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

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

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                                      Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Prüfungsfragen mit Lösungen (Q173-Q178):

                                      173. Frage
                                      You work at a bank You have a custom tabular ML model that was provided by the bank's vendor. The training data is not available due to its sensitivity. The model is packaged as a Vertex Al Model serving container which accepts a string as input for each prediction instance. In each string the feature values are separated by commas. You want to deploy this model to production for online predictions, and monitor the feature distribution over time with minimal effort What should you do?

                                      Antwort: C


                                      174. Frage
                                      You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use AI Platform's continuous evaluation service to ensure that the models have high accuracy on your test dataset. What should you do?

                                      Antwort: A

                                      Begründung:
                                      By extending your test dataset with images of the newer products, you can ensure that your ML models are still able to classify images of the older products accurately, even after they have been retrained with data from the newer products. This will help you to maintain a high level of accuracy for your visual search engine.


                                      175. Frage
                                      You work for an online grocery store. You recently developed a custom ML model that recommends a recipe when a user arrives at the website. You chose the machine type on the Vertex Al endpoint to optimize costs by using the queries per second (QPS) that the model can serve, and you deployed it on a single machine with 8 vCPUs and no accelerators.
                                      A holiday season is approaching and you anticipate four times more traffic during this time than the typical daily traffic You need to ensure that the model can scale efficiently to the increased demand. What should you do?

                                      Antwort: B

                                      Begründung:
                                      Vertex AI Endpoint is a service that allows you to serve your ML models online and scale them automatically. You can use Vertex AI Endpoint to deploy the custom ML model that you developed for recommending recipes to the users. You can maintain the same machine type on the endpoint, which is a single machine with 8 vCPUs and no accelerators. This machine type can optimize the costs by using the queries per second (QPS) that the model can serve. You can also configure the endpoint to enable autoscaling based on vCPU usage. Autoscaling is a feature that allows the endpoint to adjust the number of compute nodes based on the traffic demand. By enabling autoscaling based on vCPU usage, you can ensure that the endpoint can scale efficiently to the increased demand during the holiday season, without overprovisioning or underprovisioning the resources. You can also set up a monitoring job and an alert for CPU usage. Monitoring is a service that allows you to collect and analyze the metrics and logs from your Google Cloud resources. You can use Monitoring to monitor the CPU usage of your endpoint, which is an indicator of the load and performance of your model. You can also set up an alert for CPU usage, which is a feature that allows you to receive notifications when the CPU usage exceeds a certain threshold. By setting up a monitoring job and an alert for CPU usage, you can keep track of the health and status of your endpoint, and detect any issues or anomalies. If you receive an alert, you can investigate the cause by using the Monitoring dashboard, which provides a graphical interface for viewing and analyzing the metrics and logs from your endpoint. You can also use the Monitoring dashboard to troubleshoot and resolve the issues, such as adjusting the autoscaling parameters, optimizing the model, or updating the machine type. By using Vertex AI Endpoint, autoscaling, and Monitoring, you can ensure that the model can scale efficiently to the increased demand during the holiday season, and handle any issues or alerts that might arise. Reference:
                                      [Vertex AI Endpoint documentation]
                                      [Autoscaling documentation]
                                      [Monitoring documentation]
                                      [Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]


                                      176. Frage
                                      You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

                                      Antwort: C

                                      Begründung:
                                      Google has bought this software and support for this tool is not good. SQL can work in Cloud fusion pipelines too but I would prefer to use a single tool like Bigquery to both transform and store data.


                                      177. Frage
                                      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?

                                      Antwort: A

                                      Begründung:
                                      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 allowusers 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


                                      178. Frage
                                      ......

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