Free Access to Google.Professional-Machine-Learning-Engineer.v2024-01-19.q113 with Valid Practice Test (Page 3)

Question 6

You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

A.Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
B.Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
C.Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
D.Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.

Correct Answer: C

Developing ML models with AI Platform for image segmentation on CT scans requires a lot of computation and experimentation, as image segmentation is a complex and challenging task that involves assigning a label to each pixel in an image. Image segmentation can be used for various medical applications, such as tumor detection, organ segmentation, or lesion localization1 To minimize the computation costs and manual intervention while having version control for the code, one should use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository. Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Cloud Source Repositories, Cloud Storage, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives2 Cloud Build allows you to set up automated triggers that start a build when changes are pushed to a source code repository. You can configure triggers to filter the changes based on the branch, tag, or file path3 Cloud Source Repositories is a service that provides fully managed private Git repositories on Google Cloud Platform. Cloud Source Repositories allows you to store, manage, and track your code using the Git version control system. You can also use Cloud Source Repositories to connect to other Google Cloud services, such as Cloud Build, Cloud Functions, or Cloud Run4 To use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository, you need to do the following steps:
* Create a Cloud Source Repository for your code, and push your code to the repository. You can use the Cloud SDK, Cloud Console, or Cloud Source Repositories API to create and manage your repository5
* Create a Cloud Build trigger for your repository, and specify the build configuration and the trigger settings. You can use the Cloud SDK, Cloud Console, or Cloud Build API to create and manage your trigger.
* Specify the steps of the build in a YAML or JSON file, such as installing the dependencies, running the tests, building the container image, and submitting the training job to AI Platform. You can also use the
* Cloud Build predefined or custom build steps to simplify your build configuration.
* Push your new code to the repository, and the trigger will start the build automatically. You can monitor the status and logs of the build using the Cloud SDK, Cloud Console, or Cloud Build API.
The other options are not as easy or feasible. Using Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job is not ideal, as Cloud Functions has limitations on the memory, CPU, and execution time, and does not provide a user interface for managing and tracking your builds. Using the gcloud command-line tool to submit training jobs on AI Platform when you update your code is not optimal, as it requires manual intervention and does not leverage the benefits of Cloud Build and its integration with Cloud Source Repositories. Creating an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor is not relevant, as Cloud Composer is mainly designed for orchestrating complex workflows across multiple systems, and does not provide a version control system for your code.
References: 1: Image segmentation 2: Cloud Build overview 3: Creating and managing build triggers 4: Cloud Source Repositories overview 5: Quickstart: Create a repository : [Quickstart: Create a build trigger] :
[Configuring builds] : [Viewing build results]

Question 7

You work at a mobile gaming startup that creates online multiplayer games Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience. You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated Your model has performed well during testing, and you now need to deploy the model to production You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

A.Import the model into Vertex Al Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.
B.Save the model files in a Cloud Storage Bucket Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.
C.Save the model files in a VM Load the model files each time there is a prediction request and run an inference job on the VM.
D.Import the model into Vertex Al Model Registry Create a Vertex Al endpoint that hosts the model and make online inference requests.

Correct Answer: D

Online inference is a process where you send a single or a small number of prediction requests to a model and get immediate responses1. Online inference is suitable for scenarios where you need timely predictions, such as detecting cheating in online games. Online inference requires that the model is deployed to an endpoint, which is a resource that provides a service URL for prediction requests2.
Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models3. You can import models from various sources, such as custom models or AutoML models, and assign them to different versions and aliases3. You can also deploy models to endpoints, which are resources that provide a service URL for online prediction2.
By importing the model into Vertex AI Model Registry, you can leverage the Vertex AI features to monitor and update the model3. You can use Vertex AI Experiments to track and compare the metrics of different model versions, such as accuracy, precision, recall, and AUC. You can also use Vertex AI Explainable AI to generate feature attributions that show how much each input feature contributed to the model's prediction.
By creating a Vertex AI endpoint that hosts the model, you can use the Vertex AI Prediction service to serve online inference requests2. Vertex AI Prediction provides various benefits, such as scalability, reliability, security, and logging2. You can use the Vertex AI API or the Google Cloud console to send online inference requests to the endpoint and get immediate classifications4.
Therefore, the best option for your scenario is to import the model into Vertex AI Model Registry, create a Vertex AI endpoint that hosts the model, and make online inference requests.
The other options are not suitable for your scenario, because they either do not provide immediate classifications, such as using batch prediction or loading the model files each time, or they do not use Vertex AI Prediction, which would require more development and maintenance effort, such as creating a Cloud Function or a VM.
References:
* Online versus batch prediction | Vertex AI | Google Cloud
* Deploy a model to an endpoint | Vertex AI | Google Cloud
* Introduction to Vertex AI Model Registry | Google Cloud
* Get online predictions | Vertex AI | Google Cloud

Question 8

Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

A.Use the Natural Language API to classify support requests
B.Use AutoML Natural Language to build the support requests classifier
C.Use an established text classification model on Al Platform to perform transfer learning
D.Use an established text classification model on Al Platform as-is to classify support requests

Question 9

You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?

A.Use the Al Platform custom containers feature to receive training jobs using any framework
B.Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
C.Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob
D.Create a library of VM images on Compute Engine; and publish these images on a centralized repository

Question 10

You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

A.Build your custom containers to run distributed training jobs on Al Platform Training
B.Use a built-in model available on Al Platform Training
C.Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training
D.Build your custom container to run jobs on Al Platform Training

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