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

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

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                                      Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Prüfungsfragen mit Lösungen (Q13-Q18):

                                      13. Frage
                                      You are training an ML model on a large dataset. You are using a TPU to accelerate the training process You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity. What should you do?

                                      Antwort: D

                                      Begründung:
                                      The best option for training an ML model on a large dataset, using a TPU to accelerate the training process, and discovering that the TPU is not reaching its full capacity, is to increase the batch size. This option allows you to leverage the power and simplicity of TPUs to train your model faster and more efficiently. A TPU is a custom-developed application-specific integrated circuit (ASIC) that can accelerate machine learning workloads. A TPU can provide high performance and scalability for various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, and deep neural networks. A TPU can also support various tools and frameworks, such as TensorFlow, PyTorch, and JAX. A batch size is a parameter that specifies the number of training examples in one forward/backward pass. A batch size can affect the speed and accuracy of the training process. A larger batch size can help you utilize the parallel processing power of the TPU, and reduce the communication overhead between the TPU and the host CPU. A larger batch size can also help you avoid overfitting, as it can reduce the variance of the gradient updates. By increasing the batch size, you can train your model on a large dataset faster and more efficiently, and make full use of the TPU capacity1.
                                      The other options are not as good as option D, for the following reasons:
                                      * Option A: Increasing the learning rate would not help you utilize the parallel processing power of the TPU, and could cause errors or poor performance. A learning rate is a parameter that controls how much the model is updated in each iteration. A learning rate can affect the speed and accuracy of the training process. A larger learning rate can help you converge faster, but it can also cause instability, divergence, or oscillation. By increasing the learning rate, you may not be able to find the optimal solution, and your model may perform poorly on the validation or test data2.
                                      * Option B: Increasing the number of epochs would not help you utilize the parallel processing power of the TPU, and could increase the complexity and cost of the training process. An epoch is a measure of the number of times all of the training examples are used once in the training process. An epoch can affect the speed and accuracy of the training process. A larger number of epochs can help you learn more from the data, but it can also cause overfitting, underfitting, or diminishing returns. By increasing the number of epochs, you may not be able to improve the model performance significantly, and your training process may take longer and consume more resources3.
                                      * Option C: Decreasing the learning rate would not help you utilize the parallel processing power of the TPU, and could slow down the training process. A learning rate is a parameter that controls how much the model is updated in each iteration. A learning rate can affect the speed and accuracy of the training process. A smaller learning rate can help you find a more precise solution, but it can also cause slow convergence or local minima. By decreasing the learning rate, you may not be able to reach the optimal solution in a reasonable time, and your training process may take longer2.
                                      References:
                                      * Preparing for Google Cloud Certification: Machine Learning Engineer, Course 2: ML Models and Architectures, Week 1: Introduction to ML Models and Architectures
                                      * Google Cloud Professional Machine Learning Engineer Exam Guide, Section 2: Architecting ML solutions, 2.1 Designing ML models
                                      * Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4: ML Models and Architectures, Section 4.1: Designing ML Models
                                      * Use TPUs
                                      * Triose phosphate utilization and beyond: from photosynthesis to end ...
                                      * Cloud TPU performance guide
                                      * Google TPU: Architecture and Performance Best Practices - Run


                                      14. Frage
                                      You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?

                                      Antwort: D

                                      Begründung:
                                      This type of model is well-suited to predicting inventory levels because it can take into account trends and patterns in the data over time, such as seasonal fluctuations in demand or changes in customer behavior.


                                      15. Frage
                                      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?

                                      Antwort: B

                                      Begründung:
                                      AutoML Tables is a service that allows you to automatically build and deploy state-of-the-art machine learning models on structured data without writing code. You can use AutoML Tables to perform the following steps for the classification task:
                                      * Exploratory data analysis: AutoML Tables provides a graphical user interface (GUI) and a command-line interface (CLI) to explore your data, visualize statistics, and identify potential issues.
                                      * Feature selection: AutoML Tables automatically selects the most relevant features for your model based on the data schema and the target column. You can also manually exclude or include features, or create new features from existing ones using feature engineering.
                                      * Model building: AutoML Tables automatically builds and evaluates multiple machine learning models using different algorithms and architectures. You can also specify the optimization objective, the budget, and the evaluation metric for your model.
                                      * Training and hyperparameter tuning: AutoML Tables automatically trains and tunes your model using the best practices and techniques from Google's research and engineering teams. You can monitor the training progress and the performance of your model on the GUI or the CLI.
                                      * Serving: AutoML Tables automatically deploys your model to a fully managed, scalable, and secure environment. You can use the GUI or the CLI to request predictions from your model, either online (synchronously) or offline (asynchronously).
                                      References:
                                      * [AutoML Tables documentation]
                                      * [AutoML Tables overview]
                                      * [AutoML Tables how-to guides]


                                      16. Frage
                                      You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

                                      Antwort: D


                                      17. Frage
                                      You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

                                      Antwort: C

                                      Begründung:
                                      Reference:
                                      https://developers.google.com/machine-learning/recommendation/collaborative/basics
                                      https://cloud.google.com/architecture/recommendations-using-machine-learning-on-compute-engine#filtering_the_data


                                      18. Frage
                                      ......

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