Databricks-Machine-Learning-Associate

Main points of Databricks-Machine-Learning-Associate Test

The Databricks Machine Learning Associate (DAMA) exam assesses your understanding of how to build and deploy machine learning models on the Databricks platform. Here are the main points covered in the exam:

1. Databricks Fundamentals:

• Databricks Architecture: Understanding the Databricks architecture, including clusters, workspaces, and different components.
• Data Ingestion & Manipulation: Working with various data sources (e.g., Delta Lake, CSV, Parquet), data transformations, and data cleaning using Spark SQL and Python.
• Data Exploration & Visualization: Using Databricks notebooks and tools for data exploration, analysis, and visualization.

2. Machine Learning Fundamentals:

• Machine Learning Concepts: Understanding key machine learning concepts like supervised learning, unsupervised learning, and reinforcement learning.
• Common Machine Learning Algorithms: Familiarity with popular algorithms like linear regression, logistic regression, decision trees, random forests, and clustering algorithms.
• Model Evaluation & Selection: Knowing how to evaluate model performance using metrics like accuracy, precision, recall, F1-score, and AUC.
• Hyperparameter Tuning: Understanding the importance of hyperparameter tuning for model optimization and using techniques like grid search and random search.

3. Machine Learning on Databricks:

• MLflow: Using MLflow for model tracking, experiment management, and model deployment.
• AutoML: Leveraging Databricks AutoML for automated feature engineering and model selection.
• Model Deployment & Monitoring: Understanding how to deploy models into production using Databricks and monitor their performance over time.
• Spark MLlib: Working with the Spark MLlib library for building machine learning models in Python.

4. Databricks Security & Governance:

• Data Security: Understanding Databricks security features like access control, encryption, and data masking.
• Data Governance: Knowing how to implement data governance policies and best practices within Databricks.

Exam Preparation:

• Databricks Documentation: Utilize the official Databricks documentation for comprehensive information.
• Databricks Learning Path: Explore the Databricks Learning Path, which offers courses covering relevant topics.
• Practice Labs: Work through Databricks practice labs to gain practical experience and solidify your understanding.
• Mock Exams: Take mock exams to assess your readiness and identify areas requiring further study.

Note: The specific exam content and weighting may vary. Refer to the latest Databricks exam guide for the most current information.