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

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

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                                      Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q44-Q49):

                                      質問 # 44
                                      You developed a Vertex Al ML pipeline that consists of preprocessing and training steps and each set of steps runs on a separate custom Docker image Your organization uses GitHub and GitHub Actions as CI/CD to run unit and integration tests You need to automate the model retraining workflow so that it can be initiated both manually and when a new version of the code is merged in the main branch You want to minimize the steps required to build the workflow while also allowing for maximum flexibility How should you configure the CI/CD workflow?

                                      正解：C

                                      解説：
                                      The best option for automating the model retraining workflow is to use GitHub Actions and Cloud Build.
                                      GitHub Actions is a service that can create and run workflows for continuous integration and continuous delivery (CI/CD) on GitHub. GitHub Actions can run tests, build and deploy code, andtrigger other actions based on events such as code changes, pull requests, or manual triggers. Cloud Build is a service that can create and run scalable and reliable pipelines to build, test, and deploy software on Google Cloud. Cloud Build can build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines. Vertex AI Pipelines is a service that can orchestrate machine learning (ML) workflows using Vertex AI. Vertex AI Pipelines can run preprocessing and training steps on custom Docker images, and evaluate, deploy, and monitor the ML model. By using GitHub Actions and Cloud Build, users can leverage the power and flexibility of Google Cloud to automate the model retraining workflow, while minimizing the steps required to build the workflow.
                                      The other options are not as good as option D, for the following reasons:
                                      * Option A: Triggering a Cloud Build workflow to run tests, build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines would require more configuration and maintenance than using GitHub Actions and Cloud Build. Cloud Build is a service that can create and run pipelines to build, test, and deploy software on Google Cloud, but it is not designed to integrate with GitHub or other source code repositories. To trigger a Cloud Build workflow from GitHub, users would need to set up a webhook, a Cloud Pub/Sub topic, and a Cloud Function1. Moreover, Cloud Build does not support manual triggers, which limits the flexibility of the workflow2.
                                      * Option B: Triggering GitHub Actions to run the tests, launching a job on Cloud Run to build custom Docker images, pushing the images to Artifact Registry, and launching the pipeline in Vertex AI Pipelines would require more steps and resources than using GitHub Actions and Cloud Build. Cloud Run is a service that can run stateless containers on a fully managed environment or on Anthos. Cloud Run can build custom Docker images, but it is not optimized for this task. Users would need to write a Dockerfile, a cloudbuild.yaml file, and a Cloud Run service configuration file, and use the gcloud command-line tool to build and deploy the image3. Moreover, Cloud Run is designed for serving HTTP requests, not for running ML pipelines, which can have different performance and scalability requirements.
                                      * Option C: Triggering GitHub Actions to run the tests, building custom Docker images, pushing the images to Artifact Registry, and launching the pipeline in Vertex AI Pipelines would require more skills and tools than using GitHub Actions and Cloud Build. GitHub Actions can run tests and build code, but it is not specialized for building Docker images. Users would need to install and configure Docker on the GitHub Actions runner, write a Dockerfile, and use the docker command-line tool to build and push the image. Moreover, GitHub Actions has limitations on the disk space, memory, and CPU of the runner, which can affect the speed and reliability of the image building process.
                                      References:
                                      * Building CI/CD for Vertex AI pipelines: The first solution
                                      * Cloud Build
                                      * GitHub Actions
                                      * Vertex AI Pipelines
                                      * Triggering builds from GitHub
                                      * Triggering builds manually
                                      * Building containers
                                      * Cloud Run
                                      * [Building and testing Docker images with GitHub Actions]
                                      * [Usage limits, billing, and administration]


                                      質問 # 45
                                      You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

                                      正解：A

                                      解説：
                                      This is an important step in ensuring that the model has been developed and trained properly before it is put into production.
                                      Model performance monitoring is also a crucial step to ensure that the model is working as expected after it is released, and to identify areas where further refinement may be necessary.
                                      This would help to ensure that the model is performing well in production, and would also help to identify any issues that may arise over time.
                                      Additionally, this would allow the team to better understand what changes need to be made in order to help the model perform optimally in production.


                                      質問 # 46
                                      You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
                                      - Optimizer: SGD
                                      - Image shape = 224-224
                                      - Batch size = 64
                                      - Epochs = 10
                                      - Verbose =2
                                      During training you encounter the following error: ResourceExhaustedError: Out Of Memory (OOM) when allocating tensor. What should you do?

                                      正解：B

                                      解説：
                                      To fix memory overflow you need to reduce batch size also reduce input resolution is valid but reducing image size can harm model performance.


                                      質問 # 47
                                      You work for an online travel agency that also sells advertising placements on its website to other companies.
                                      You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor.
                                      You want to Implement the simplest solution. How should you configure the prediction pipeline?

                                      正解：C


                                      質問 # 48
                                      You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company's historical data as your training set, you created a TensorFlow model and deployed it to Vertex AI. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

                                      正解：C


                                      質問 # 49
                                      ......

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