PMLE Drag & Drop — 10 Questions

Question 1

Drag and drop the steps to perform a hyperparameter tuning job on Vertex AI in the correct order.

Accepted Answer

Step 1: Define the search space. Step 2: Create and run the tuning job. Step 3: Monitor the job. Step 4: Select the best parameters.. Define the search space, then create and run the tuning job, monitor, and select the best parameters.

Answer

Step 1: Create and run the tuning job. Step 2: Define the search space. Step 3: Monitor the job. Step 4: Select the best parameters.

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Step 1: Define the search space. Step 2: Create and run the tuning job. Step 3: Select the best parameters. Step 4: Monitor the job.

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Step 1: Monitor the job. Step 2: Define the search space. Step 3: Create and run the tuning job. Step 4: Select the best parameters.

Question 2

Drag and drop the steps to set up a feature store for ML features using Vertex AI Feature Store in the correct order.

Accepted Answer

Define entity type and features, then ingest data, then serve, then monitor.. The correct sequence to set up a feature store using Vertex AI Feature Store is: first define the entity type and its features (schema), then ingest feature data into the store, then serve those features to models for predictions, and finally monitor the store for data quality, freshness, and drift. This ensures a logical flow from definition to consumption to observability.

Answer

Ingest data, then define entity type and features, then serve, then monitor.

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Define entity type and features, then serve, then ingest data, then monitor.

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Monitor, then define entity type and features, then ingest data, then serve.

Question 3

Drag and drop the steps to set up data lineage tracking for ML pipelines using Vertex AI Experiments in the correct order.

Accepted Answer

Initialize the Vertex AI SDK, then create an experiment, then log metrics and parameters, then record artifacts, then review lineage.. Start with SDK setup, then create an experiment, log metrics, record artifacts, and review lineage.

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Create an experiment, then initialize the SDK, then log metrics, then review lineage, then record artifacts.

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Initialize the SDK, then log metrics and parameters, then create an experiment, then record artifacts, then review lineage.

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Initialize the SDK, then create an experiment, then record artifacts, then log metrics, then review lineage.

Question 4

Drag and drop the steps to set up a BigQuery ML linear regression model for forecasting in the correct order.

Accepted Answer

Prepare training data → Create model → Evaluate model → Use model for predictions. For BigQuery ML linear regression, the correct order is: 1) Prepare training data by selecting and preprocessing features in a SQL query; 2) Create the model using `CREATE MODEL` with the training data; 3) Evaluate the model using `ML.EVALUATE` to check metrics like R²; 4) Use the model for predictions with `ML.PREDICT`. This sequence ensures the model is built on clean data, validated, and then applied.

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Prepare training data → Create model → Use model for predictions → Evaluate model

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Create model → Prepare training data → Evaluate model → Use model for predictions

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Prepare training data → Evaluate model → Create model → Use model for predictions

Question 5

Drag and drop the steps to deploy a trained TensorFlow model to Vertex AI Prediction in the correct order.

Accepted Answer

Export the model, then upload to GCS, then register as a model, then deploy to endpoint, then test. Export the model, upload to GCS, register as a model, deploy to endpoint, then test.

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Upload to GCS, then export the model, then register as a model, then deploy to endpoint, then test

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Export the model, then register as a model, then upload to GCS, then deploy to endpoint, then test

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Export the model, then upload to GCS, then deploy to endpoint, then register as a model, then test

Question 6

Drag and drop the steps to implement a CI/CD pipeline for ML models using Cloud Build and Vertex AI in the correct order.

Accepted Answer

Configure the trigger, then Define the pipeline, then Commit to trigger training and deployment. In a CI/CD pipeline for ML models using Cloud Build and Vertex AI, the correct order is: first, configure the trigger (e.g., a Cloud Build trigger linked to a source repository), then define the pipeline (e.g., using Cloud Build configuration file or Vertex AI Pipelines), and finally commit code changes to trigger the automated training and deployment. This ensures that the trigger is set up to detect changes and the pipeline is defined before execution.

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Define the pipeline, then Configure the trigger, then Commit to trigger training and deployment

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Commit, then Configure the trigger, then Define the pipeline

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Configure the trigger, then Commit, then Define the pipeline

Question 7

Drag and drop the steps to create and deploy a custom ML model on Vertex AI using a container in the correct order.

Accepted Answer

1. Build and push the container, 2. Register the model, 3. Deploy to an endpoint, 4. Test. The correct order to create and deploy a custom ML model on Vertex AI using a container is: First, build and push the container image to Artifact Registry. Next, register the model in Vertex AI Model Registry. Then, deploy the model to an endpoint using Vertex AI Endpoints. Finally, test the endpoint by sending prediction requests to verify it works.

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1. Register the model, 2. Build and push the container, 3. Deploy to an endpoint, 4. Test

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1. Build and push the container, 2. Deploy to an endpoint, 3. Register the model, 4. Test

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1. Build and push the container, 2. Register the model, 3. Test, 4. Deploy to an endpoint

Question 8

Drag and drop the steps to set up a distributed training job on Vertex AI using a custom container in the correct order.

Accepted Answer

Prepare training code and create Dockerfile, then build and push the container image to Artifact Registry, then configure the distributed training job on Vertex AI, then submit the job.. The correct sequence for setting up a distributed training job on Vertex AI using a custom container is to first prepare the training code and Dockerfile, then build and push the container image to a registry, then configure the job with the image URI and distributed settings, and finally submit the job. This order ensures that all dependencies are met at each step.

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Build and push the container image first, then write the training code and create Dockerfile, then configure the job, then submit.

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Configure the distributed training job on Vertex AI first, then prepare code and container, then push, then submit.

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Submit the job first, then build and push the container, then write code, then configure.

Question 9

Drag and drop the steps to set up model monitoring for drift detection on Vertex AI in the correct order.

Accepted Answer

Deploy model, then Enable monitoring, then Set thresholds, then Configure alerts, then Review. The correct order to set up model monitoring for drift detection on Vertex AI is: 1. Deploy model to an endpoint. 2. Enable monitoring on the deployed endpoint. 3. Set drift detection thresholds (e.g., for feature distributions or prediction outputs). 4. Configure alerts (e.g., email notifications). 5. Review monitoring results and alerts.

Answer

Deploy model, then Set thresholds, then Enable monitoring, then Configure alerts, then Review

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Enable monitoring, then Deploy model, then Set thresholds, then Configure alerts, then Review

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Deploy model, then Enable monitoring, then Configure alerts, then Set thresholds, then Review

Question 10

Drag and drop the steps to set up a batch prediction job using Vertex AI in the correct order.

Accepted Answer

Prepare input data, then register model, then create batch prediction job, then submit job, then retrieve results.. The correct sequence for setting up a batch prediction job using Vertex AI is to first prepare your input data, then register your model, create the batch prediction job, submit the job, and finally retrieve the results. This order ensures that all necessary resources are available before job creation and submission, and that results are only requested after job completion.

Answer

Register model, then prepare input data, then create batch prediction job, then submit job, then retrieve results.

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Create batch prediction job, then prepare input data, then register model, then submit job, then retrieve results.

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Prepare input data, then register model, then create batch prediction job, then retrieve results, then submit job.