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CertNexus DSP-110 Test Passed : Certified Data Science Practitioner

DSP-110 actual test
  • Exam Code: DSP-110
  • Exam Name: Certified Data Science Practitioner
  • Updated: Oct 26, 2026
  • Q & A: 0 Questions and Answers
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CertNexus DSP-110 Exam Syllabus Topics:

SectionWeightObjectives
Extracting, Transforming, and Loading Data21.3%- Apply Problem-Specific Transformations to Datasets
  • 1. Apply word embeddings
    • 2. Generate latent representations for image data
      - Clean Datasets
      • 1. Parse data
        • 2. Identify and eliminate data irregularities
          • 3. Correct data formats for storage and querying
            • 4. Check for corrupted data
              • 5. Remove duplicate data
                - Gather Relevant Datasets
                • 1. Collect open-source data
                  • 2. Research third-party data availability
                    • 3. Read data
                      - Merge Datasets
                      • 1. Join data from different sources
                        - Load Data
                        • 1. Load data into a database
                          • 2. Export data to CSV files
                            • 3. Load data into a DataFrame
                              • 4. Load data into a visualization tool
                                • 5. Create an endpoint
                                  Building Models24%- Prepare Datasets for Modeling
                                  • 1. Split data into training, testing, and validation sets as applicable
                                    • 2. Determine the proportions of data for training, testing, and validation
                                      - Evaluate Models
                                      • 1. Select the best-performing model
                                        • 2. Define evaluation metrics
                                          • 3. Compare model outputs
                                            • 4. Store the model for operational use
                                              - Build Training Models
                                              • 1. Identify algorithms to evaluate
                                                • 2. Tune hyperparameters where applicable
                                                  • 3. Train models
                                                    Defining the Question to Be Addressed Through the Application of Data Science8%- Understand Stakeholder Challenges
                                                    • 1. Understand data privacy, security, and governance policies
                                                      • 2. Obtain permission and access to data
                                                        • 3. Understand stakeholder terminology
                                                          - Classify a Question into a Known Data Science Problem
                                                          • 1. Select a modeling type
                                                            • 2. Access references
                                                              • 3. Identify data sources and types
                                                                - Identify the Project Scope
                                                                • 1. Identify project specifications, objectives, metrics, KPIs, and stakeholder requirements
                                                                  • 2. Identify mandatory and optional deliverables
                                                                    • 3. Identify project limitations, including time, technical, resource, data, and risk constraints
                                                                      Communicating Findings8%- Report Findings
                                                                      • 1. Derive insights from findings
                                                                        • 2. Generate lift or gain charts
                                                                          • 3. Identify features that drive outcomes using explainability techniques and variable importance plots
                                                                            • 4. Present model results
                                                                              • 5. Implement a model in a basic web application for proof-of-concept demonstration
                                                                                Performing Exploratory Data Analysis30.7%- Preprocess Data
                                                                                • 1. Determine how to handle missing values through imputation or record removal
                                                                                  • 2. Normalize, standardize, or scale data
                                                                                    • 3. Identify missing values
                                                                                      - Carry Out Feature Engineering
                                                                                      • 1. Apply feature reduction methods
                                                                                        • 2. Split features
                                                                                          • 3. Convert dates into useful features
                                                                                            • 4. Assign feature values to bins or groups
                                                                                              • 5. Encode categorical data
                                                                                                - Examine Data
                                                                                                • 1. Identify outliers
                                                                                                  • 2. Generate summary statistics
                                                                                                    • 3. Find correlations
                                                                                                      • 4. Visualize distributions
                                                                                                        • 5. Examine feature types
                                                                                                          • 6. Identify target features
                                                                                                            Testing Models8%- Test Pipelines
                                                                                                            • 1. Monitor model and pipeline performance over time
                                                                                                              • 2. Verify that models operate correctly
                                                                                                                • 3. Put models into production
                                                                                                                  - Test Hypotheses
                                                                                                                  • 1. Evaluate test results
                                                                                                                    • 2. Define success criteria for tests
                                                                                                                      • 3. Design A/B tests

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