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

SectionObjectives
Topic 1: ML model development- Evaluation
  • 1. Model validation strategies
    • 2. Evaluate model performance metrics
      - Model training and tuning
      • 1. Hyperparameter tuning and optimization
        • 2. Train models using TensorFlow / Vertex AI
          Topic 2: Deployment and operations- Monitoring and maintenance
          • 1. Retraining and lifecycle management
            • 2. Monitor model drift and performance
              - Model deployment
              • 1. Batch and online prediction systems
                • 2. Deploy models using Vertex AI endpoints
                  Topic 3: ML pipeline automation and orchestration- Pipeline design
                  • 1. Build end-to-end ML pipelines
                    • 2. Use Vertex AI Pipelines
                      Topic 4: Data preparation and processing- Feature engineering
                      • 1. Transform and preprocess datasets
                        • 2. Feature selection and representation techniques
                          - Data ingestion and pipelines
                          • 1. Use BigQuery and data processing services
                            • 2. Build data pipelines for training and serving
                              Topic 5: Designing ML solutions- Framing ML problems
                              • 1. Define success metrics and evaluation criteria
                                • 2. Translate business problems into ML tasks
                                  - ML architecture design
                                  • 1. Select appropriate ML models and approaches
                                    • 2. Design scalable ML systems on GCP

                                      >> Professional-Machine-Learning-Engineer Exam Actual Questions <<

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                                      Google Professional Machine Learning Engineer Sample Questions (Q384-Q389):

                                      NEW QUESTION # 384
                                      You are working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

                                      Answer: D

                                      Explanation:
                                      https://towardsdatascience.com/time-series-nested-cross-validation-76adba623eb9


                                      NEW QUESTION # 385
                                      Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

                                      Answer: B

                                      Explanation:
                                      Vertex AI Pipelines and AI Platform Prediction are the platform components that best suit the requirements of the data science team. Vertex AI Pipelines is a service that allows you to orchestrate and automate your machine learning workflows using pipelines. Pipelines are portable and scalable ML workflows that are based on containers. You can use Vertex AI Pipelines to schedule model retraining, use custom containers, and integrate with other Google Cloud services. AI Platform Prediction is a service that allows you to host your trained models and serve online predictions. You can use AI Platform Prediction to deploy models trained on Vertex AI or elsewhere, and benefit from features such as autoscaling, monitoring, logging, and explainability. Reference:
                                      Vertex AI Pipelines
                                      AI Platform Prediction


                                      NEW QUESTION # 386
                                      You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

                                      Answer: B

                                      Explanation:
                                      The performance of an image classification model can be measured by various metrics, such as accuracy, precision, recall, F1-score, and mean average precision (mAP). These metrics can be calculated based on the confusion matrix, which compares the predicted labels and the true labels of the images1 One of the best ways to monitor the performance of multiple versions of an image classification model on AI Platform is to compare the mean average precision across the models using the Continuous Evaluation feature.
                                      Mean average precision is a metric that summarizes the precision and recall of a model across different confidence thresholds and classes. Mean average precision is especially useful for multi-class and multi-label image classification problems, where the model has to assign one or more labels to each image from a set of possible labels. Mean average precision can range from 0 to 1, where a higher value indicates a better performance2 Continuous Evaluation is a feature of AI Platform that allows you to automatically evaluate the performance of your deployed models using online prediction requests and responses. Continuous Evaluation can help you monitor the quality and consistency of your models over time, and detect any issues or anomalies that may affect the model performance. Continuous Evaluation can also provide various evaluation metrics and visualizations, such as accuracy, precision, recall, F1-score, ROC curve, and confusion matrix, for different types of models, such as classification, regression, and object detection3 To compare the mean average precision across the models using the Continuous Evaluation feature, you need to do the following steps:
                                      * Enable the online prediction logging for each model version that you want to evaluate. This will allow AI Platform to collect the prediction requests and responses from your models and store them in BigQuery4
                                      * Create an evaluation job for each model version that you want to evaluate. This will allow AI Platform to compare the predicted labels and the true labels of the images, and calculate the evaluation metrics, such as mean average precision. You need to specify the BigQuery table that contains the prediction logs, the data schema, the label column, and the evaluation interval.
                                      * View the evaluation results for each model version on the AI Platform Models page in the Google Cloud console. You can see the mean average precision and other metrics for each model version over time, and compare them using charts and tables. You can also filter the results by different classes and confidence thresholds.
                                      The other options are not as effective or feasible. Comparing the loss performance for each model on a held-out dataset or on the validation data is not a good idea, as the loss function may not reflect the actual performance of the model on the online prediction data, and may vary depending on the choice of the loss function and the optimization algorithm. Comparing the receiver operating characteristic (ROC) curve for each model using the What-If Tool is not possible, as the What-If Tool does not support image data or multi-class classification problems.
                                      References: 1: Confusion matrix 2: Mean average precision 3: Continuous Evaluation overview 4: Configure online prediction logging : [Create an evaluation job] : [View evaluation results] : [What-If Tool overview]


                                      NEW QUESTION # 387
                                      You built a custom ML model using scikit-learn. Training time is taking longer than expected. You decide to migrate your model to Vertex AI Training, and you want to improve the model's training time. What should you try out first?

                                      Answer: C


                                      NEW QUESTION # 388
                                      You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

                                      Answer: B


                                      NEW QUESTION # 389
                                      ......

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