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

SectionObjectives
Topic 1: ML model development- Evaluation
  • 1. Evaluate model performance metrics
    • 2. Model validation strategies
      - Model training and tuning
      • 1. Train models using TensorFlow / Vertex AI
        • 2. Hyperparameter tuning and optimization
          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: Data preparation and processing- Data ingestion and pipelines
                  • 1. Build data pipelines for training and serving
                    • 2. Use BigQuery and data processing services
                      - Feature engineering
                      • 1. Transform and preprocess datasets
                        • 2. Feature selection and representation techniques
                          Topic 4: Deployment and operations- Model deployment
                          • 1. Batch and online prediction systems
                            • 2. Deploy models using Vertex AI endpoints
                              - Monitoring and maintenance
                              • 1. Retraining and lifecycle management
                                • 2. Monitor model drift and performance
                                  Topic 5: ML pipeline automation and orchestration- Pipeline design
                                  • 1. Build end-to-end ML pipelines
                                    • 2. Use Vertex AI Pipelines

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                                      Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q292-Q297):

                                      質問 # 292
                                      You developed a Vertex Al pipeline that trains a classification model on data stored in a large BigQuery table.
                                      The pipeline has four steps, where each step is created by a Python function that uses the KubeFlow v2 API The components have the following names:

                                      You launch your Vertex Al pipeline as the following:

                                      You perform many model iterations by adjusting the code and parameters of the training step. You observe high costs associated with the development, particularly the data export and preprocessing steps. You need to reduce model development costs. What should you do?

                                      正解：B

                                      解説：
                                      According to the official exam guide1, one of the skills assessed in the exam is to "automate and orchestrate ML pipelines using Cloud Composer". Vertex AI Pipelines2 is a service that allows you to orchestrate your ML workflows using Kubeflow Pipelines SDK v2 or TensorFlow Extended. Vertex AI Pipelines supports execution caching, which means that if you run a pipeline and it reaches a component that has already been run with the same inputs and parameters, the component does not run again. Instead, the component uses the output from the previous run. This can save you time and resources when you are iterating on your pipeline.
                                      Therefore, option A is the best way to reduce model development costs, as it enables execution caching for the data export and preprocessing steps, which are likely to be the same for each model iteration. The other options are not relevant or optimal for this scenario. References:
                                      * Professional ML Engineer Exam Guide
                                      * Vertex AI Pipelines
                                      * Google Professional Machine Learning Certification Exam 2023
                                      * Latest Google Professional Machine Learning Engineer Actual Free Exam Questions


                                      質問 # 293
                                      You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?

                                      正解：A

                                      解説：
                                      According to the web search results, Vertex AI Pipelines is a serverless orchestrator for running ML pipelines, using either the KFP SDK or TFX1. Vertex AI Pipelines provides a set of prebuilt components that can be used to perform common ML tasks, such as training, evaluation, deployment, and more2. Vertex AI ModelEvaluationOp and ModelDeployOp are two such components that can be used to evaluate and deploy a model to an endpoint for online inference3. However, Vertex AI Pipelines does not provide a prebuilt component for hyperparameter tuning. Therefore, to have control over how the model parameters are tuned, you need to use a custom component that calls the Vertex AI HyperparameterTuningJob service4. Therefore, option A is the best way to decide which Google Cloud pipeline components to use for the given use case, as it includes a custom component for hyperparameter tuning, and prebuilt components for model evaluation and deployment. The other options are not relevant or optimal for this scenario. References:
                                      * Vertex AI Pipelines
                                      * Google Cloud Pipeline Components
                                      * Vertex AI ModelEvaluationOp and ModelDeployOp
                                      * Vertex AI HyperparameterTuningJob
                                      * Google Professional Machine Learning Certification Exam 2023
                                      * Latest Google Professional Machine Learning Engineer Actual Free Exam Questions


                                      質問 # 294
                                      You are building a predictive maintenance model to preemptively detect part defects in bridges.
                                      You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?

                                      正解：A


                                      質問 # 295
                                      You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
                                      - Optimizer: SGD
                                      - Image shape = 224-224
                                      - Batch size = 64
                                      - Epochs = 10
                                      - Verbose =2
                                      During training you encounter the following error: ResourceExhaustedError: Out Of Memory (OOM) when allocating tensor. What should you do?

                                      正解：C

                                      解説：
                                      To fix memory overflow you need to reduce batch size also reduce input resolution is valid but reducing image size can harm model performance.


                                      質問 # 296
                                      You want to migrate a scikrt-learn classifier model to TensorFlow. You plan to train the TensorFlow classifier model using the same training set that was used to train the scikit-learn model and then compare the performances using a common test set. You want to use the Vertex Al Python SDK to manually log the evaluation metrics of each model and compare them based on their F1 scores and confusion matrices. How should you log the metrics?

                                      正解：A

                                      解説：
                                      To log the metrics of a machine learning model in TensorFlow using the Vertex AI Python SDK, you should utilize the aiplatform.log_metrics function to log the F1 score and aiplatform.
                                      log_classification_metrics function to log the confusion matrix. These functions allow users to manually record and store evaluation metrics for each model, facilitating an efficient comparison based on specific performance indicators like F1 scores and confusion matrices. References : The answer can be verified from official Google Cloud documentation and resources related to Vertex AI and TensorFlow.
                                      * Vertex AI Python SDK reference | Google Cloud
                                      * Logging custom metrics | Vertex AI
                                      * Migrating from scikit-learn to TensorFlow | TensorFlow


                                      質問 # 297
                                      ......

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