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

SectionObjectives
Topic 1: 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 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: 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
                          Topic 4: ML pipeline automation and orchestration- Pipeline design
                          • 1. Use Vertex AI Pipelines
                            • 2. Build end-to-end ML pipelines
                              Topic 5: 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. Transform and preprocess datasets
                                    • 2. Feature selection and representation techniques

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                                      Google Professional Machine Learning Engineer Sample Questions (Q119-Q124):

                                      NEW QUESTION # 119
                                      You work for a company that is developing an application to help users with meal planning. You want to use machine learning to scan a corpus of recipes and extract each ingredient (e.g., carrot, rice, pasta) and each kitchen cookware (e.g., bowl, pot, spoon) mentioned. Each recipe is saved in an unstructured text file. What should you do?

                                      Answer: C


                                      NEW QUESTION # 120
                                      A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
                                      Which of the following will accomplish this? (Choose two.)

                                      Answer: B,D


                                      NEW QUESTION # 121
                                      You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company's weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter's published date and the user remains on the page for at least one minute.
                                      All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model's performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?

                                      Answer: B

                                      Explanation:
                                      The best option for monitoring the model to determine when retraining is necessary is to schedule a weekly query in BigQuery to compute the success metric. This option has the following advantages:
                                      * It allows the model performance to be evaluated regularly, based on the actual outcome of the recommendations. By computing the success metric, which is the percentage of articles that are opened within two days and read for at least one minute, you can measure how well the model is achieving its objective and compare it with the acceptable baseline.
                                      * It leverages the scalability and efficiency of BigQuery, which is a serverless, fully managed, and highly scalable data warehouse that can run complex queries over petabytes of data in seconds. By using BigQuery, you can access and analyze all the information needed to compute the success metric, such as the newsletter publication date, the article opening date, and the user reading time, without worrying about the infrastructure or the cost.
                                      * It simplifies the model monitoring and retraining workflow, as the weekly query can be scheduled and executed automatically using BigQuery's built-in scheduling feature. You can also set up alerts or notifications to inform you when the success metric falls below the acceptable baseline, and trigger the model retraining process accordingly.
                                      The other options are less optimal for the following reasons:
                                      * Option A: Using Vertex AI Model Monitoring to detect skew of the input features with a sample rate of
                                      100% and a monitoring frequency of two days introduces additional complexity and overhead. This option requires setting up and managing a Vertex AI Model Monitoring service, which is a managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. However, using Vertex AI Model Monitoring to detect skew of the input features may not reflect the actual performance of the model, as skew is the discrepancy between the distributions of the features in the training dataset and the serving data, which may not affect the outcome of the recommendations. Moreover, using a sample rate of 100% and a monitoring frequency of two days may incur unnecessary cost and latency, as it requires analyzing all the input features every two days, which may not be needed for the model monitoring.
                                      * Option B: Scheduling a cron job in Cloud Tasks to retrain the model every week before the newsletter is created introduces additional cost and risk. This option requires creating and running a cron job in Cloud Tasks, which is a fully managed service that allows you to schedule and execute tasks that are invoked by HTTP requests. However, using Cloud Tasks to retrain the model every week may not be optimal, as it may retrain the model more often than necessary, wasting compute resources and cost.
                                      Moreover, using Cloud Tasks to retrain the model before the newsletter is created may introduce risk, as it may deploy a new model version that has not been tested or validated, potentially affecting the quality of the recommendations.
                                      * Option D: Scheduling a daily Dataflow job in Cloud Composer to compute the success metric introduces additional complexity and cost. This option requires creating and running a Dataflow job in Cloud Composer, which is a fully managed service that runs Apache Airflow pipelines for workflow orchestration. Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. However, using Dataflow and Cloud Composer to compute the success metric may not be necessary, as it may add more steps and overhead to the model monitoring process. Moreover, using Dataflow and Cloud Composer to compute the success metric daily may not be optimal, as it may compute the success metric more often than needed, consuming more compute resources and cost.
                                      :
                                      [BigQuery documentation]
                                      [Vertex AI Model Monitoring documentation]
                                      [Cloud Tasks documentation]
                                      [Cloud Composer documentation]
                                      [Dataflow documentation]


                                      NEW QUESTION # 122
                                      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

                                      Explanation:
                                      To log the metrics of a machine learning model in TensorFlow using the Vertex AI Python SDK, you should utilize the aiplatform.log_metrics function to log the F1 score and aiplatform.log_classification_metrics function to log the confusion matrix. These functions allow users to manually record and store evaluation metrics for each model, facilitating an efficient comparison based on specific performance indicators like F1 scores and confusion matrices. References: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI and TensorFlow.
                                      * Vertex AI Python SDK reference | Google Cloud
                                      * Logging custom metrics | Vertex AI
                                      * Migrating from scikit-learn to TensorFlow | TensorFlow


                                      NEW QUESTION # 123
                                      You developed a Python module by using Keras to train a regression model. You developed two model architectures, linear regression and deep neural network (DNN). within the same module. You are using the - raining_method argument to select one of the two methods, and you are using the Learning_rate-and num_hidden_layers arguments in the DNN. You plan to use Vertex Al's hypertuning service with a Budget to perform 100 trials. You want to identify the model architecture and hyperparameter values that minimize training loss and maximize model performance What should you do?

                                      Answer: A


                                      NEW QUESTION # 124
                                      ......

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