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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

SectionObjectives
ML pipeline automation and orchestration- Pipeline design
  • 1. Build end-to-end ML pipelines
    • 2. Use Vertex AI Pipelines
      Data preparation and processing- Feature engineering
      • 1. Feature selection and representation techniques
        • 2. Transform and preprocess datasets
          - Data ingestion and pipelines
          • 1. Use BigQuery and data processing services
            • 2. Build data pipelines for training and serving
              Deployment and operations- Model deployment
              • 1. Deploy models using Vertex AI endpoints
                • 2. Batch and online prediction systems
                  - Monitoring and maintenance
                  • 1. Monitor model drift and performance
                    • 2. Retraining and lifecycle management
                      Designing ML solutions- Framing ML problems
                      • 1. Define success metrics and evaluation criteria
                        • 2. Translate business problems into ML tasks
                          - ML architecture design
                          • 1. Design scalable ML systems on GCP
                            • 2. Select appropriate ML models and approaches
                              ML model development- Model training and tuning
                              • 1. Train models using TensorFlow / Vertex AI
                                • 2. Hyperparameter tuning and optimization
                                  - Evaluation
                                  • 1. Model validation strategies
                                    • 2. Evaluate model performance metrics

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                                      最新的 Google Cloud Certified Professional-Machine-Learning-Engineer 免費考試真題 (Q381-Q386):

                                      問題 #381
                                      You are creating a deep neural network classification model using a dataset with categorical input values.
                                      Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?

                                      答案：D

                                      解題說明：
                                      * Option A is incorrect because converting each categorical value into an integer value is not a good way to encode categorical values with high cardinality. This method implies an ordinal relationship between the categories, which may not be true. For example, assigning the values 1, 2, and 3 to the categories
                                      "red", "green", and "blue" does not make sense, as there is no inherent order among these colors1.
                                      * Option B is correct because converting the categorical string data to one-hot hash buckets is a suitable way to encode categorical values with high cardinality. This method uses a hash function to map each category to a fixed-length vector of binary values, where only one element is 1 and the rest are 0. This method preserves the sparsity and independence of the categories, and reduces the dimensionality of the input space2.
                                      * Option C is incorrect because mapping the categorical variables into a vector of boolean values is not a valid way to encode categorical values with high cardinality. This method implies that each category can be represented by a combination of true/false values, which may not be possible for a large number of categories. For example, if there are 10,000 categories, then there are 2

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