Google pass4success newly Released Professional-machine-learning-engineer Exam Pdf by Osman q15 vce pdf

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Exam Name:	Google Professional Machine Learning Engineer
Exam Code:	Professional-Machine-Learning-Engineer Dumps
Vendor:	Google	Certification:	Machine Learning Engineer
Questions:	296 Q&A's	Shared By:	osman

Question 64

You have a custom job that runs on Vertex Al on a weekly basis The job is Implemented using a proprietary ML workflow that produces the datasets. models, and custom artifacts, and sends them to a Cloud Storage bucket Many different versions of the datasets and models were created Due to compliance requirements, your company needs to track which model was used for making a particular prediction, and needs access to the artifacts for each model. How should you configure your workflows to meet these requirement?

Options:

Configure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.

Create a Vertex Al experiment, and enable autologging inside the custom job

Use the Vertex Al Metadata API inside the custom Job to create context, execution, and artifacts for each model, and use events to link them together.

Register each model in Vertex Al Model Registry, and use model labels to store the related dataset and model information.

Discussion

Question 65

You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?

Options:

Retrain the model by using BigQuery ML. and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.

Retrain the model by using Vertex Al Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.

Alter the model by using BigQuery ML and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.

Export the model from BigQuery ML to Cloud Storage Import the model into Vertex Al Model Registry Deploy the model to a Vertex Al endpoint.

Discussion

Question 66

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?

Options:

Trigger a Cloud Build workflow to run tests build custom Docker images, push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.

Trigger GitHub Actions to run the tests launch a job on Cloud Run to build custom Docker images push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.

Trigger GitHub Actions to run the tests build custom Docker images push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.

Trigger GitHub Actions to run the tests launch a Cloud Build workflow to build custom Dicker images, push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.

Discussion

Answer:

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 F unction 1 . Moreover, Cloud Build does not support manual triggers, which limits the flexibility of the workflow 2 .

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 image 3 . 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], ]

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Question 67

Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API. You want to use the most efficient approach. What should you do?

Options:

1 Upload the audio files to Cloud Storage

2. Call the speech: Iongrunningrecognize API endpoint to generate transcriptions

3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions

1 Upload the audio files to Cloud Storage

2 Call the speech: Iongrunningrecognize API endpoint to generate transcriptions.

3 Create a Cloud Function that calls the Natural Language API by using the analyzesentiment method

1 Iterate over your local Tiles in Python

2. Use the Speech-to-Text Python library to create a speech.RecognitionAudio object and set the content to the audio file data

3. Call the speech: recognize API endpoint to generate transcriptions

4. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions

1 Iterate over your local files in Python