PMLE practice questions 81–90

1. Question 81 · 364 in bank

   You created an ML pipeline with multiple input parameters. You want to investigate the tradeoffs between different parameter combinations. The parameter options are • Input dataset • Max tree depth of the boosted tree regressor • Optimizer learning rate You need to compare the pipeline performance of the different parameter combinations measured in F1 score, time to train, and model complexity. You want your approach to be reproducible, and track all pipeline runs on the same platform. What should you do?

   • A1. Use BigQueryML to create a boosted tree regressor, and use the hyperparameter tuning capability. 2. Configure the hyperparameter syntax to select different input datasets: max tree depths, and optimizer learning rates. Choose the grid search option.
   • B1. Create a Vertex AI pipeline with a custom model training job as part of the pipeline. Configure the pipeline’s parameters to include those you are investigating. 2. In the custom training step, use the Bayesian optimization method with F1 score as the target to maximize.
   • C1. Create a Vertex AI Workbench notebook for each of the different input datasets. 2. In each notebook, run different local training jobs with different combinations of the max tree depth and optimizer learning rate parameters. 3. After each notebook finishes, append the results to a BigQuery table.
   • D1. Create an experiment in Vertex AI Experiments. 2. Create a Vertex AI pipeline with a custom model training job as part of the pipeline. Configure the pipeline’s parameters to include those you are investigating. 3. Submit multiple runs to the same experiment, using different values for the parameters.

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   Answer: D

   Explanation

   Vertex AI Experiments: This service allows you to group and track different pipeline runs associated with the same experiment. This facilitates comparing runs with various parameter combinations. Vertex AI Pipelines: Pipelines enable you to define a workflow for training your model. You can include a custom training step within the pipeline and configure its parameters as needed. This ensures reproducibility as all runs follow the same defined workflow. Submitting multiple runs: By submitting multiple pipeline runs to the same experiment with different parameter values, you can efficiently explore various configurations and track their performance metrics like F1 score, training time, and model complexity within Vertex AI Experiments.

   Source: Adapted from community discussion — fitri001

   References

   • Google Cloud documentation
   • Google Cloud documentation

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2. Question 82 · 364 in bank

   You received a training-serving skew alert from a Vertex AI Model Monitoring job running in production. You retrained the model with more recent training data, and deployed it back to the Vertex AI endpoint, but you are still receiving the same alert. What should you do?

   • AUpdate the model monitoring job to use a lower sampling rate.
   • BUpdate the model monitoring job to use the more recent training data that was used to retrain the model.
   • CTemporarily disable the alert. Enable the alert again after a sufficient amount of new production traffic has passed through the Vertex AI endpoint.
   • DTemporarily disable the alert until the model can be retrained again on newer training data. Retrain the model again after a sufficient amount of new production traffic has passed through the Vertex AI endpoint.

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   Answer: B

   Explanation

   the cause of the issue could be that the developer forgot to switch their monitoring job to the latest training dataset and the monitoring job still compares prod data with old training dataset and they of course have a skew

   Source: Adapted from community discussion — BlehMaks

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3. Question 83 · 364 in bank

   You developed a custom model by using Vertex AI to forecast the sales of your company’s products based on historical transactional data. You anticipate changes in the feature distributions and the correlations between the features in the near future. You also expect to receive a large volume of prediction requests. You plan to use Vertex AI Model Monitoring for drift detection and you want to minimize the cost. What should you do?

   • AUse the features for monitoring. Set a monitoring-frequency value that is higher than the default.
   • BUse the features for monitoring. Set a prediction-sampling-rate value that is closer to 1 than 0.
   • CUse the features and the feature attributions for monitoring. Set a monitoring-frequency value that is lower than the default.
   • DUse the features and the feature attributions for monitoring. Set a prediction-sampling-rate value that is closer to 0 than 1.

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   Answer: D

   Explanation

   Feature and Feature Attribution Monitoring: Since you anticipate changes in feature distributions and correlations, monitoring both features and their attributions provides a more comprehensive view of potential drift. Feature attributions explain how each feature contributes to the model's predictions. Monitoring them helps identify if these contributions are changing as expected. Lower Prediction Sampling Rate: This reduces the cost associated with Vertex AI Model Monitoring. The sampling rate determines the percentage of prediction requests used for monitoring calculations. A lower rate reduces the number of predictions analyzed, lowering monitoring costs. However, it's important to strike a balance between cost and having enough data for drift detection.

   Source: Adapted from community discussion — fitri001

   References

   • Google Cloud documentation

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4. Question 84 · 364 in bank

   You have recently trained a scikit-learn model that you plan to deploy on Vertex AI. This model will support both online and batch prediction. You need to preprocess input data for model inference. You want to package the model for deployment while minimizing additional code. What should you do?

