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

SectionObjectives
Topic 1: 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 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- 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 4: 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
                                  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 (Q391-Q396):

                                      質問 # 391
                                      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?

                                      正解：C

                                      解説：
                                      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


                                      質問 # 392
                                      You work at a manufacturing company. You are building an assistant to help engineers write scripts for a proprietary, legacy machine control language called "MechScript." MechScript is highly specialized, uses unique syntax not found in public datasets, and is strictly internal to your company.
                                      You built the base Gemini model using Agent Platform RAG Engine and grounded the model with the MechScript reference manual. The model correctly answers questions about the language, but it consistently fails to generate syntactically correct code snippets, often hallucinating Python- like syntax instead. What should you do?

                                      正解：C

                                      解説：
                                      Supervised fine-tuning with high-quality MechScript input-output examples teaches the model the language's proprietary syntax and code-generation patterns. RAG supplies reference knowledge at inference time, but fine-tuning is more appropriate when the model must consistently produce outputs in a specialized format that is absent from its original training data.


                                      質問 # 393
                                      You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on-premises, and the data science workloads are native to PySpark. Your team plans to migrate their data science workloads to Google Cloud. You need to build a proof of concept to migrate one data science job to Google Cloud. You want to propose a migration process that requires minimal cost and effort. What should you do first?

                                      正解：C


                                      質問 # 394
                                      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：D

                                      解説：
                                      According to the official exam guide 1 , one of the skills assessed in the exam is to "automate and orchestrate ML pipelines using Cloud Composer". Vertex AI Pipelines 2 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 # 395
                                      A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
                                      Which storage option provides the most processing flexibility and will allow access control with IAM?

                                      正解：D

                                      解説：
                                      Explanation


                                      質問 # 396
                                      ......

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