Professional-Machine-Learning-Engineer試験の準備方法｜信頼できるProfessional-Machine-Learning-Engineer対応内容試験｜ハイパスレートのGoogle Professional Machine Learning Engineer受験トレーリング

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

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

                                      >> Professional-Machine-Learning-Engineer対応内容 <<

                                      Professional-Machine-Learning-Engineer受験トレーリング & Professional-Machine-Learning-Engineer資格参考書

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                                      Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q258-Q263):

                                      質問 # 258
                                      You have recently developed a new ML model in a Jupyter notebook. You want to establish a reliable and repeatable model training process that tracks the versions and lineage of your model artifacts. You plan to retrain your model weekly. How should you operationalize your training process?

                                      正解：A

                                      解説：
                                      The best way to operationalize your training process is to use Vertex AI Pipelines, which allows you to create and run scalable, portable, and reproducible workflows for your ML models. Vertex AI Pipelines also integrates with Vertex AI Metadata, which tracks the provenance, lineage, and artifacts of your ML models. By using a Vertex AI CustomTrainingJobOp component, you can train your model using the same code as in your Jupyter notebook. By using a ModelUploadOp component, you can upload your trained model to Vertex AI Model Registry, which manages the versions and endpoints of your models. By using Cloud Scheduler and Cloud Functions, you can trigger your Vertex AI pipeline to run weekly, according to your plan. Reference:
                                      Vertex AI Pipelines documentation
                                      Vertex AI Metadata documentation
                                      Vertex AI CustomTrainingJobOp documentation
                                      ModelUploadOp documentation
                                      Cloud Scheduler documentation
                                      [Cloud Functions documentation]


                                      質問 # 259
                                      You developed a Vertex Al ML pipeline that consists of preprocessing and training steps and each set of steps runs on a separate custom Docker image Your organization uses GitHub and GitHub Actions as CI/CD to run unit and integration tests You need to automate the model retraining workflow so that it can be initiated both manually and when a new version of the code is merged in the main branch You want to minimize the steps required to build the workflow while also allowing for maximum flexibility How should you configure the CI/CD workflow?

                                      正解：A


                                      質問 # 260
                                      You have been given a dataset with sales predictions based on your company's marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the data. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks.
                                      You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?

                                      正解：D

                                      解説：
                                      * Option A is correct because using BigQuery ML to run several regression models, and analyze their performance is the most efficient and self-serviced way to complete the task. BigQuery ML is a service that allows you to create and use ML models within BigQuery using SQL queries1. You can use BigQuery ML to run different types of regression models, such as linear regression, logistic regression, or DNN regression2. You can also use BigQuery ML to analyze the performance of your models, such as the mean squared error, the accuracy, or the ROC curve3. BigQuery ML is fast, scalable, and easy to use, as it does not require any data movement, coding, or additional tools4.
                                      * Option B is incorrect because reading the data from BigQuery using Dataproc, and running several models using SparkML is not the most efficient and self-serviced way to complete the task. Dataproc is a service that allows you to create and manage clusters of virtual machines that run Apache Spark and other open-source tools5. SparkML is a library that provides ML algorithms and utilities for Spark.
                                      However, this option requires more effort and resources than option A, as it involves moving the data from BigQuery to Dataproc, creating and configuring the clusters, writing and running the SparkML code, and analyzing the results.
                                      * Option C is incorrect because using Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics is not the most efficient and self-serviced way to complete the task. Vertex AI Workbench is a service that allows you to create and use notebooks for ML development and experimentation. Scikit-learn is a library that provides ML algorithms and utilities for Python. However, this option also requires more effort and resources than option A, as it involves creating and managing the notebooks, writing and running the scikit-learn code, and analyzing the results.
                                      * Option D is incorrect because training a custom TensorFlow model with Vertex AI, reading the data
                                      * from BigQuery featuring a variety of ML algorithms is not the most efficient and self-serviced way to complete the task. TensorFlow is a framework that allows you to create and train ML models using Python or other languages. Vertex AI is a service that allows you to train and deploy ML models using built-in algorithms or custom containers. However, this option also requires more effort and resources than option A, as it involves writing and running the TensorFlow code, creating and managing the training jobs, and analyzing the results.
                                      References:
                                      * BigQuery ML overview
                                      * Creating a model in BigQuery ML
                                      * Evaluating a model in BigQuery ML
                                      * BigQuery ML benefits
                                      * Dataproc overview
                                      * [SparkML overview]
                                      * [Vertex AI Workbench overview]
                                      * [Scikit-learn overview]
                                      * [TensorFlow overview]
                                      * [Vertex AI overview]


                                      質問 # 261
                                      You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

                                      正解：B

                                      解説：
                                      https://cloud.google.com/automl-tables/docs/beginners-guide


                                      質問 # 262
                                      Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input dat a. How should you address the input differences in production?

                                      正解：A

                                      解説：
                                      Data drift doesn't necessarily require feature reselection (e.g. by L2 regularization). https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning#challenges Data values skews: These skews are significant changes in the statistical properties of data, which means that data patterns are changing, and you need to trigger a retraining of the model to capture these changes. https://developers.google.com/machine-learning/guides/rules-of-ml/#rule_37_measure_trainingserving_skew


                                      質問 # 263
                                      ......

                                      Google Professional Machine Learning Engineer衝動的にまたは考慮せずに何かを購入すると、望ましくない選択につながる可能性があります。 その結果を防ぐために,Google Professional Machine Learning Engineerトレーニング資料を用意しました。 これらは、保証期間中の専門的な練習資料です。 参考のために許容できる価格に加えて、3つのバージョンのすべての資料は、10年以上にわたってこの分野の専門家によって編集されています。 さらに、一連の利点があります。 したがって、Google Professional Machine Learning Engineerの実際のテストの重要性は言うまでもありません。 今すぐご注文いただいた場合、1年間無料の更新をお送りします。 これらのサプリメントはすべて、Google Professional Machine Learning EngineerのProfessional-Machine-Learning-Engineer模擬試験にも役立ちます。

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