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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architect low-code AI solutions | 12% | - Identify use cases for low-code/no-code AI tools - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder - Apply responsible AI principles to low-code designs |
| Topic 2: Automate and orchestrate ML pipelines | 18% | - Design end-to-end ML workflows - Automate retraining and model updates - Implement CI/CD for ML systems - Use Vertex AI Pipelines, TFX, and other orchestration tools |
| Topic 3: Train and deploy models | 20% | - Configure training jobs and environments - Use Vertex AI deployment features and infrastructure - Deploy models for online, batch, and streaming prediction - Implement generative AI deployment patterns |
| Topic 4: Scale prototypes into AI models | 18% | - Optimize model performance and generalization - Design and run experiments - Select appropriate model architectures and frameworks - Work with foundation models and generative AI techniques |
| Topic 5: Collaborate to manage data and models | 16% | - Address data privacy, compliance, and governance - Manage datasets and features in Vertex AI - Organize and prepare enterprise data
|
| Topic 6: Monitor and optimize AI solutions | 16% | - Monitor model performance, fairness, and drift - Troubleshoot and maintain production systems - Optimize cost, latency, and resource usage - Monitor data quality and pipeline health |
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NEW QUESTION # 225
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?
Answer: A
Explanation:
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, and trigger 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]
NEW QUESTION # 226
You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don't overfit the model. What should you do?
Answer: A
Explanation:
The best option to ensure that the features with the largest magnitude don't overfit the model is to normalize the data by scaling it to have values between 0 and 1. This is also known as min-max scaling or feature scaling, and it can reduce the variance and skewness of the data, as well as improve the numerical stability and convergence of the model. Normalizing the data can also make the model less sensitive to the scale of the features, and more focused on the relative importance of each feature. Normalizing the data can be done using various methods, such as dividing each value by the maximum value, subtracting the minimum value and dividing by the range, or using the sklearn.preprocessing.MinMaxScaler function in Python.
The other options are not optimal for the following reasons:
A . Standardizing the data by transforming it with a logarithmic function is not a good option, as it can distort the distribution and relationship of the data, and introduce bias and errors. Moreover, the logarithmic function is not defined for negative or zero values, which can limit its applicability and cause problems for the model.
B . Applying a principal component analysis (PCA) to minimize the effect of any particular feature is not a good option, as it can reduce the interpretability and explainability of the data and the model. PCA is a dimensionality reduction technique that transforms the data into a new set of orthogonal features that capture the most variance in the data. However, these new features are not directly related to the original features, and can lose some information and meaning in the process. Moreover, PCA can be computationally expensive and complex, and may not be necessary for the problem at hand.
C . Using a binning strategy to replace the magnitude of each feature with the appropriate bin number is not a good option, as it can lose the granularity and precision of the data, and introduce noise and outliers. Binning is a discretization technique that groups the continuous values of a feature into a finite number of bins or categories. However, this can reduce the variability and diversity of the data, and create artificial boundaries and gaps that may not reflect the true nature of the data. Moreover, binning can be arbitrary and subjective, and depend on the choice of the bin size and number.
Reference:
Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud launches machine learning engineer certification Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization sklearn.preprocessing.MinMaxScaler documentation Principal Component Analysis Explained Visually Binning Data in Python
NEW QUESTION # 227
You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?
Answer: A
Explanation:
Vertex AI Feature Store is a service that allows you to store and manage your ML features on Google Cloud. You can use Vertex AI Feature Store to store features such as parcels delivered and truck locations over time, and retrieve them with low latency for online prediction. Online prediction is a type of prediction that provides low-latency responses to individual or small batches of input data. You can also use Vertex AI Feature Store to retrieve historical data at a specific point in time for model training. Model training is a process of learning the parameters of a ML model from data. By using Vertex AI Feature Store, you can store the features with minimal effort, and avoid the complexity of managing your own data storage and serving system. Reference:
Vertex AI Feature Store documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
NEW QUESTION # 228
You work for a manufacturing company.
You need to train a custom image classification model to detect product defects at the end of an assembly line Although your model is performing well some images in your holdout set are consistently mislabeled with high confidence.
You want to use Vertex Al to understand your model's results What should you do?
Answer: B
Explanation:
Vertex Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models, natively integrated with a number of Google's products and services 1
. With Vertex Explainable AI, you can generate feature-based explanations that show how much each input feature contributed to the model's prediction 2 . This can help you debug and improve your model performance, and build confidence in your model's behavior. Feature-based explanations are supported for custom image classification models deployed on Vertex AI Prediction 3 . References :
* Explainable AI | Google Cloud
* Introduction to Vertex Explainable AI | Vertex AI | Google Cloud
* Supported model types for feature-based explanations | Vertex AI | Google Cloud
NEW QUESTION # 229
You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?
Answer: A
Explanation:
Kubeflow pipelines have different types of components, ranging from low- to high-level. They have a ComponentStore that allows you to access prebuilt functionality from GitHub.
https://github.com/kubeflow/pipelines/blob/master/components/gcp/bigquery/query/sample.ipynb
https://v0-5.kubeflow.org/docs/pipelines/reusable-components/
NEW QUESTION # 230
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