   • A1. Upload your model to the Vertex AI Model Registry by using a prebuilt scikit-ieam prediction container. 2. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data.
   • B1. Wrap your model in a custom prediction routine (CPR). and build a container image from the CPR local model. 2. Upload your scikit learn model container to Vertex AI Model Registry. 3. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job
   • C1. Create a custom container for your scikit learn model. 2. Define a custom serving function for your model. 3. Upload your model and custom container to Vertex AI Model Registry. 4. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job.
   • D1. Create a custom container for your scikit learn model. 2. Upload your model and custom container to Vertex AI Model Registry. 3. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data.

   Show answer

   Answer: B

   Explanation

   I go with B: “Custom prediction routines (CPR) lets you build custom containers with pre/post processing code easily, without dealing with the details of setting up an HTTP server or building a container from scratch.” (https://cloud.google.com/vertex-ai/docs/predictions/custom-prediction-routines). This alone makes B preferable to C and D, provided lack of complex model architecture. Regarding A, pre-built containers only allow serving predictions, but not preprocessing of data (https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers#use_a_prebuilt_container). B thus remains the most likely option.

   Source: Adapted from community discussion — b1a8fae

   References

   • Google Cloud documentation
   • Google Cloud documentation

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5. Question 85 · 364 in bank

   You work for a food product company. Your company’s historical sales data is stored in BigQuery.You need to use Vertex AI’s custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. You plan to implement a data preprocessing algorithm that performs mm-max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost, and development effort. How should you configure this workflow?

   • AWrite the transformations into Spark that uses the spark-bigquery-connector, and use Dataproc to preprocess the data.
   • BWrite SQL queries to transform the data in-place in BigQuery.
   • CAdd the transformations as a preprocessing layer in the TensorFlow models.
   • DCreate a Dataflow pipeline that uses the BigQuerylO connector to ingest the data, process it, and write it back to BigQuery.

   Show answer

   Answer: B

   Explanation

   In-place Transformation: BigQuery allows you to perform data transformations directly within the data warehouse using SQL queries. This eliminates the need for data movement and reduces processing time compared to other options that involve data transfer. Minimized Development Effort: Since you're already familiar with SQL, writing queries for mm-max scaling and bucketing requires minimal additional development effort compared to learning and implementing new frameworks like Spark or Dataflow. Cost-Effective: BigQuery's serverless architecture scales processing power based on your workload. This can be more cost-effective than managing separate processing clusters like Dataproc.

   Source: Adapted from community discussion — fitri001

   References

   • Google Cloud documentation

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6. Question 86 · 364 in bank

   You have created a Vertex AI pipeline that includes two steps. The first step preprocesses 10 TB data completes in about 1 hour, and saves the result in a Cloud Storage bucket. The second step uses the processed data to train a model. You need to update the model’s code to allow you to test different algorithms. You want to reduce pipeline execution time and cost while also minimizing pipeline changes. What should you do?

   • AAdd a pipeline parameter and an additional pipeline step. Depending on the parameter value, the pipeline step conducts or skips data preprocessing, and starts model training.
   • BCreate another pipeline without the preprocessing step, and hardcode the preprocessed Cloud Storage file location for model training.
   • CConfigure a machine with more CPU and RAM from the compute-optimized machine family for the data preprocessing step.
   • DEnable caching for the pipeline job, and disable caching for the model training step.

   Show answer

   Answer: D

   Explanation

   According to this documentation cited: https://cloud.google.com/vertex-ai/docs/pipelines/configure-caching it is possible to write a pipeline setting True or False for each task component, like this: # Model training step with caching disabled train_model_task = train_model_op() train_model_task.set_caching_options(False) # Disable caching for this step # Model training step depends on the preprocessing step train_model_task.after(preprocess_task) So, with this, letter D is the best option. Furthermore, letter A and, Adding a pipeline parameter and an additional pipeline step introduces unnecessary complexity when caching can handle conditional execution efficiently and in letter C, configuring a machine with more CPU and RAM for preprocessing does not address the goal of minimizing pipeline changes and reducing execution time/cost effectively.

   Source: Adapted from community discussion — guilhermebutzke

   References

   • Google Cloud documentation

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7. Question 87 · 364 in bank

   You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well, and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible. What should you do?

   • ACreate an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI.
   • BCreate a BigQuery ML deep neural network model and use the ML.EXPLAIN_PREDICT method with the num_integral_steps parameter.
   • CUpload the custom model to Vertex AI Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.
   • DUpdate the custom serving container to include sampled Shapley-based explanations in the prediction outputs.

   Show answer

   Answer: C

   Explanation

   According to the documentation at https://cloud.google.com/vertex-ai/docs/explainable-ai/overview, we can utilize both feature-based attribution and sampled Shapley-based explanations. Therefore, for providing explanations for each prediction in a loan classification problem, I believe that feature-based attribution is the optimal approach. Furthermore, updating the custom serving container to include sampled Shapley-based explanations, as suggested in option D, might require more effort, considering that the custom model deployed on Vertex AI already provides this option for explanations.

