat matters most in data interviews?How should I practice SQL?How do I handle ambiguous metrics?
Practice Questions
44+ Microsoft questions
Microsoft Data Scientist Interview Guide 2026

TL;DR

Microsoft’s Data Scientist interview process in 2026 is usually a recruiter screen, a hiring manager or technical screen, and then a final loop with 4 to 5 interviews. The distinctive part is the balance. You are not just tested on technical skill, but on how well you define ambiguous problems, connect analysis to product impact, and work across PM, engineering, and research partners. SQL and experimentation come up often, and behavioral performance matters more than many candidates expect. The process is also somewhat team-dependent. AI-heavy or senior roles may add more system design, production ML, or light LLM discussion, while other teams stay focused on analytics, statistics, and product sense. End to end, expect roughly 4 to 8 weeks, with possible delays after the final loop.

Interview Rounds
OnsiteTechnical Screen
Key Topics
Machine LearningBehavioral & LeadershipCoding & AlgorithmsAnalytics & ExperimentationStatistics & Math
Practice Bank

44+ questions

Estimated Timeline

1–2 weeks

Browse all Microsoft questions

Sample Questions

44+ in practice bank
Statistics & Math
1

Use confusion matrix to choose model metric

EasyStatistics & MathPremium
View full question
2

Choose Classification Metrics Under Asymmetric Costs

MediumStatistics & Math

You are evaluating a binary classification model for a business problem.

Explain how to use a confusion matrix to compute and interpret:

  • precision,
  • recall (sensitivity),
  • specificity,
  • false positive rate,
  • false negative rate,
  • ROC-AUC,
  • PR-AUC.

Also answer the following:

  1. What is the difference between Type I and Type II error?
  2. How does your metric choice change across scenarios such as spam filtering, medical screening, fraud detection, and ad click prediction?
  3. Why can accuracy be misleading under class imbalance?
  4. How do decision thresholds, prevalence drift, and probability calibration affect model evaluation?
  5. If false positives and false negatives have different business costs, how would you choose an operating threshold?
View full question
Data Manipulation (SQL/Python)
3

Query departments and top earners

EasyData Manipulation (SQL/Python)CodingPremium
View full question
4

Find common friends from directed edges

MediumData Manipulation (SQL/Python)

You have a directed edge list that records who followed whom. A mutual “friendship” exists only if both directions appear (A→B and B→A). Schema and sample data:

Schema: FriendEdges(id INT PRIMARY KEY, user_from VARCHAR(10), user_to VARCHAR(10))

Sample rows: id | user_from | user_to 1 | A | B 2 | B | A 3 | A | C 4 | C | A 5 | A | D 6 | D | A 7 | B | C 8 | C | B

Tasks:

  1. Write a single SQL query that derives an undirected friendship table Friends(u1, u2) containing one row per friendship with u1 < u2, based on reciprocal edges in FriendEdges.
  2. Using only FriendEdges (no temporary tables), write a single SQL query that returns, for every unordered pair of distinct users (x, y) with x < y, all of their common friends f (users who are friends with both x and y). Output columns: user1, user2, common_friend. Exclude the pair themselves from being counted as their own friend.
  3. Extend (2) to also return, per pair (x, y), the count of distinct common friends. Ensure no duplicates even if multiple reciprocal edges are present.
  4. Explain the indexes you would add on FriendEdges to make (2) performant on 100M rows, and why.
View full question
Machine Learning
5

Explain KNN and how to tune it

EasyMachine Learning

K-Nearest Neighbors (KNN) fundamentals

You are interviewing for a Data Scientist role.

  1. Explain how the KNN algorithm works for both classification and regression.
  2. What are the key hyperparameters and design choices?
    • Choice of K
    • Distance metric (e.g., Euclidean, Manhattan, cosine)
    • Weighting (uniform vs distance-weighted neighbors)
  3. What data preprocessing is important for KNN and why? (e.g., feature scaling, handling missing values, categorical encoding)
  4. Discuss the main strengths, weaknesses, and failure modes of KNN.
    • Consider class imbalance, high dimensionality, and large datasets.
  5. How would you select K and evaluate the model? Include at least one approach for avoiding overfitting.

