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

SectionObjectives
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
          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
                  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
                          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
                                  ML pipeline automation and orchestration- Pipeline design
                                  • 1. Use Vertex AI Pipelines
                                    • 2. Build end-to-end ML pipelines

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                                      Google Professional Machine Learning Engineer Sample Questions (Q274-Q279):

                                      NEW QUESTION # 274
                                      You are creating an ML pipeline for data processing, model training, and model deployment that uses different Google Cloud services. You have developed code for each individual task, and you expect a high frequency of new files. You now need to create an orchestration layer on top of these tasks. You only want this orchestration pipeline to run if new files are present in your dataset in a Cloud Storage bucket. You also want to minimize the compute node costs. What should you do?

                                      Answer: C

                                      Explanation:
                                      Cloud Function to be triggered by Cloud storage trigger, and then deploy the Vertex AI pipeline.


                                      NEW QUESTION # 275
                                      You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

                                      Answer: D


                                      NEW QUESTION # 276
                                      You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?

                                      Answer: B


                                      NEW QUESTION # 277
                                      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?

                                      Answer: B


                                      NEW QUESTION # 278
                                      You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team's spending. How should you reduce your Google Cloud compute costs without impacting the model's performance?

                                      Answer: B


                                      NEW QUESTION # 279
                                      ......

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