PMLE practice questions 211–220

1. Question 211 · 364 in bank

   You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on AI Platform for high-throughput online prediction. Which architecture should you use?

   • AValidate the accuracy of the model that you trained on preprocessed data. Create a new model that uses the raw data and is available in real time. Deploy the new model onto AI Platform for online prediction.
   • BSend incoming prediction requests to a Pub/Sub topic. Transform the incoming data using a Dataflow job. Submit a prediction request to AI Platform using the transformed data. Write the predictions to an outbound Pub/Sub queue.
   • CStream incoming prediction request data into Cloud Spanner. Create a view to abstract your preprocessing logic. Query the view every second for new records. Submit a prediction request to AI Platform using the transformed data. Write the predictions to an outbound Pub/Sub queue.
   • DSend incoming prediction requests to a Pub/Sub topic. Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic. Implement your preprocessing logic in the Cloud Function. Submit a prediction request to AI Platform using the transformed data. Write the predictions to an outbound Pub/Sub queue.

   Show answer

   Answer: B

   Explanation

   - using options eliminatios , A totally wrong , D also not valid as cloud functions is not sutiable for heavy data workflows - answer between B,D will vote for B as dataflow is the best solution while dealing with heavy data workflows

   Source: Adapted from community discussion — Mohamed_Mossad

   References

   • Google Cloud documentation

   Next question

2. Question 212 · 364 in bank

   You developed a Python module by using Keras to train a regression model. You developed two model architectures, linear regression and deep neural network (DNN), within the same module. You are using the training_method argument to select one of the two methods, and you are using the learning_rate and num_hidden_layers arguments in the DNN. You plan to use Vertex AI's hypertuning service with a budget to perform 100 trials. You want to identify the model architecture and hyperparameter values that minimize training loss and maximize model performance. What should you do?

   • ARun one hypertuning job for 100 trials. Set num_hidden_layers as a conditional hyperparameter based on its parent hyperparameter training_method, and set learning_rate as a non-conditional hyperparameter.
   • BRun two separate hypertuning jobs, a linear regression job for 50 trials, and a DNN job for 50 trials. Compare their final performance on a common validation set, and select the set of hyperparameters with the least training loss.
   • CRun one hypertuning job with training_method as the hyperparameter for 50 trials. Select the architecture with the lowest training loss, and further hypertune it and its corresponding hyperparameters tor 50 trials.
   • DRun one hypertuning job for 100 trials. Set num_hidden_layers and learning_rate as conditional hyperparameters based on their parent hyperparameter training_method.

   Show answer

   Answer: A

   Explanation

   A is the best option because: Running one single job with conditional logics added to hyperparameters settings avoids unnecessary computing usage and comparison efforts. Only num_hidden_layers needs to be set as a conditional hyperparameter under training_method; no explicit conditional logic is needed for learning_rate -- the latter is intelligently ignored by Vertex AI when linear regression is the training_method. B, C and D are less suitable because B and C run 2 separate jobs; D runs only one job, but it's hyperparameter tuning strategy adds redundant processing, even if it's true that the learning_rate is irrelevant for linear regression methods. The underlying logics behind it: As a STRUCTURAL hyperparameter, num_hidden_layers is intrinsically tied to the DNN's architecture definition. As a TRAINING hyperparameter, learning_rate is linked to the training process, not directly tied to the architecture definition.

   Source: Adapted from community discussion — lunalongo

   References

   • Google Cloud documentation

   Next question

3. Question 213 · 364 in bank

   You work for a hospital. You received approval to collect the necessary patient data, and you trained a Vertex AI tabular AutoML model that calculates patients' risk score for hospital admission. You deployed the model. However, you're concerned that patient demographics might change over time and alter the feature interactions and impact prediction accuracy. You want to be alerted if feature interactions change, and you want to understand the importance of the features for the predictions. You want your alerting approach to minimize cost. What should you do?

   • ACreate a feature drift monitoring job. Set the sampling rate to 1 and the monitoring frequency to weekly.
   • BCreate a feature drift monitoring job. Set the sampling rate to 0.1 and the monitoring frequency to weekly.
   • CCreate a feature attribution drift monitoring job. Set the sampling rate to 1 and the monitoring frequency to weekly.
   • DCreate a feature attribution drift monitoring job. Set the sampling rate to 0.1 and the monitoring frequency to weekly.

   Show answer

   Answer: D

   Explanation

   specifically concerned about changes in feature interactions and their impact on predictions. Feature attribution drift monitoring directly addresses this by tracking how the importance of different features (and their interactions) changes over time. https://cloud.google.com/vertex-ai/docs/model-monitoring/monitor-explainable-ai

   Source: Adapted from community discussion — Omi_04040

   References

   • Google Cloud documentation

   Next question

4. Question 214 · 364 in bank

   You are developing a TensorFlow Extended (TFX) pipeline with standard TFX components. The pipeline includes data preprocessing steps. After the pipeline is deployed to production, it will process up to 100 TB of data stored in BigQuery. You need the data preprocessing steps to scale efficiently, publish metrics and parameters to Vertex AI Experiments, and track artifacts by using Vertex ML Metadata. How should you configure the pipeline run?