Optionally: Explain how dimensionality reduction (e.g., PCA) could help KNN and when it might hurt.

View full question
6

Compare CNN/RNN/LSTM and implement K-means

HardMachine Learning

Deep Learning Concepts and K-means Implementation (Onsite ML Interview)

This is a two-part onsite round for a Data Scientist role: a conceptual deep-learning comparison (Part A) followed by a from-scratch coding exercise (Part B). Part A probes whether you understand why different architectures fit different modalities — not just definitions — and asks you to be precise about LSTM math and tensor shapes. Part B asks you to implement and reason about K-means rigorously, including a short proof and complexity analysis.

Constraints & Assumptions

  • Part A, LSTM shapes: assume a single-layer LSTM, input tensor with batch=32, seq_len=100, feat=64, and hidden size H=128. State the layout convention you use (batch-first vs time-first).
  • Part B: input is a dense numeric matrix $X \in \mathbb{R}^{n \times d}$; you may use NumPy but not a clustering library (no sklearn.cluster). Distances are squared Euclidean.
  • You should be able to discuss, but are not required to fully implement, $k$-selection and the mini-batch variant in code.

Clarifying Questions to Ask

  • For Part A, should the comparison assume a fixed task per modality (e.g. image classification vs. text classification), or stay architecture-general?
  • Is a Transformer in scope as a third point of comparison for text, or should the focus stay on CNN vs RNN/LSTM?
  • For Part B, do you want a numerically stable distance computation, or is the naive broadcast acceptable for the expected data size?
  • Should K-means handle replicates (multiple random restarts) and return the best run, or is a single run sufficient?
  • Any constraints on memory (e.g. can the full $n \times k$ distance matrix be materialized)?

Part A: CNNs vs RNNs and LSTMs

Contrast CNNs and RNNs across two modalities — (i) $224\times224$ RGB images and (ii) variable-length text — and then dig into LSTM internals.

For the modality comparison, address: inductive biases (translation equivariance, spatial/temporal locality, temporal order), parameter sharing, receptive-field growth with depth, and ability to model long-range dependencies. Then state precisely when a 1D CNN can replace an RNN/Transformer for sequences, including assumptions and caveats.

For LSTMs specifically: (a) write the gate equations and define every symbol and its shape; (b) show mathematically how the cell state mitigates vanishing gradients; (c) compute the output shapes for the given input under both unidirectional and bidirectional single-layer settings.

Anchor each architecture to the structure of its data. Images have a 2D grid with *spatial stationarity* (a cat is a cat anywhere in the frame) → weight-shared local kernels. Text has *order* and variable length → a recurrence that carries state, or a 1D conv over the token axis.

For a stack of $L$ conv layers with kernel $k$, stride 1, no dilation, the 1D receptive field is $R = 1 + L(k-1)$. With exponentially increasing dilations $1,2,4,\dots$ it grows to roughly $R \approx 1 + (k-1)(2^L - 1)$ — this is the lever that lets a CNN reach long context with shallow depth.

Look at $\partial c_t / \partial c_{t-1}$. The additive cell update $c_t = f_t \odot c_{t-1} + i_t \odot g_t$ makes this Jacobian (approximately) the forget gate $f_t$, so the gradient chain across time is a *product of forget gates* rather than a product of weight matrices and saturating nonlinearities.

A bidirectional layer runs two independent recurrences and concatenates their per-step outputs, so the feature dimension of the sequence output doubles while `seq_len` and `batch` stay fixed.

What This Part Should Cover

  • Correct mapping of each modality's structure to the matching inductive bias (2D locality/translation equivariance for images; order + variable length for text), and why serializing an image for an RNN is a p
View full question
Coding & Algorithms
7

Traverse an Org Chart by Level

MediumCoding & AlgorithmsCoding

You are given an organization's reporting structure as a flat list of employee-manager relationships. Exactly one employee is the root (the CEO) and has no manager.

Example input schema:

  • employee_id: int
  • employee_name: string
  • manager_id: int | null

Task:

  1. Convert the flat reporting structure into a tree.
  2. Return the org chart from top to bottom, one level at a time.
  3. Each level should be shown in full before moving to the next level.

