Microsoft Machine Learning Engineer (Senior Level) - Comprehensive Interview Preparation Guide
Microsoft's Machine Learning Engineer interview process for senior-level candidates is a comprehensive, multi-stage evaluation designed to assess technical depth, system design thinking, production experience, and cultural fit. The process typically spans 4-6 weeks and includes an initial recruiter screen, a timed online assessment, a technical phone screen, and 5 onsite interview rounds conducted virtually or in-person. Each round evaluates different competencies: foundational coding skills, core machine learning theory, system-level design thinking, behavioral characteristics, and business acumen. Senior-level candidates are expected to demonstrate expertise in designing scalable ML systems, understanding production constraints, mentoring capabilities, and the ability to balance technical excellence with business value.
Interview Rounds
Recruiter Screening
What to Expect
Initial conversation with a Microsoft recruiter to assess resume fit, motivation for the role, and general alignment with Microsoft's culture. This is typically a 30-minute call where the recruiter will verify your background, discuss your experience with machine learning and software engineering, ask about your familiarity with cloud platforms, and explain the interview process. The recruiter is looking for genuine interest in Microsoft and a realistic understanding of what the role entails. This is also your opportunity to clarify any questions about the position, team structure, and career growth opportunities.
Tips & Advice
Be authentic and specific about why you want to join Microsoft—generic answers about company reputation will not resonate. Reference specific products, research, or projects at Microsoft that excite you (e.g., Azure ML capabilities, research in NLP/computer vision, or Microsoft's approach to responsible AI). Have a clear 2-3 minute narrative about your background, emphasizing progression and impact rather than just titles. Highlight any experience with production ML systems, cloud platforms, or cross-functional teamwork. Prepare thoughtful questions about the team structure, current challenges they're solving, and what success looks like in the first 6 months. Don't oversell—be honest about gaps and your eagerness to learn. If asked about salary, do research on typical senior ML engineer compensation in your region but defer specific discussion until a formal offer stage.
Focus Topics
Experience with Cloud ML Platforms
Any hands-on experience with Azure ML, AWS SageMaker, Google Cloud Vertex AI, or similar platforms. Discuss scaling challenges, deployment workflows, or monitoring in production.
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Cross-functional Collaboration
Examples of working effectively with data scientists, software engineers, product managers, or stakeholders. Emphasize communication and problem-solving.
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Your ML Background and Impact
Concise narrative of your machine learning career progression, key projects, and measurable impact. Focus on end-to-end ownership and complexity.
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Motivation for Microsoft and This Role
Specific reasons for wanting to join Microsoft, the ML Engineer role, and the team (if known). Connect your expertise to Microsoft's AI strategy and products.
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Online Assessment
What to Expect
A 60-minute timed online assessment testing Python proficiency, data structures and algorithms (DSA), and foundational machine learning concepts. Typically administered through an online coding platform (like HackerRank or LeetCode-style environment). This round evaluates your ability to solve problems efficiently, write clean code under time pressure, and apply core ML knowledge. You'll likely face 1-2 coding problems (medium difficulty level) and 10-15 multiple-choice or short-answer questions on ML fundamentals. The coding problems may include array/string manipulation, graph traversal, dynamic programming, or algorithms relevant to data processing. The ML portion tests understanding of supervised/unsupervised learning, model evaluation, regularization, and basic neural network concepts.
Tips & Advice
Time management is critical—allocate roughly 40 minutes for coding (2 problems) and 20 minutes for ML questions. Start with the problem you feel most confident about to build momentum. For coding, focus on correctness first, then optimization; write clean, readable code with comments explaining your approach. Use a language you're very comfortable with (Python is standard for ML roles). For DSA, brush up on common patterns: two-pointer techniques, hash maps for frequency counting, BFS/DFS for graphs, binary search, and dynamic programming. For ML questions, focus on fundamentals: train/test split, overfitting vs. underfitting, precision/recall/F1, cross-validation, regularization, and activation functions. Don't overthink edge cases unless they're ambiguous—clarify assumptions quickly. Submit solutions even if not optimal; partial credit is better than incomplete. Practice on LeetCode (medium level) and review ML concepts from Andrew Ng's ML course or equivalent.
Focus Topics
Neural Networks and Backpropagation Basics
Basic understanding of neural network architectures (input, hidden, output layers), activation functions (ReLU, sigmoid, tanh), loss functions, and conceptual understanding of backpropagation.
