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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- Framing ML problems
          • 1. Define success metrics and evaluation criteria
            • 2. Translate business problems into ML tasks
              - ML architecture design
              • 1. Select appropriate ML models and approaches
                • 2. Design scalable ML systems on GCP
                  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. Feature selection and representation techniques
                        • 2. Transform and preprocess datasets
                          Topic 4: Deployment and operations- Monitoring and maintenance
                          • 1. Retraining and lifecycle management
                            • 2. Monitor model drift and performance
                              - Model deployment
                              • 1. Deploy models using Vertex AI endpoints
                                • 2. Batch and online prediction systems
                                  Topic 5: 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 (Q178-Q183):

                                      NEW QUESTION # 178
                                      You deployed a conversational application that uses a large language model (LLM). The application has 1,000 users. You collect user feedback about the verbosity and accuracy of the model 's responses. The user feedback indicates that the responses are factually correct but users want different levels of verbosity depending on the type of question. You want the model to return responses that are more consistent with users' expectations, and you want to use a scalable solution. What should you do?

                                      Answer: D

                                      Explanation:
                                      Modifying the prompt to explicitly include user expectations about verbosity for different scenarios allows the LLM to adapt responses dynamically without retraining. This prompt engineering approach is scalable, efficient, and aligned with best practices for controlling LLM behavior based on user needs.


                                      NEW QUESTION # 179
                                      A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.
                                      Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)

                                      Answer: D,E

                                      Explanation:
                                      Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/private-package-installation-in-amazon- sagemaker-running-in-internet-free-mode/


                                      NEW QUESTION # 180
                                      You work with a team of researchers to develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?

                                      Answer: C

                                      Explanation:
                                      A TPU VM is a virtual machine that has direct access to a Cloud TPU device. TPU VMs provide a simpler and more flexible way to use Cloud TPUs, as they eliminate the need for a separate host VM and network setup. TPU VMs also support interactive debugging tools such as TensorFlow Debugger (tfdbg) and Python Debugger (pdb), which can help researchers develop and troubleshoot complex models. A v3-8 TPU VM has
                                      8 TPU cores, which can provide high performance and scalability for training large models. SSHing into the TPU VM allows the user to run and debug the TensorFlow code directly on the TPU device, without any network overhead or data transfer issues. References:
                                      * 1: TPU VMs Overview
                                      * 2: TPU VMs Quickstart
                                      * 3: Debugging TensorFlow Models on Cloud TPUs


                                      NEW QUESTION # 181
                                      You recently trained a XGBoost model that you plan to deploy to production for online inference Before sending a predict request to your model's binary you need to perform a simple data preprocessing step This step exposes a REST API that accepts requests in your internal VPC Service Controls and returns predictions You want to configure this preprocessing step while minimizing cost and effort What should you do?

                                      Answer: C

                                      Explanation:
                                      * Option A is not the best answer because it requires storing the pickled model in Cloud Storage, which may incur additional cost and latency for loading the model. It also requires building a Flask-based app, which may not be necessary for a simple data preprocessing step.
                                      * Option B is not the best answer because it requires building a Flask-based app, which may not be necessary for a simple data preprocessing step. It also requires packaging the app andthe pickled model in a custom container image, which may increase the size and complexity of the image.
                                      * Option C is not the best answer because it requires packaging the pickled model in a custom container image, which may increase the size and complexity of the image. It also does not leverage the Vertex built-in container image, which may provide some optimizations and integrations for XGBoost models.
                                      * Option D is the best answer because it leverages the Vertex built-in container image, which may provide some optimizations and integrations for XGBoost models. It also allows storing the pickled model in Cloud Storage, which may reduce the size and complexity of the image. It also allows building a custom predictor class based on XGBoost Predictor from the Vertex AI SDK, which may simplify the data preprocessing step and the prediction logic.


                                      NEW QUESTION # 182
                                      You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop. Model training will use a large batch size, and you expect training to take several weeks. You need to configure a training architecture that minimizes both training time and compute costs. What should you do?

                                      Answer: D


                                      NEW QUESTION # 183
                                      ......

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