Example output format:

  • [[CEO], [VP1, VP2], [Mgr1, Mgr2, Mgr3], ...]

Discuss:

  • your data structures,
  • time and space complexity,
  • and how you would handle invalid input such as cycles, missing managers, or multiple roots.
View full question
8

Write an average-income function

EasyCoding & AlgorithmsCodingPremium
View full question
Analytics & Experimentation
9

Design and analyze email deliverability experiment

HardAnalytics & ExperimentationPremium
View full question
10

Design evaluation when A/B test is impossible

EasyAnalytics & ExperimentationPremium
View full question
Behavioral & Leadership
11

Handle unreliable email during time-critical coordination

HardBehavioral & Leadership

Scheduling Under Email Deliverability Risk: 72-Hour Action Plan and Escalation

Scenario

You must schedule a critical Data Scientist technical screen within five business days. A senior stakeholder intermittently claims not to receive your emails. You do not have IT/admin privileges and HR has not engaged yet.

Assume today is Wednesday, Oct 29, 2025, and the interview must be held by Wednesday, Nov 5, 2025.

Task

Describe, step by step:

  1. A 72-hour plan to restore reliable communication and secure a confirmed meeting time, including at least two redundant channels (e.g., alternate sender address via Outlook/colleague, calendar holds with RSVP requests, LinkedIn InMail, a third-party scheduler) and how you time your follow-ups.
  2. How you would verify end-to-end deliverability without read receipts, including seeded test inboxes, unique tracking tokens in the subject/body, and prearranged acknowledgments.
  3. The escalation path and a decision matrix for when to switch providers/channels, involve HR, or move to a live call, including guardrails for professionalism and compliance.
  4. Exact subject lines and header tweaks you would try to reduce spam risk, plus a cadence that avoids rate limiting.
  5. How you will document attempts, define SLAs, assign owners, and keep the candidate experience intact.
  6. If, on the Friday before the planned interview, you learn the position is filled, how you salvage the relationship and convert the interaction into future opportunities.

Specify concrete success metrics and dates.

View full question
12

Describe leading an ambiguous ML project end-to-end

MediumBehavioral & Leadership

Behavioral & Leadership: End-to-End ML Project Under Ambiguity (STAR)

Provide a STAR-format example where you led an end-to-end ML project with ambiguous requirements. Be concrete and quantitative.

Include the following:

  1. Scope
    • How you converted vague requirements into a clear problem statement and success metrics (e.g., target AUC, latency, cost).
  2. Technical Leadership
    • Model(s) you evaluated/selected and why.
    • Explicit trade-offs across accuracy, latency, interpretability, and cost; include thresholds/targets.
  3. Stakeholders
    • How you aligned PM/engineering/legal on risks (bias, privacy), handled disagreements, and set decision checkpoints.
    • Include one example of "disagree-and-commit."
  4. Execution
    • How you de-risked (offline evaluation → shadow or A/B testing), defined rollback criteria, and monitored for drift.
    • What dashboards/alerts you set up.
  5. Impact & Reflection
    • Quantified business impact.
    • What you would do differently (e.g., experimentation plan, metric design, documentation).
View full question
ML System Design
13

Design Product-Description Content Moderation at Marketplace Scale

MediumML System Design

Prompt

Design a system that detects and handles inappropriate text in seller-uploaded product descriptions for a marketplace containing billions of product listings. Your design should cover the machine-learning approach and the production system around it, from upload-time decisions through human review, appeals, monitoring, and model improvement.

Do not assume that “inappropriate” is one universal binary label. Explain how policy categories, severity, confidence, language, and context affect the action taken.

Constraints & Assumptions

  • New and edited descriptions should receive an upload-time decision quickly enough not to make listing creation unusable.
  • The corpus is multilingual and includes slang, obfuscation, quoted text, and legitimate terms that can be ambiguous out of context.
  • Some violations are severe enough to block immediately; uncertain or lower-severity cases may be queued for review or checked asynchronously.
  • A seller must receive a reason category and have an appeal path where policy permits it.
  • Models, policy rules, and thresholds change over time, so every decision must be reproducible and auditable.
  • Images and other listing fields may exist, but the required scope is the product-description text. Treat richer context as an optional extension.