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Core ML Concepts and Model Evaluation
Understanding supervised vs. unsupervised learning, train/test/validation splits, cross-validation, overfitting/underfitting, regularization basics (L1, L2), and evaluation metrics (accuracy, precision, recall, F1, ROC-AUC).
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Data Structures and Algorithms (DSA)
Proficiency in arrays, linked lists, hash tables, trees, graphs, and classic algorithms (sorting, searching, DFS, BFS, dynamic programming). Ability to analyze time and space complexity.
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Python Programming
Fluent Python coding: writing clean, efficient code; understanding Python data structures (lists, dictionaries, sets); basic libraries (math, collections). Avoid syntax errors under time pressure.
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Technical Phone Screen - ML Fundamentals
What to Expect
A 45-60 minute technical phone/video interview with an ML engineer or data scientist from Microsoft, conducted before onsite interviews. This round dives deeper into machine learning fundamentals, your practical experience, and core concepts. You'll discuss how you approach ML problems, explain algorithms and techniques, and potentially code on a shared document or whiteboard. The interviewer assesses your ability to articulate complex ML concepts clearly, think through trade-offs, and demonstrate hands-on experience. Expect questions like 'walk me through building an ML model' or 'how do you choose algorithms based on dataset characteristics.' You may also be asked to explain a past ML project in detail or discuss how you handled specific challenges (e.g., handling imbalanced datasets, tuning hyperparameters, improving model performance).
Tips & Advice
Before the call, have 2-3 detailed ML projects prepared that you can discuss for 10-15 minutes each—include the problem, your approach, models used, challenges, and results. For senior-level candidates, emphasize not just what you did, but *why* you made specific decisions and what you learned. When asked conceptual questions, don't just define concepts; explain when and why you'd use them. For example, instead of just defining regularization, explain when you've used L1 vs. L2, why overfitting occurred, and how regularization helped. Use the STAR method (Situation, Task, Action, Result) for project discussions. Have a notebook handy with quick reference notes on key algorithms, their assumptions, complexity, and when to apply them. If asked to code, think aloud as you write; explain your approach before coding. For this round, focus on clarity and depth over speed. If you don't know an answer, say so honestly and discuss how you'd approach learning it. Ask clarifying questions if a prompt is ambiguous—this shows maturity.
Focus Topics
Handling Data Quality Issues
Practical approaches to missing data (imputation strategies, deletion), outlier detection and handling, class imbalance, data drift, and working with noisy or incomplete datasets. Real-world experience dealing with messy data.
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Neural Networks and Deep Learning Basics
Understanding neural network architectures (MLPs, CNNs, RNNs, transformers), activation functions, loss functions, optimization methods (SGD, Adam), and backpropagation concept. Experience with at least one deep learning framework (TensorFlow, PyTorch).
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Machine Learning Algorithms and Trade-offs
Deep understanding of supervised learning (linear/logistic regression, decision trees, random forests, SVMs, gradient boosting), unsupervised learning (k-means, hierarchical clustering, PCA), and ensemble methods. Know when to use each, their assumptions, and trade-offs (bias-variance, interpretability vs. accuracy, training time vs. performance).
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Feature Engineering and Selection
Practical experience with feature scaling/normalization, handling categorical variables, feature interaction, dimensionality reduction (PCA), and strategies for high-dimensional data. Understanding how features impact model performance.
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Regularization and Overfitting Prevention
Detailed understanding of overfitting vs. underfitting, regularization techniques (L1, L2, elastic net, dropout, early stopping), cross-validation methods, and how to diagnose when each is needed. Practical experience applying these to real datasets.
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Model Evaluation and Metrics
Comprehensive understanding of metrics (accuracy, precision, recall, F1, ROC-AUC, RMSE, MAE) and when each is appropriate. Experience with confusion matrices, handling class imbalance (SMOTE, class weights), and understanding metric trade-offs. Business context for choosing metrics.
