Databricks-Certified-Professional-Data-Scientist Dumps

Databricks-Certified-Professional-Data-Scientist Exam Topics

The Databricks-Certified-Professional-Data-Scientist exam covers a wide range of topics related to data science and machine learning. The following table provides a summary of the main topics covered in the exam, along with the percentage of the exam score that each topic represents:

In order to pass the exam, candidates must achieve a score of at least 70%.

Data Engineering

This topic covers the skills and knowledge required to work with data in a variety of formats and sources. Candidates should be able to:

• Understand the different types of data and their characteristics
• Choose the appropriate data storage and processing technologies
• Perform data cleaning and transformation tasks
• Create and manage data pipelines

Exploratory Data Analysis and Visualization

This topic covers the skills and knowledge required to explore and visualize data in order to gain insights. Candidates should be able to:

• Use data visualization tools to create informative and engaging visualizations
• Identify patterns and trends in data
• Develop and test hypotheses about data
• Communicate findings to others in a clear and concise manner

Machine Learning Algorithms

This topic covers the skills and knowledge required to develop and implement machine learning models. Candidates should be able to:

• Understand the different types of machine learning algorithms and their strengths and weaknesses
• Select the appropriate machine learning algorithm for a given problem
• Train and evaluate machine learning models
• Interpret the results of machine learning models

Model Deployment and Evaluation

This topic covers the skills and knowledge required to deploy and evaluate machine learning models in production. Candidates should be able to:

• Understand the different methods for deploying machine learning models
• Monitor and evaluate the performance of deployed machine learning models
• Troubleshoot and resolve problems with deployed machine learning models

Effective Communication

This topic covers the skills and knowledge required to effectively communicate data science and machine learning results to others. Candidates should be able to:

• Write clear and concise technical reports
• Give presentations about data science and machine learning projects
• Explain data science and machine learning concepts to non-technical audiences