早速ダウンロードProfessional-Machine-Learning-Engineer最新試験情報 |最初の試行で簡単に勉強して試験に合格する &有効なProfessional-Machine-Learning-Engineer: Google Professional Machine Learning Engineer

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

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

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                                      Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q132-Q137):

                                      質問 # 132
                                      A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
                                      Which model will meet the business requirement?

                                      正解：D


                                      質問 # 133
                                      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?

                                      正解：B

                                      解説：
                                      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


                                      質問 # 134
                                      You work for a telecommunications company. You're building a model to predict which customers may fail to pay their next phone bill. The purpose of this model is to proactively offer at-risk customers assistance such as service discounts and bill deadline extensions. The data is stored in BigQuery and the predictive features that are available for model training include:
                                      - Customer_id
                                      - Age
                                      - Salary (measured in local currency)
                                      - Sex
                                      - Average bill value (measured in local currency)
                                      - Number of phone calls in the last month (integer)
                                      - Average duration of phone calls (measured in minutes)
                                      You need to investigate and mitigate potential bias against disadvantaged groups, while preserving model accuracy.
                                      What should you do?

                                      正解：C


                                      質問 # 135
                                      You need to build an object detection model for a small startup company to identify if and where the company's logo appears in an image. You were given a large repository of images, some with logos and some without. These images are not yet labelled. You need to label these pictures, and then train and deploy the model. What should you do?

                                      正解：A

                                      解説：
                                      A is correct as this will allow you to easily create a request for a labelling task and deploy a high- performance model.
                                      B is not correct because Vision API is not guaranteed to work with any company logos, and in the statement it explicitly mentions a small startup, which will further decrease the chance of success.
                                      C is not correct because the task of manually labelling the data is time consuming and should be avoided if possible.
                                      D is not correct because the task of labelling object detection data is very tedious, and real time object detection is designed detecting objects in videos rather than in images.
                                      https://cloud.google.com/ai-platform/data-labeling/docs


                                      質問 # 136
                                      You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

                                      正解：D


                                      質問 # 137
                                      ......

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