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Onsite Interview 1: Machine Learning System Design
What to Expect
A 60-minute onsite interview assessing your ability to design end-to-end ML systems for production. You'll be given a scenario (e.g., 'Design a recommendation system for Microsoft Teams' or 'Build a content moderation system') and asked to sketch an architecture, choose appropriate services from Azure ecosystem, discuss trade-offs, failure recovery, and monitoring. This round evaluates system-level thinking, understanding of scalability constraints, production considerations, and ability to connect technical decisions to business requirements. You'll discuss data pipelines, model training infrastructure, serving strategies, monitoring, and operational aspects. For senior engineers, expect questions about handling millions of users, minimizing latency, optimizing compute costs, and ensuring reliability.
Tips & Advice
Start by clarifying requirements and constraints: scale (QPS, data volume), latency targets, consistency requirements, business metrics to optimize. Draw a clear architecture diagram covering data ingestion, preprocessing, model training, inference serving, and monitoring. Discuss trade-offs explicitly (e.g., batch vs. real-time serving, accuracy vs. latency, cost vs. freshness). For Microsoft roles, mention specific Azure services: Azure ML for experiment tracking and deployment, Azure Data Factory or Synapse for data engineering, AKS or Azure Functions for hosting, Cosmos DB or Data Lake for storage. Discuss MLOps: CI/CD pipelines, model versioning, A/B testing infrastructure, and canary deployments. Address operational concerns: monitoring model drift, retraining frequency, handling failures gracefully, and alerting. For senior roles, discuss how you'd scale this to millions of users, multi-region deployment, cost optimization, and organizational processes. Use concrete numbers: estimate throughput, latency, storage needs. Be comfortable saying 'I'd need to discuss this with the team' if a question goes outside your expertise, but show you understand the considerations. Practice on system design resources; ML-specific system design is less common than traditional system design but uses similar principles.
Focus Topics
ML Pipeline Automation and MLOps
CI/CD for ML: automating data validation, model training, testing, and deployment. Model versioning, experiment tracking, hyperparameter tuning infrastructure. Integration with DevOps practices.
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Model Serving and Inference Optimization
Strategies for deploying models to production: batch inference, real-time serving, edge deployment. Optimization for latency and throughput. Containerization, serving frameworks (Flask, FastAPI, TensorFlow Serving, KServe), and scaling patterns.
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Azure ML Ecosystem and Cloud Services
Familiarity with Microsoft's cloud ML platform: Azure Machine Learning (experiment tracking, model registry, deployment), Azure Data Factory/Synapse (data pipelines), AKS/Azure Functions (model serving), Cosmos DB/Data Lake (storage), and integration patterns.
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Monitoring, Alerting, and Model Drift Detection
Strategies for monitoring model performance in production, detecting data drift and model drift, setting up alerts, establishing SLOs, and automated retraining pipelines. Understanding degradation patterns and incident response.
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End-to-End ML System Architecture
Ability to design complete ML systems from data ingestion through serving and monitoring. Including data pipelines, feature stores, model training infrastructure, inference serving layers, and feedback loops. Understanding of batch vs. real-time processing trade-offs.
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Scalability and Performance Optimization
Designing systems to handle millions of users, minimizing latency, optimizing compute costs, caching strategies, load balancing, and multi-region deployment. Understanding bottlenecks and profiling.
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Onsite Interview 2: Core ML Theory and Algorithm Design
What to Expect
A 60-minute technical interview diving deep into machine learning theory, algorithm design, and mathematical foundations. You'll be asked to derive update rules, explain the intuition behind algorithms, and solve challenging ML problems from first principles. Expect questions about backpropagation, optimization methods, regularization theory, bias-variance trade-off, and how algorithms handle specific data characteristics. You might be asked to explain how gradient descent works, derive a decision boundary for logistic regression, or discuss convergence properties of different optimizers. For anomaly detection, you might design an approach to identify outliers. For imbalanced datasets, you'd discuss techniques like SMOTE vs. class-weighted loss functions and their trade-offs. This round tests whether you understand ML at a deep level, not just how to apply libraries.
Tips & Advice
Prepare by reviewing mathematical foundations: linear algebra basics (matrix multiplication, eigenvalues), calculus (gradients, chain rule), and probability. Practice deriving core algorithms—be able to write out the update rule for linear regression, logistic regression, gradient descent, and backpropagation. Use a whiteboard or paper; visualize concepts. For senior roles, go beyond 'I know how this works' to 'here's why this design choice matters.' For example, discuss why Adam optimizer has adaptive learning rates and when that helps. Be comfortable with the phrase 'Let me think through this' and work through problems methodically. If stuck, break problems into simpler components. Discuss assumptions, trade-offs, and when approaches fail. For example, discuss when k-means fails and alternative clustering methods. Prepare to discuss how you'd approach an unusual ML problem (e.g., anomaly detection with limited labeled data, model compression for mobile deployment). Have several concrete examples ready from your work. If asked about research papers or techniques you're unfamiliar with, show intellectual honesty—discuss how you'd approach learning new techniques. For senior candidates, discuss contributing to or reading ML research.
