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Databricks-Certified-Professional-Data-Scientist pdf
  • Exam Code: Databricks-Certified-Professional-Data-Scientist
  • Exam Name: Databricks Certified Professional Data Scientist Exam
  • Q & A: 140 Questions and Answers
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  • Exam Code: Databricks-Certified-Professional-Data-Scientist
  • Exam Name: Databricks Certified Professional Data Scientist Exam
  • Q & A: 140 Questions and Answers
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  • Exam Code: Databricks-Certified-Professional-Data-Scientist
  • Exam Name: Databricks Certified Professional Data Scientist Exam
  • Q & A: 140 Questions and Answers
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To help professionals demonstrate their proficiency in using the Databricks platform, the company has introduced the Databricks Certified Professional Data Scientist exam. Databricks Certified Professional Data Scientist Exam certification is designed to test a candidate's knowledge and skills in using the Databricks platform for data science tasks. It covers a range of topics, including data preparation, exploratory data analysis, feature engineering, and model tuning.

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Databricks Certified Professional Data Scientist (DCPDS) exam is designed to test the skills and knowledge of data scientists who use Databricks for their work. Databricks is a cloud-based platform that provides a unified analytics engine for big data processing and machine learning. The DCPDS exam is administered by Databricks, and passing it is a significant achievement for data scientists who wish to validate their expertise in using Databricks.

Reference: https://credentials.databricks.com/group/227970

Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:

SectionObjectives
Machine Learning Fundamentals- Supervised Learning
  • 1. Regression models
    • 2. Model evaluation metrics
      • 3. Classification models
        - Unsupervised Learning
        • 1. Clustering techniques
          • 2. Dimensionality reduction
            Responsible AI and Production ML- Model governance
            • 1. Reproducibility practices
              • 2. Model monitoring
                Model Evaluation and Optimization- Performance evaluation
                • 1. Cross-validation
                  • 2. Bias-variance tradeoff
                    - Hyperparameter tuning
                    • 1. Random search
                      • 2. Grid search
                        Machine Learning with Databricks- MLflow and experiment tracking
                        • 1. Experiment comparison
                          • 2. Model logging
                            - Model deployment
                            • 1. Real-time inference concepts
                              • 2. Batch inference
                                Data Preparation and Feature Engineering- Data cleaning and transformation
                                • 1. Handling missing values
                                  • 2. Encoding categorical variables
                                    - Feature engineering with Spark
                                    • 1. Feature pipelines
                                      • 2. Distributed data processing

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