Big Data and Machine Learning Practice Exam
Big Data and Machine Learning Practice Exam
About Big Data and Machine Learning Exam
The Big Data and Machine Learning Certification Exam is designed to validate a professional’s ability to harness large-scale data systems and apply intelligent algorithms to uncover patterns, make predictions, and automate decisions. This certification demonstrates a candidate’s practical and theoretical understanding of managing vast data ecosystems and implementing machine learning solutions in real-world scenarios. The exam bridges the domains of data engineering, analytics, and data science, focusing on scalable computing platforms, advanced statistical modeling, and applied machine learning frameworks. It reflects current industry demands for professionals who can handle data volume, variety, and velocity while designing and deploying intelligent systems.
Who should take the Exam?
This certification is ideal for professionals who work with data-intensive applications and aim to enhance their technical and analytical capabilities. The exam is suitable for:
- Data Scientists seeking to validate end-to-end ML project capabilities.
- Big Data Engineers and Architects building scalable infrastructure for analytics and modeling.
- Machine Learning Engineers implementing algorithms in production environments.
- Business Intelligence Professionals transitioning into AI and advanced analytics roles.
- Software Developers and Analysts integrating ML models into enterprise solutions.
- Graduate Students or Researchers specializing in data mining, AI, or predictive modeling.
The exam is also relevant for technology leaders evaluating AI adoption or designing data-driven strategies.
Skills Required
Candidates are expected to demonstrate a combination of technical expertise, mathematical aptitude, and practical problem-solving capabilities. Key skills include:
- Proficiency in Python, R, or Java for data manipulation and modeling.
- Understanding of distributed computing frameworks like Hadoop and Spark.
- Solid grasp of data structures, algorithms, and database technologies (SQL, NoSQL).
- Knowledge of data preprocessing, ETL pipelines, and real-time data streaming.
- Familiarity with statistical analysis, probability theory, and linear algebra.
- Hands-on experience with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Ability to train, tune, evaluate, and deploy ML models at scale.
Knowledge Gained
Upon completing the exam and its preparation, candidates will be able to:
- Build and optimize scalable data pipelines using tools such as Apache Spark, Kafka, and Hive.
- Apply classification, regression, clustering, and dimensionality reduction algorithms effectively.
- Perform feature selection, engineering, and model validation using industry standards.
- Interpret model outcomes and metrics like precision, recall, ROC-AUC, RMSE, and F1-score.
- Integrate ML solutions with cloud platforms and APIs for real-time decision-making.
- Evaluate data quality, handle missing values, and manage unstructured data types (text, images).
- Implement solutions that are reproducible, interpretable, and aligned with ethical AI standards.
Course Outline
Domain 1 - Foundations of Big Data- Introduction to data types, sources, and formats
- Characteristics of big data: Volume, Variety, Velocity, Veracity, and Value
- Overview of traditional vs. distributed systems
- Data warehousing, data lakes, and cloud-native storage
Domain 2 - Data Engineering and Processing
- Building ETL and ELT pipelines
- Data ingestion with Apache Kafka, Flume, and Sqoop
- Processing with MapReduce and Apache Spark (RDD, DataFrame, SQL)
- Data storage in HDFS, Hive, HBase, Cassandra
Domain 3 - Machine Learning Essentials
- Supervised and unsupervised learning techniques
- Linear regression, decision trees, support vector machines
- K-means clustering, hierarchical clustering, PCA
- Model selection, bias-variance trade-off, cross-validation
Domain 4 - Model Development and Evaluation
- Data preprocessing and feature engineering
- Handling outliers, normalization, encoding
- Hyperparameter tuning (Grid Search, Random Search)
- Model evaluation metrics and confusion matrix interpretation
Domain 5 - Deep Learning and Advanced Topics
- Introduction to neural networks and deep learning
- Convolutional and recurrent neural networks (CNN, RNN)
- Transfer learning and model stacking
- Reinforcement learning basics
Domain 6 - Scalable Machine Learning Systems
- Machine learning with Apache Spark MLlib
- Model parallelization and distributed training
- Batch vs. stream processing for ML pipelines
- Real-time predictions with model-serving APIs
Domain 7 - Cloud Integration and Deployment
- Machine learning services on AWS, GCP, Azure
- CI/CD for ML models using containers and orchestration tools
- Monitoring, logging, and lifecycle management
- AutoML and managed services comparison
Domain 8 - Ethics, Governance, and Responsible AI
- Data privacy and anonymization techniques
- Fairness and bias mitigation in algorithms
- Explainable AI (XAI) principles and tools
- Legal and compliance considerations in AI systems