Focus Topics
Advanced Architectures and Specialized Techniques
Deep understanding of CNN, RNN, Transformer architectures, attention mechanisms, GANs, autoencoders, or other advanced techniques relevant to your experience. Ability to discuss architectural choices and trade-offs.
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Anomaly Detection and Imbalanced Data Handling
Approaches to anomaly detection (isolation forests, autoencoders, one-class SVM), handling class imbalance (SMOTE, class weights, threshold adjustment), and understanding the trade-offs. When to use each technique.
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Backpropagation and Neural Network Training
Detailed understanding of backpropagation algorithm, chain rule application, computational graphs, and vanishing/exploding gradients. Practical techniques to stabilize training (batch normalization, gradient clipping, careful initialization).
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Bias-Variance Trade-off and Generalization
Mathematical understanding of bias and variance, the bias-variance decomposition of error, and how different models/hyperparameters affect this trade-off. Practical strategies to reduce bias or variance based on symptoms.
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Regularization Theory and Techniques
Mathematical foundations of regularization: L1, L2 norms, elastic net, dropout (and its connection to ensemble methods), early stopping. Understanding regularization as a form of inductive bias. Trade-offs between different approaches.
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Gradient Descent and Optimization Methods
Deep understanding of gradient descent variants (SGD, Mini-batch GD, Adam, RMSprop, Momentum), convergence analysis, learning rate scheduling, and optimization challenges. Derivation of update rules and understanding when each method is appropriate.
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Onsite Interview 3: Coding and Data Structures
What to Expect
A 60-minute onsite technical interview focused on data structures, algorithms, and coding problem-solving under pressure. You'll be given 1-2 coding problems (typically medium to hard difficulty) to solve on a whiteboard or collaborative coding platform. Problems may involve graphs, trees, dynamic programming, or algorithms relevant to data processing (e.g., efficiently finding patterns in data, optimizing computations). The interviewer is assessing your ability to solve problems methodically, write clean code, optimize for efficiency, and communicate your thinking. This is similar to the online assessment but in-person, with an interviewer asking clarifying questions and follow-up questions. For senior roles, you may be asked to optimize a solution further or discuss how to parallelize an algorithm.
Tips & Advice
Follow this approach: (1) Ask clarifying questions about input/output format, constraints, and edge cases; (2) Discuss your approach verbally before coding; (3) Start with a clear, correct solution (even if not optimal); (4) Then optimize if time permits. Write readable code with variable names that make sense. For senior roles, don't just solve—discuss trade-offs. For example, if you use a hash map, discuss why it's better than sorting or a different data structure. If asked for optimization, discuss time/space trade-offs and why the optimization matters (e.g., 'this reduces time from O(n²) to O(n log n), critical if n is large'). Practice on LeetCode or HackerRank, focusing on medium and some hard problems. Understand common patterns: two pointers, sliding window, BFS/DFS for graphs, binary search, dynamic programming. For senior roles, aim to solve problems quickly, giving you time to discuss complexity and optimization. If you get stuck, say so and ask for hints—this is better than sitting silently. Talk through your logic; interviewers want to understand your thinking.
Focus Topics
Dynamic Programming and Optimization
Understanding dynamic programming approach: breaking problems into overlapping subproblems, memoization, bottom-up solutions. Recognizing when DP applies and solving DP problems efficiently.
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Trees and Graphs
Understanding tree structures (binary trees, BSTs, balanced trees), graph representations, and traversal algorithms (DFS, BFS). Solving problems like finding paths, detecting cycles, or optimizing traversals.
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Complexity Analysis and Optimization
Ability to analyze time and space complexity of algorithms, recognize bottlenecks, and optimize solutions. Understanding trade-offs (e.g., preprocessing time vs. query time). Discussing optimizations from O(n²) to O(n log n) or O(n).
