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Professional Certification

Machine Learning Engineering - Skill Assessment

This assessment is designed to evaluate the technical knowledge and practical problem-solving skills required for a professional Machine Learning Engineer role. Machine Learning Engineering is not limited to training a model and checking its accuracy. A professional Machine Learning Engineer must understand the complete lifecycle of a machine learning system from collecting and preparing data to selecting models, training, evaluating, deploying, monitoring, and improving them in production. This assessment focuses on practical machine learning concepts and real engineering challenges. Candidates will be tested on data preparation, feature engineering, model selection, training strategies, evaluation metrics, overfitting, underfitting, validation methods, optimization, deployment, and production monitoring. The assessment also covers the engineering side of machine learning, including reproducibility, model versioning, data pipelines, APIs, model deployment, scalability, monitoring, and identifying problems such as data drift and model performance degradation. Candidates may be presented with real-world scenarios and asked to identify the cause of poor model performance, choose an appropriate evaluation method, detect data leakage, improve generalization, or decide how a model should be deployed and monitored.

Questions

Duration

90 Mins

Passing

75%

Format

MCQ

Exam Fee

₹ 49.00

Digital Certificate Included

Important Details

Strict Time Limit

You have exactly 90 minutes. Once started, the timer cannot be paused.

Passing Threshold

You must score 75% or higher to prove your skills and receive your certificate.

Retakes & Improvements

If you fail, or want to aim for a higher score, you can purchase a new exam session for ₹99.

Physical Print Delivery

Your digital certificate is included. If you want a professionally printed copy mailed to your door, it costs ₹399.

Detailed Report

Want to see which exact questions you missed? An optional detailed performance analysis is available after the exam for ₹29.

No Refunds

By paying, you provision secure server resources immediately. Payments are strictly non-refundable.

Who is this for?

Candidates should have a good understanding of programming and basic mathematics. Knowledge of Python is strongly recommended. Candidates should also have familiarity with concepts such as statistics, probability, linear algebra, and data analysis. Practical experience using libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, or PyTorch will be helpful. Candidates do not need to be experts in every machine learning framework, but they should understand how machine learning models are trained, evaluated, and used in real applications. This assessment is suitable for candidates applying for roles such as: Machine Learning Engineer Machine Learning Developer AI Engineer Data Scientist MLOps Engineer Applied Machine Learning Engineer Artificial Intelligence Developer Junior Machine Learning Engineer

What you'll be tested on

Machine Learning Fundamentals

Supervised Learning

Unsupervised Learning

Regression

Classification

Clustering

Feature Engineering

Feature Selection

Data Preprocessing

Data Cleaning

Missing Values

Data Normalization

Standardization

Categorical Encoding

Training Data

Validation Data

Test Data

Cross Validation

Data Leakage

Overfitting

Underfitting

Bias Variance Tradeoff

Model Selection

Hyperparameter Tuning

Grid Search

Random Search

Regularization

Loss Functions

Gradient Descent

Model Evaluation

Accuracy

Precision

Recall

F1 Score

ROC Curve

AUC

Confusion Matrix

Regression Metrics

Mean Absolute Error

Mean Squared Error

Random Forest

Decision Trees

Support Vector Machines

K-Nearest Neighbors

Neural Networks

Deep Learning Basics

Python

NumPy

Pandas

Scikit-learn

TensorFlow Basics

PyTorch Basics

Model Deployment

MLOps

Model Versioning

Experiment Tracking

Data Pipelines

Model Monitoring

Data Drift

Concept Drift

Model APIs

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