Clarifying Questions to Ask

  • Which policy categories and jurisdictions are in scope, and what action corresponds to each category?
  • What are the tolerable false-positive and false-negative costs by severity?
  • What upload latency, peak traffic, availability, and review-capacity targets apply?
  • Is existing labeled moderation and appeal data available, and how biased is it toward previously reviewed traffic?
  • Should existing listings be rescanned when policy or models change?
  • What seller context may legally and ethically be used?

Part 1: Frame the ML Problem

Define labels, training data, model families, metrics, and thresholding. Explain how you would handle multilingual text, class imbalance, ambiguous context, adversarial spelling, and changing policy.

Hints

Optimize decisions, not one global model score. A severe category with costly misses may need a different threshold and review path from a contextual category with costly false blocks.

What This Part Should Cover

  • Multi-label or hierarchical policy taxonomy
  • Label provenance, reviewer agreement, and leakage controls
  • Per-category evaluation rather than aggregate accuracy
  • Calibration and action-specific thresholds
  • Slice analysis across language and seller cohorts

Part 2: Design the Online and Offline System

Describe the upload request path, asynchronous processing, feature and model versioning, human-review queues, appeals, rescans, and failure behavior. Explain how the design scales to billions of stored descriptions without scanning all of them on every change.

Hints

Separate the low-latency decision path from expensive enrichment and historical rescans. Store enough decision metadata to explain and replay outcomes.

What This Part Should Cover

  • Fast deterministic checks plus model inference
  • Confidence/severity-based routing to allow, block, review, or limited publication
  • Idempotent events and versioned decision records
  • Prioritized, checkpointed backfills for existing listings
  • Safe degradation when a dependency or model is unavailable

Part 3: Evaluate and Operate the System

Explain offline evaluation, a safe launch, online monitoring, feedback loops, abuse resistance, and rollback. Address the fact that appealed and reviewed content is not a random sample of all uploads.

Hints

Observed reviewer outcomes are selected by the current system. Include random sampling or another strategy that can estimate blind spots.

What This Part Should Cover

  • Precision/recall and workload at candidate thresholds
  • Shadow evaluation and staged rollout
  • Random audits plus targeted review
  • Drift, latency, availability, queue
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About the Interview Process

What to expect

Microsoft’s Data Scientist interview process in 2026 is usually a recruiter screen, a hiring manager or technical screen, and then a final loop with 4 to 5 interviews. The distinctive part is the balance. You are not just tested on technical skill, but on how well you define ambiguous problems, connect analysis to product impact, and work across PM, engineering, and research partners. SQL and experimentation come up often, and behavioral performance matters more than many candidates expect.

The process is also somewhat team-dependent. AI-heavy or senior roles may add more system design, production ML, or light LLM discussion, while other teams stay focused on analytics, statistics, and product sense. End to end, expect roughly 4 to 8 weeks, with possible delays after the final loop.

Microsoft Data Scientist Interview Guide 2026 visual study map Visual study map Screen resume, SQL basics Core skills SQL, stats, product sense Onsite case, metrics, experiments Decision impact and communication Use this map to decide what to practice first, then check each area against the examples in the guide.

Video companion: This verified YouTube video gives a second pass on the same prep area.

Interview rounds

Recruiter / HR screen

This round is usually a 30-minute conversation over phone or Teams. Expect questions about your background, why Microsoft, why the team, your current scope and impact, and logistical topics like location, visa, or compensation. The recruiter is checking role fit, communication, level alignment, and whether your experience matches the team’s needs.

Hiring manager screen

This round usually lasts 30 to 45 minutes and is commonly done over Teams or phone. It often focuses on one or two projects, how you framed the problem, how you measured impact, and how you worked with stakeholders. Some teams also add light SQL, Python, product analytics, or experimentation questions. The goal is to see whether you clear the initial technical and business bar for the full loop.