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Problem-Solving Methodology and Communication
Systematic approach to solving: clarifying requirements, discussing approach before coding, explaining reasoning, handling edge cases, and optimizing. Clear communication of thought process.
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Data Structures: Arrays, Strings, and Hash Maps
Proficiency with basic data structures: arrays, strings, hash maps. Understanding time/space complexity, when to use each, and common algorithms (searching, sorting, filtering). Solving problems involving these structures efficiently.
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Onsite Interview 4: Behavioral and Leadership
What to Expect
A 45-60 minute behavioral interview assessing your leadership potential, teamwork, problem-solving approach, and cultural fit with Microsoft. You'll be asked about past experiences where you handled complex challenges, made difficult decisions, collaborated across teams, or mentored others. Expect questions using the STAR method (Situation, Task, Action, Result). For senior roles, the focus is on how you've influenced team direction, handled conflicts, driven results through others, and grown professionally. You might be asked about a time you disagreed with a colleague, how you've handled failure or setbacks, or how you prioritize work when facing competing deadlines. This round also assesses how you embody Microsoft's cultural values: learning mindset, ownership, collaboration, and making ethical decisions. The interviewer is listening for maturity, reflection, and ability to grow from experiences.
Tips & Advice
Prepare 5-7 detailed stories using the STAR format: each story should be specific (names, dates if relevant), show your agency (what YOU did, not the team), demonstrate a skill or value, and end with measurable results. Stories should cover: (1) a complex technical problem you led, (2) a time you mentored someone, (3) a conflict or disagreement you resolved, (4) a project failure and what you learned, (5) a time you had competing priorities, (6) innovation or improvement you drove, (7) a time you collaborated cross-functionally. For senior roles, stories should show leadership of initiatives (not just participation), influence on team direction, and impact beyond your immediate work. When answering, be concise (2-3 minutes per story), focus on your role, and connect to the role requirements (e.g., 'This shows how I'd lead complex ML system design initiatives'). Be authentic about failures—discuss what went wrong, what you learned, and how you'd approach it differently. Show growth mindset: discuss how feedback improved your work. Avoid corporate jargon; use natural language. Listen carefully to each question; tailor your answer rather than delivering a canned response. For Microsoft specifically, reference company values and research recent CEO emails or announcements to show understanding of company direction.
Focus Topics
Handling Conflict and Disagreement
Examples of technical disagreements or interpersonal conflicts, how you handled them constructively, and what you learned. Emphasis on collaboration and finding good solutions rather than 'winning.'
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Learning from Failure and Setbacks
A specific project or initiative that didn't succeed as planned, what went wrong, how you responded, and what you learned. Reflecting on personal growth and course correction.
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Cross-functional Collaboration and Communication
Examples of working effectively with non-ML teams (product, engineering, business), communicating technical concepts to non-technical audiences, and driving alignment across functions.
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Mentoring and Developing Team Members
Specific examples of mentoring junior engineers or data scientists: how you helped them grow, technical guidance provided, their outcomes. Showing investment in others' development and success.
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Owning and Leading Complex ML Projects
Examples of projects where you took full ownership or leadership, including defining scope, making technical decisions, managing timelines, and delivering results. Emphasis on end-to-end impact and how you drove success.
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Onsite Interview 5: Product Sense and Business Impact
What to Expect
A 60-minute interview assessing your ability to think beyond technical solutions to business impact, user needs, and strategic thinking. You'll discuss how to measure success, understand business metrics, and make trade-off decisions that balance accuracy, latency, cost, and other factors. Expect scenario-based questions like 'Design a model to improve user engagement in Teams' or 'How would you approach building a recommendation system while respecting privacy?' You'll be asked to define success metrics, identify what data you'd need, discuss privacy/ethical considerations, and explain how your solution creates business value. For senior roles, this assesses whether you can think strategically about problems, not just technically solve them. The interviewer is also evaluating whether you understand Microsoft's mission and products, and how your work aligns with broader company goals.
