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| Section | Objectives |
|---|---|
| ML model development | - Evaluation
|
| Designing ML solutions | - Framing ML problems
|
| Data preparation and processing | - Data ingestion and pipelines
|
| ML pipeline automation and orchestration | - Pipeline design
|
| Deployment and operations | - Monitoring and maintenance
|
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NEW QUESTION # 325
You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?
Answer: D
NEW QUESTION # 326
You work for a bank You have been asked to develop an ML model that will support loan application decisions. You need to determine which Vertex Al services to include in the workflow You want to track the model's training parameters and the metrics per training epoch. You plan to compare the performance of each version of the model to determine the best model based on your chosen metrics. Which Vertex Al services should you use?
Answer: A
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "track the lineage of pipeline artifacts". Vertex ML Metadata2 is a service that allows you to store, query, and visualize metadata associated with your ML workflows, such as datasets, models, metrics, and executions. Vertex ML Metadata helps you track the provenance and lineage of your ML artifacts and understand the relationships between them. Vertex AI Experiments3 is a service that allows you to track and compare the results of your model training runs. Vertex AI Experiments automatically logs metadata such as hyperparameters, metrics, and artifacts for each training run. You can use Vertex AI Experiments to train your custom model using TensorFlow, PyTorch, XGBoost, or scikit-learn. Vertex AI TensorBoard4 is a service that allows you to visualize and monitor your ML experiments using TensorBoard, an open source tool for ML visualization. Vertex AI TensorBoard helps you track the model's training parameters and the metrics per training epoch, and compare the performance of each version of the model. Therefore, option C is the best way to determine which Vertex AI services to include in the workflow for the given use case. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Vertex ML Metadata
Vertex AI Experiments
Vertex AI TensorBoard
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
NEW QUESTION # 327
You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?
Answer: D
Explanation:
This type of model is well-suited to predicting inventory levels because it can take into account trends and patterns in the data over time, such as seasonal fluctuations in demand or changes in customer behavior.
NEW QUESTION # 328
You work for a bank with strict data governance requirements. You recently implemented a custom model to detect fraudulent transactions You want your training code to download internal data by using an API endpoint hosted in your projects network You need the data to be accessed in the most secure way, while mitigating the risk of data exfiltration. What should you do?
Answer: D
Explanation:
The best option for accessing internal data in the most secure way, while mitigating the risk of data exfiltration, is to enable VPC Service Controls for peerings, and add Vertex AI to a service perimeter. This option allows you to leverage the power and simplicity of VPC Service Controls to isolate and protect your data and services on Google Cloud. VPC Service Controls is a service that can create a secure perimeter around your Google Cloud resources, such as BigQuery, Cloud Storage, and Vertex AI. VPC Service Controls can help you prevent unauthorized access and data exfiltration from your perimeter, and enforce fine- grained access policies based on context and identity. Peerings are connections that can allow traffic to flow between different networks. Peerings can help you connect your Google Cloud network with other Google Cloud networks or external networks, and enable communication between your resources and services. By enabling VPC Service Controls for peerings, you can allow your training code to download internal data by using an API endpoint hosted in your project's network, and restrict the data transfer to only authorized networks and services. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can support various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, and deep neural networks. Vertex AI can also provide various tools and services for data analysis, model development, model deployment, model monitoring, and model governance. By adding Vertex AI to a service perimeter, you can isolate and protect your Vertex AI resources, such as models, endpoints, pipelines, and feature store, and prevent data exfiltration from your perimeter1.
The other options are not as good as option A, for the following reasons:
* Option B: Creating a Cloud Run endpoint as a proxy to the data, and using Identity and Access Management (IAM) authentication to secure access to the endpoint from the training job would require more skills and steps than enabling VPC Service Controls for peerings, and adding Vertex AI to a service perimeter. Cloud Run is a service that can run your stateless containers on a fully managed environment or on your own Google Kubernetes Engine cluster. Cloud Run can help you deploy and scale your containerized applications quickly and easily, and pay only for the resources you use. A Cloud Run endpoint is a URL that can expose your containerized application to the internet or to other Google Cloud services. A Cloud Run endpoint can help you access and invoke your application from anywhere, and handle the load balancing and traffic routing. A proxy is a server that can act as an intermediary between a client and a target server. A proxy can help you modify, filter, or redirect the requests and responses between the client and the target server, and provide additional functionality or security. IAM is a service that can manage access control for Google Cloud resources. IAM can help you define who (identity) has what access (role) to which resource, and enforce the access policies. By creating a Cloud Run endpoint as a proxy to the data, and using IAM authentication to secure access to the endpoint from the training job, you can access internal data by using an API endpoint hosted in your project's network, and restrict the data access to only authorized identities and roles. However, creating a Cloud Run endpoint as a proxy to the data, and using IAM authentication to secure access to the endpoint from the training job would require more skills and steps than enabling VPC Service Controls for peerings, and adding Vertex AI to a service perimeter. You would need to write code, create and configure the Cloud Run endpoint, implement the proxy logic, deploy and monitor the Cloud Run endpoint, and set up the IAM policies. Moreover, this option would not prevent data exfiltration from your network, as the Cloud Run endpoint can be accessed from outside your network2.
* Option C: Configuring VPC Peering with Vertex AI and specifying the network of the training job would not allow you to access internal data by using an API endpoint hosted in your project's network, and could cause errors or poor performance. VPC Peering is a service that can create a peering connection between two VPC networks. VPC Peering can help you connect your Google Cloud network with another Google Cloud network or an external network, and enable communication between your resources and services. By configuring VPC Peering with Vertex AI and specifying the network of the training job, you can allow your training code to access Vertex AI resources, such as models, endpoints, pipelines, and feature store, and use the same network for the training job. However, configuring VPC Peering with Vertex AI and specifying the network of the training job would not allow you to access internal data by using an API endpoint hosted in your project's network, and could cause errors or poor performance. You would need to write code, create and configure the VPC Peering connection, and specify the network of the training job. Moreover, this option would not isolate and protect your data and services on Google Cloud, as the VPC Peering connection can expose your network to other networks and services3.
* Option D: Downloading the data to a Cloud Storage bucket before calling the training job would not allow you to access internal data by using an API endpoint hosted in your project's network, and could increase the complexity and cost of the data access. Cloud Storage is a service that can store and manage your data on Google Cloud. Cloud Storage can help you upload and organize your data, and track the data versions and metadata. A Cloud Storage bucket is a container that can hold your data on Cloud Storage. A Cloud Storage bucket can help you store and access your data from anywhere, and provide various storage classes and options. By downloading the data to a Cloud Storage bucket before calling the training job, you can access the data from Cloud Storage, and use it as the input for the training job. However, downloading the data to a Cloud Storage bucket before calling the training job would not allow you to access internal data by using an API endpoint hosted in your project's network, and could increase the complexity and cost of the data access. You would need to write code, create and configure the Cloud Storage bucket, download the data to the Cloud Storage bucket, and call the training job. Moreover, this option would create an intermediate data source on Cloud Storage, which can increase the storage and transfer costs, and expose the data to unauthorized access or data exfiltration4.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 1: Data Engineering
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 1: Framing ML problems,
1.2 Defining data needs
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 2: Data Engineering, Section 2.2: Defining Data Needs
* VPC Service Controls
* Cloud Run
* VPC Peering
* Cloud Storage
NEW QUESTION # 329
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: C
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 # 330
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