Technical phone screen

When included as a separate round, this is typically 45 to 60 minutes with a live editor or shared document. You may be asked to write SQL, code in Python or R, manipulate data, or work through statistics and A/B testing questions while explaining your reasoning. Interviewers are evaluating hands-on technical execution, structure, and your ability to discuss tradeoffs as you solve.

Final loop: behavioral / competencies round

This interview usually runs 45 to 60 minutes in a one-on-one format. It is built around Microsoft competencies such as adaptability, collaboration, customer focus, drive for results, influencing for impact, and judgment. Expect detailed behavioral prompts about ambiguity, conflict, influence without authority, learning from failure, and delivering results under uncertainty.

Final loop: SQL / data manipulation round

This round is usually 45 to 60 minutes and is often a live coding session or shared-editor exercise. Microsoft uses it to assess whether you can work with realistic data structures, write correct and efficient queries, and reason through messy relational problems. Expect joins, CTEs, window functions, aggregations, funnel or retention analysis, and possibly data cleanup or table-structure discussion.

Final loop: statistics / experimentation round

This round usually takes 45 to 60 minutes and is often case-based rather than purely computational. You will likely be asked to design experiments, choose primary and guardrail metrics, interpret results, and explain statistical pitfalls like confounding or bad randomization. The emphasis is on statistical rigor and whether you can turn a product question into a credible measurement plan.

Final loop: machine learning / modeling round

This interview is generally 45 to 60 minutes and mixes conceptual discussion with applied modeling scenarios, sometimes including coding. Be ready to explain model choice, overfitting, regularization, feature engineering, evaluation metrics, and tradeoffs between approaches. For some teams, especially AI-related ones, you may also need to briefly discuss LLMs or production considerations.

Final loop: product / business analytics or case round

This round is usually 45 to 60 minutes and centers on open-ended product thinking. You may be asked how to evaluate a feature, diagnose a drop in engagement, prioritize metrics, or make a recommendation from incomplete behavioral data. Interviewers want to see whether you can define the right problem before jumping into analysis.

Final loop: system design / applied ML design round

This round is more common for senior, staff, principal, or ML-heavy data science roles and typically lasts 45 to 60 minutes. It focuses on end-to-end system thinking: productionizing models, monitoring, retraining, feature pipelines, and latency-versus-accuracy tradeoffs. In AI-focused teams, the discussion may extend to RAG or LLM system design.

What they test

Microsoft repeatedly tests a core group of technical skills: SQL, coding, statistics, experimentation, machine learning, and product analytics. SQL is a major part of the process rather than a minor screen, so you should be comfortable with joins, self-joins, CTEs, subqueries, window functions, aggregations, retention analysis, funnel analysis, and working with messy relational data. In Python or R, the bar is usually practical rather than algorithm-heavy: data manipulation, writing clean functions, and reasoning through table- or event-based problems.

Statistics and experimentation are especially important. Expect probability, distributions, sampling, confidence intervals, p-values, hypothesis testing, and regression basics. You also need the applied side: choosing metrics, setting guardrails, planning A/B tests, thinking about power and sample size, and identifying bias or confounding. In machine learning, the focus is usually on practical fundamentals such as regression, classification, tree-based methods, regularization, overfitting, bias-variance tradeoffs, feature engineering, evaluation, and handling imbalanced data. For senior roles, Microsoft also looks for production judgment, architecture thinking, and the ability to connect modeling decisions to deployment and monitoring.

What stands out most is Microsoft’s emphasis on problem definition. Interviewers often care less about whether you jump quickly to a model and more about whether you clarify the goal, define success, choose the right metrics, and explain the business or product consequences of your recommendation. Strong candidates show that they can move from ambiguity to a measurable plan, then communicate tradeoffs clearly to non-technical partners.