Tips & Advice
Approach these scenarios systematically: (1) clarify the business problem and success criteria, (2) discuss what you'd measure (north star metric), (3) explain your approach to the technical problem, (4) discuss trade-offs (accuracy vs. latency vs. cost), (5) address non-technical considerations (privacy, ethics, fairness, compliance). For senior roles, think about scale and sustainability: how would this work for millions of users? What operational burden does it create? For Microsoft, consider how this aligns with their cloud/AI strategy and products. Show business acumen: discuss whether a 95% vs. 98% accuracy improvement justifies the complexity cost. For example, in a content moderation system, understand that some false positives (incorrectly flagged content) might be acceptable to minimize harmful content (false negatives). Discuss A/B testing: how would you validate that your approach actually improves business outcomes? Show ethical awareness: discuss potential harms, fairness, and bias mitigation. For Microsoft roles, familiarize yourself with key products and their ML components. Research Microsoft's responsible AI principles and show how your thinking aligns. When discussing trade-offs, be explicit: 'I'd prioritize latency over marginal accuracy gains because most users care more about speed.' This shows mature prioritization. Use concrete examples from your experience where you made similar decisions.
Focus Topics
Understanding Microsoft Products and AI Strategy
Familiarity with key Microsoft products (Azure, Office 365, Teams, Copilot, Bing, etc.), how ML enhances them, Microsoft's approach to cloud AI, and recent announcements about AI initiatives.
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Ethical Considerations, Bias, and Fairness
Awareness of potential harms from ML systems, strategies to mitigate bias, ensuring fairness across user groups, privacy considerations, and regulatory compliance (e.g., GDPR). Thoughtful approach to responsible AI.
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Defining Success Metrics and Business Impact
Ability to translate business problems into measurable success metrics, identify north star metrics vs. supporting metrics, and understand how ML solutions drive business value. Knowing when to optimize for accuracy vs. other factors.
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End-to-End Product Thinking and Trade-offs
Balancing competing objectives: accuracy, latency, cost, model size, interpretability. Making explicit trade-off decisions based on business context. Understanding when 'good enough' is actually better than perfect.
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Experimentation and Measurement
Designing A/B tests to validate ML improvements, choosing relevant metrics to measure success, understanding statistical significance, and learning from results. Skepticism of vanity metrics.
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Frequently Asked Machine Learning Engineer Interview Questions
Sample Answer
Sample Answer
Sample Answer
def fib_memo(n, memo=None):
if memo is None: memo = {0: 0, 1: 1}
if n in memo: return memo[n]
memo[n] = (fib_memo(n-1, memo) + fib_memo(n-2, memo))
return memo[n]def fib_iter(n):
if n == 0: return 0
a, b = 0, 1
for _ in range(1, n):
a, b = b, a + b
return bMOD = 10**9+7
def fib_fast_doubling(n):
# returns (F(n), F(n+1))
if n == 0: return (0, 1)
a, b = fib_fast_doubling(n // 2)
c = (a * ((b * 2 - a) % MOD)) % MOD
d = (a*a + b*b) % MOD
if n % 2 == 0:
return (c, d)
else:
return (d, (c + d) % MOD)
def fib_mod(n):
return fib_fast_doubling(n)[0]Sample Answer
Sample Answer
Sample Answer
Sample Answer
import os, random, numpy as np, torch
SEED = 42
os.environ['PYTHONHASHSEED'] = str(SEED)
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed_all(SEED)
torch.use_deterministic_algorithms(True) # PyTorch deterministic kernelsSample Answer
Sample Answer
from collections import deque
def intersect_x(L1, L2):
# return x-coordinate of intersection as float
m1,k1 = L1; m2,k2 = L2
return (k2 - k1) / (m1 - m2)
class CHT:
def __init__(self):
self.deq = deque()
self.xpos = deque() # optional store intersections
def add_line(self, m, k):
# add line with slope m (monotonic increasing)
while len(self.deq) >= 1:
m0,k0 = self.deq[-1]
if m0 == m:
# keep smaller intercept for same slope
if k >= k0: return
else: self.deq.pop();
elif len(self.deq) >= 2:
# check if new line makes last obsolete
x_prev = intersect_x(self.deq[-2], self.deq[-1])
x_new = intersect_x(self.deq[-1], (m,k))
if x_new <= x_prev:
self.deq.pop()
else:
break
else:
break
self.deq.append((m,k))
def query(self, x):
# monotonic non-decreasing x
while len(self.deq) >= 2 and (self.deq[0][0]*x + self.deq[0][1]) >= (self.deq[1][0]*x + self.deq[1][1]):
self.deq.popleft()
m,k = self.deq[0]
return m*x + kSample Answer
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