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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. 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
              Topic 3: 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 4: 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 5: 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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                                      Exam Professional-Machine-Learning-Engineer Labs, Most Professional-Machine-Learning-Engineer Reliable Questions

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                                      Google Professional Machine Learning Engineer Sample Questions (Q10-Q15):

                                      NEW QUESTION # 10
                                      You are going to train a DNN regression model with Keras APIs using this code:

                                      How many trainable weights does your model have? (The arithmetic below is correct.)

                                      Answer: B


                                      NEW QUESTION # 11
                                      During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

                                      Answer: C

                                      Explanation:
                                      https://developers.google.com/machine-learning/crash-course/introduction-to-neural-networks/playground-exercises


                                      NEW QUESTION # 12
                                      You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to Al Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the Al Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model's final layer softmax threshold to increase precision?

                                      Answer: C

                                      Explanation:
                                      Precision and recall are two common metrics for evaluating the performance of a classification model.
                                      Precision measures the proportion of positive predictions that are correct, while recall measures the proportion of positive examples that are correctly predicted. Precision and recall are inversely related, meaning that increasing one will decrease the other, and vice versa. The trade-off between precision and recall depends on the goal and the cost of the classification problem1.
                                      For the use case of detecting whether posted images contain cars, precision is more important than recall, as the social media company wants to minimize the number of false positives, or images that are incorrectly labeled as containing cars. A high precision means that the model is confident and accurate in its positive predictions, while a low recall means that the model may miss some positive examples, or images that actually contain cars. The cost of missing some positive examples is lower than the cost of making wrong positive predictions, as the latter may affect the user experience and the reputation of the social media company.
                                      The softmax function is a function that transforms a vector of real numbers into a probability distribution over the possible classes. The softmax function is often used as the final layer of a neural network for multi-class classification problems, as it assigns a probability to each class, and the class with the highest probability is chosen as the prediction. The softmax function is defined as:
                                      softmax (x_i) = exp (x_i) / sum_j exp (x_j)
                                      where x_i is the input value for class i, and softmax (x_i) is the output probability for class i.
                                      The softmax threshold is a parameter that determines the minimum probability that a class must have to be chosen as the prediction. For example, if the softmax threshold is 0.5, then the class with the highest probability must have at least 0.5 to be selected, otherwise the prediction is none. The softmax threshold can be used to adjust the trade-off between precision and recall, as a higher threshold will increase the precision and decrease the recall, while a lower threshold will decrease the precision and increase the recall2.
                                      For the use case of detecting whether posted images contain cars, the best way to adjust the model's final layer softmax threshold to increase precision is to decrease the recall. This means that the softmax threshold should be increased, so that the model will only make positive predictions when it is highly confident, and avoid making false positives. By increasing the softmax threshold, the model will become more selective and accurate in its positive predictions, and improve the precision metric. Therefore, decreasing the recall is the best option for this use case.
                                      References:
                                      * Precision and recall - Wikipedia
                                      * How to add a threshold in softmax scores - Stack Overflow


                                      NEW QUESTION # 13
                                      You trained a model packaged it with a custom Docker container for serving, and deployed it to Vertex AI Model Registry. When you submit a batch prediction job, it fails with this error: "Error model server never became ready. Please validate that your model file or container configuration are valid. " There are no additional errors in the logs. What should you do?

                                      Answer: A


                                      NEW QUESTION # 14
                                      You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?

                                      Answer: B


                                      NEW QUESTION # 15
                                      ......

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