How to stand out

  • Prepare 4 to 5 strong stories that map directly to Microsoft’s competency themes: ambiguity, collaboration, customer focus, influencing without authority, failure and learning, and delivering results.
  • Treat SQL as a primary area of study, not a side topic. Be ready to solve medium-to-hard query problems involving window functions, CTEs, joins, and event-data analysis while narrating your logic.
  • Start open-ended questions by clarifying the objective, assumptions, constraints, and success metric. At Microsoft, defining the right problem is often a key differentiator.
  • In experimentation questions, explicitly name a primary metric, guardrail metrics, likely sources of bias, and how you would validate that the test result is trustworthy.
  • Tie technical work back to product and business impact. When you describe a project or propose an analysis, explain what decision it enabled and what changed because of it.
  • Keep behavioral answers concise at first. A tight 60- to 90-second structure works better than a long monologue, and it leaves room for the interviewer to probe the parts they care about.
  • If you are interviewing for a senior or AI-focused team, go beyond model selection and show production judgment: deployment constraints, monitoring, retraining, failure modes, and tradeoffs between accuracy, latency, and maintainability.

How to Use This Page as a Prep Plan

Do not treat this as passive reading. Convert the ideas in this page into a short weekly loop: learn one idea, practice it under interview conditions, then write down what changed. That is the fastest way to turn advice into visible interview behavior.

Prep areaWhat you need to provePractice artifact
Metric framingDefine the unit, window, and denominator.One clear metric contract.
SQL executionUse readable CTEs and test row counts.A query with checks after each join.
StatisticsConnect methods to decision risk.Assumptions, confidence, and caveats.
CommunicationTurn findings into a recommendation.One concise business interpretation.

For Microsoft Data Scientist Interview Guide 2026, the strongest candidates usually do three things well: they make their assumptions explicit, they use concrete examples instead of vague claims, and they review mistakes quickly enough that the next practice rep is better than the last one.

FAQ

What matters most in data interviews?

Clear assumptions, correct query structure, and the ability to explain what the result means.

How should I practice SQL?

Practice with messy business prompts, then write checks for joins, nulls, duplicates, and time windows.

How do I handle ambiguous metrics?

State a default definition, explain the tradeoff, and ask whether the interviewer wants a different lens.

Frequently Asked Questions

I’d call it moderately hard to hard, mostly because they test range, not just one skill. You need solid stats and machine learning basics, but also product sense, experimentation, SQL, coding, and how you communicate with non-technical partners. The questions usually are not impossible on their own, but the pressure comes from switching contexts fast and explaining your thinking clearly. If your background is only research-heavy or only analytics-heavy, you’ll probably feel the gaps more than someone who has done both modeling and business-facing work.

The exact loop can vary by team, but the usual path starts with a recruiter screen, then a hiring manager or technical phone screen, and then a virtual or onsite interview loop. In the loop, I’d expect a mix of coding, SQL, statistics, machine learning, experiment design, product or business case questions, and behavioral interviews. Some teams lean more into modeling, while others care more about analytics and decision-making. You may also get resume deep dives where they press on past projects, tradeoffs, impact, and what you personally owned.

For most people, 4 to 8 weeks of focused prep is enough if you already use data science at work. If you’re rusty on coding or statistics, give yourself closer to 8 to 12 weeks. What helped me most was splitting prep into tracks: SQL and Python practice, stats and probability review, machine learning concepts, A/B testing, and mock interviews. I’d also spend time on storytelling for past projects, because Microsoft interviewers often want clear, practical communication, not just correct answers. Consistency matters more than giant weekend cram sessions.

The biggest ones are statistics, probability, hypothesis testing, experiment design, regression, classification, feature thinking, model evaluation, SQL, and Python. You should be able to explain bias, variance, overfitting, data leakage, and how you would choose metrics for a business problem. Product sense matters more than some candidates expect, especially framing ambiguous questions and turning them into measurable goals. I’d also be ready for dashboard or stakeholder-style thinking: what to measure, how to interpret noisy results, and what recommendation you’d make if the data is incomplete.

The biggest mistake is answering like a textbook instead of like someone solving a real business problem. I’ve seen people jump into fancy models before defining the goal, metric, or data quality issues. Another common miss is weak communication: not stating assumptions, not checking edge cases, or giving scattered answers. On coding and SQL, sloppy syntax is fine if your logic is strong, but messy structure and no testing hurts. In behavioral rounds, generic stories fall flat. They want ownership, tradeoffs, mistakes you learned from, and evidence that you work well with partners.

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