   Source: Adapted from community discussion — guilhermebutzke

   References

   • Google Cloud documentation
   • Google Cloud documentation

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8. Question 88 · 364 in bank

   You recently used XGBoost to train a model in Python that will be used for online serving. Your model prediction service will be called by a backend service implemented in Golang running on a Google Kubernetes Engine (GKE) cluster. Your model requires pre and postprocessing steps. You need to implement the processing steps so that they run at serving time. You want to minimize code changes and infrastructure maintenance, and deploy your model into production as quickly as possible. What should you do?

   • AUse FastAPI to implement an HTTP server. Create a Docker image that runs your HTTP server, and deploy it on your organization’s GKE cluster.
   • BUse FastAPI to implement an HTTP server. Create a Docker image that runs your HTTP server, Upload the image to Vertex AI Model Registry and deploy it to a Vertex AI endpoint.
   • CUse the Predictor interface to implement a custom prediction routine. Build the custom container, upload the container to Vertex AI Model Registry and deploy it to a Vertex AI endpoint.
   • DUse the XGBoost prebuilt serving container when importing the trained model into Vertex AI. Deploy the model to a Vertex AI endpoint. Work with the backend engineers to implement the pre- and postprocessing steps in the Golang backend service.

   Show answer

   Answer: C

   Explanation

   My answer C: Considering pre- and postprocessing implementation, The option C directly deals with implementing the processing steps in a custom container, offering full control over their placement and execution. This documentation says: “Custom prediction routines (CPR) lets you build [custom containers](https://cloud.google.com/vertex-ai/docs/predictions/use-custom-container) with pre/post processing code easily, without dealing with the details of setting up an HTTP server or building a container from scratch.” https://cloud.google.com/vertex-ai/docs/predictions/custom-prediction-routines So, it is better to use C instead of A or B. D is better because it offers the option of pre and post-processing, which is not available in D due to its use of prebuilt serving.

   Source: Adapted from community discussion — guilhermebutzke

   References

   • Google Cloud documentation
   • Google Cloud documentation

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9. Question 89 · 364 in bank

   You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 20 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

   • AManually combine all columns that contain a time signal into an array. AIlow AutoML to interpret this array appropriately. Choose an automatic data split across the training, validation, and testing sets.
   • BSubmit the data for training without performing any manual transformations. AIlow AutoML to handle the appropriate transformations. Choose an automatic data split across the training, validation, and testing sets.
   • CSubmit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column. AIlow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets.
   • DSubmit the data for training without performing any manual transformations. Use the columns that have a time signal to manually split your data. Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing sets from 30 days after your validation set.

   Show answer

   Answer: D

   Explanation

   C You use the Time column to tell AutoML Tables that time matters for your data; it is not randomly distributed over time. When you specify the Time column, AutoML Tables use the earliest 80% of the rows for training, the next 10% of rows for validation, and the latest 10% of rows for testing. AutoML Tables treats each row as an independent and identically distributed training example; setting the Time column does not change this. The Time column is used only to split the data set. You must include a value for the Time column for every row in your dataset. Make sure that the Time column has enough distinct values, so that the evaluation and test sets are non-empty. Usually, having at least 20 distinct values should be sufficient. https://cloud.google.com/automl-tables/docs/prepare#time

   Source: Adapted from community discussion — DucLee3110

   References

   • Google Cloud documentation
   • Google Cloud documentation

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10. Question 90 · 364 in bank

    You recently deployed a pipeline in Vertex AI Pipelines that trains and pushes a model to a Vertex AI endpoint to serve real-time traffic. You need to continue experimenting and iterating on your pipeline to improve model performance. You plan to use Cloud Build for CI/CD You want to quickly and easily deploy new pipelines into production, and you want to minimize the chance that the new pipeline implementations will break in production. What should you do?

    • ASet up a CI/CD pipeline that builds and tests your source code. If the tests are successful, use the Google. Cloud console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex AI Pipelines.
    • BSet up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre-production environment. Run unit tests in the pre-production environment. If the tests are successful deploy the pipeline to production.
    • CSet up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment. After a successful pipeline run in the pre-production environment, deploy the pipeline to production.
    • DSet up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment. After a successful pipeline run in the pre-production environment, rebuild the source code and deploy the artifacts to production.

    Show answer

    Answer: C

    Explanation

    C. Pre-production environment: Deploying to a pre-production environment before production allows you to thoroughly test the new pipeline's functionality and performance without affecting real-time traffic. Successful pipeline run: This ensures the entire pipeline executes correctly in the pre-production environment, including training, model pushing, and endpoint deployment. No rebuild in production: Rebuilding the source code after a successful pre-production run is unnecessary and adds an extra step that could potentially introduce new errors.

    Source: Adapted from community discussion — ddogg

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PMLE practice, question 81. 364 questions available.