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

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

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                                      Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Prüfungsfragen mit Lösungen (Q383-Q388):

                                      383. Frage
                                      You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

                                      Antwort: D

                                      Begründung:
                                      The class imbalance problem is a common challenge in machine learning, especially in classification tasks. It occurs when the distribution of the target classes is highly skewed, such that one class (the majority class) has much more examples than the other class (the minority class). The minority class is often the more interesting or important class, such as failure incidents, fraud cases, or rare diseases. However, most machine learning algorithms are designed to optimize the overall accuracy, which can be biased towards the majority class and ignore the minority class. This can result in poor predictive performance, especially for the minority class.
                                      There are different techniques to deal with the class imbalance problem, such as data-level methods, algorithm-level methods, and evaluation-level methods1. Data-level methods involve resampling the original dataset to create a more balanced class distribution. There are two main types of data-level methods:
                                      oversampling and undersampling. Oversampling methods increase the number of examples in the minority class, either by duplicating existing examples or by generating synthetic examples. Undersampling methods reduce the number of examples in the majority class, either by randomly removing examples or by using clustering or other criteria to select representative examples. Both oversampling and undersampling methods can be combined with upweighting or downweighting, which assign different weights to the examples according to their class frequency, to further balance the dataset.
                                      For the use case of investigating failures of a production line component based on sensor readings, the best option is to downsample the data with upweighting to create a sample with 10% positive examples. This option involves randomly removing some of the negative examples (the majority class) until the ratio of positive to negative examples is 1:9, and then assigning higher weights to the positive examples to compensate for their low frequency. This option can create a more balanced dataset that can improve the performance of the classification models, while preserving the diversity and representativeness of the original data. This option can also reduce the computation time and memory usage, as the size of the dataset is reduced. Therefore, downsampling the data with upweighting to create a sample with 10% positive examples is the best option for this use case.
                                      References:
                                      * A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks


                                      384. Frage
                                      You are developing an ML model that uses sliced frames from video feed and creates bounding boxes around specific objects. You want to automate the following steps in your training pipeline:
                                      ingestion and preprocessing of data in Cloud Storage, followed by training and hyperparameter tuning of the object model using Vertex AI jobs, and finally deploying the model to an endpoint.
                                      You want to orchestrate the entire pipeline with minimal cluster management. What approach should you use?

                                      Antwort: B

                                      Begründung:
                                      If you use TensorFlow in an ML workflow that processes terabytes of structured data or text data, should use TFX. For other use cases, Kubeflow.
                                      https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline


                                      385. Frage
                                      You recently developed a wide and deep model in TensorFlow. You generated training datasets using a SQL script that preprocessed raw data in BigQuery by performing instance-level transformations of the data. You need to create a training pipeline to retrain the model on a weekly basis. The trained model will be used to generate daily recommendations. You want to minimize model development and training time. How should you develop the training pipeline?

                                      Antwort: A

                                      Begründung:
                                      * Explanation: TensorFlow Extended (TFX) is a platform for building end-to-end machine learning pipelines using TensorFlow. TFX provides a set of components that can be orchestrated using either the TFX SDK or Kubeflow Pipelines. TFX components can handle different aspects of the pipeline, such as data ingestion, data validation, data transformation, model training, model evaluation, model serving, and more. TFX components can also leverage other Google Cloud services, such as BigQuery, Dataflow, and Vertex AI.
                                      * Why not A: Using the Kubeflow Pipelines SDK to implement the pipeline is a valid option, but using the BigQueryJobOp component to run the preprocessing script is not optimal. This would require writing and maintaining a separate SQL script for data transformation, which could introduce inconsistencies and errors. It would also make it harder to reuse the same preprocessing logic for both training and serving.
                                      * Why not B: Using the Kubeflow Pipelines SDK to implement the pipeline is a valid option, but using the DataflowPythonJobOp component to preprocess the data is not optimal. This would require writing and maintaining a separate Python script for data transformation, which could introduce inconsistencies and errors. It would also make it harder to reuse the same preprocessing logic for both training and serving.
                                      * Why not D: Using the TensorFlow Extended SDK to implement the pipeline is a valid option, but implementing the preprocessing steps as part of the input_fn of the model is not optimal. This would make the preprocessing logic tightly coupled with the model code, which could reduce modularity and flexibility. It would also make it harder to reuse the same preprocessing logic for both training and serving.


                                      386. Frage
                                      Your team frequently creates new ML models and runs experiments. Your team pushes code to a single repository hosted on Cloud Source Repositories. You want to create a continuous integration pipeline that automatically retrains the models whenever there is any modification of the code. What should be your first step to set up the CI pipeline?

                                      Antwort: D

                                      Begründung:
                                      According to the web search results, Cloud Build1 is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Cloud Source Repositories2, Cloud Storage, GitHub, Bitbucket, or any publicly hosted Git repository. Cloud Build allows you to create and manage build triggers, which are automated workflows that run whenever a code change is pushed to your source repository. You can use Cloud Build triggers to automatically retrain your ML models whenever there is any modification of the code. Therefore, option B is the best way to set up the CI pipeline for the given use case, as it allows you to configure a Cloud Build trigger with the event set as "Push to a branch", which means the trigger will run whenever a new commit is pushed to a specific branch of your source repository. The other options are not relevant or optimal for this scenario. Reference:
                                      Cloud Build
                                      Cloud Source Repositories
                                      Google Professional Machine Learning Certification Exam 2023
                                      Latest Google Professional Machine Learning Engineer Actual Free Exam Questions


                                      387. Frage
                                      You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

                                      Antwort: A


                                      388. Frage
                                      ......

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