   • ARun the TFX pipeline in Vertex AI Pipelines. Configure the pipeline to use Vertex AI Training jobs with distributed processing.
   • BRun the TFX pipeline in Vertex AI Pipelines. Set the appropriate Apache Beam parameters in the pipeline to run the data preprocessing steps in Dataflow.
   • CRun the TFX pipeline in Dataproc by using the Apache Beam TFX orchestrator. Set the appropriate Vertex AI permissions in the job to publish metadata in Vertex AI.
   • DRun the TFX pipeline in Dataflow by using the Apache Beam TFX orchestrator. Set the appropriate Vertex AI permissions in the job to publish metadata in Vertex AI.

   Show answer

   Answer: B

   Explanation

   A (Vertex AI Training jobs): While Vertex AI Training jobs are useful for model training, they are not the primary way to scale data preprocessing within a TFX pipeline. C and D (Dataproc and Dataflow with Apache Beam TFX orchestrator): While you can run TFX pipelines on Dataproc or Dataflow directly, using Vertex AI Pipelines as the orchestrator provides better integration with Vertex AI services and simplifies metadata tracking and experiment management.

   Source: Adapted from community discussion — AB_C

   Next question

5. Question 215 · 364 in bank

   You are developing a batch process that will train a custom model and perform predictions. You need to be able to show lineage for both your model and the batch predictions. What should you do?

   • A1. Upload your dataset to BigQuery. 2. Use a Vertex AI custom training job to train your model. 3. Generate predictions by using Vertex AI SDK custom prediction routines.
   • B1. Use Vertex AI Experiments to evaluate model performance during training. 2. Register your model in Vertex AI Model Registry. 3. Generate batch predictions in Vertex AI.
   • C1. Create a Vertex AI managed dataset. 2. Use a Vertex AI training pipeline to train your model. 3. Generate batch predictions in Vertex AI.
   • D1. Use a Vertex AI Pipelines custom training job component to train your model. 2. Generate predictions by using a Vertex AI Pipelines model batch predict component.

   Show answer

   Answer: D

   Explanation

   A (Vertex AI custom training job and custom prediction routines): This approach lacks the built-in lineage tracking capabilities of Vertex AI Pipelines. You would need to implement custom mechanisms to log and track the relevant metadata. B (Vertex AI Experiments and Model Registry): These are valuable tools, but they focus more on experiment management and model versioning. They don't provide the same level of workflow and lineage tracking as pipelines. C (Vertex AI managed dataset and batch prediction): While helpful, this doesn't provide the same level of granularity and traceability as pipelines for tracking the complete lineage, especially the training process.

   Source: Adapted from community discussion — AB_C

   Next question

6. Question 216 · 364 in bank

   You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model. What should you do?

   • ACreate a Vertex AI Workbench notebook. Use IPython magic to run the CREATE MODEL statement to create an ARIMA model.
   • BAccess BigQuery Studio in the Google Cloud console. Run the CREATE MODEL statement in the SQL editor to create an AutoML regression model.
   • CCreate a Vertex AI Workbench notebook. Use IPython magic to run the CREATE MODEL statement to create an AutoML regression model.
   • DAccess BigQuery Studio in the Google Cloud console. Run the CREATE MODEL statement in the SQL editor to create an ARIMA model.

   Show answer

   Answer: B

   Explanation

   A and C (Vertex AI Workbench): While Vertex AI Workbench is a powerful platform for ML development, it requires setting up a notebook environment and writing Python code, which adds complexity compared to using BigQuery ML directly. D (ARIMA model): ARIMA models are specifically designed for time series forecasting. While they might be applicable in some CLTV scenarios, AutoML Regression provides a more general and potentially more accurate solution for predicting CLTV based on various customer features.

   Source: Adapted from community discussion — AB_C

   Next question

7. Question 217 · 364 in bank

   You work at a retail company, and are tasked with developing an ML model to predict product sales. Your company’s historical sales data is stored in BigQuery and includes features such as date, store location, product category, and promotion details. You need to choose the most effective combination of a BigQuery ML model and feature engineering to maximize prediction accuracy. What should you do?

   • AUse a linear regression model. Perform one-hot encoding on categorical features, and create additional features based on the date, such as day of the week or month.
   • BUse a boosted tree model. Perform label encoding on categorical features, and transform the date column into numeric values.
   • CUse an autoencoder model. Perform label encoding on categorical features, and normalize the date column.
   • DUse a matrix factorization model. Perform one-hot encoding on categorical features, and create interaction features between the store location and product category variables.

   Show answer

   Answer: B

   Explanation

   B. Use a boosted tree model. Perform label encoding on categorical features, and transform the date column into numeric values.

   Source: Adapted from community discussion — strafer

   Next question

8. Question 218 · 364 in bank

   Your organization’s employee onboarding team wants you to build an interactive self-help tool for new employees. The tool needs to receive queries from users and provide answers from the organization’s internal documentation. This documentation is spread across standalone documents such as PDF files. You want to build a solution quickly while minimizing maintenance overhead. What should you do?

   • ACreate a custom chatbot user interface hosted on App Engine. Use Vertex AI to fine-tune a Gemini model on the organization’s internal documentation. Send users’ queries to the fine-tuned model by using the custom chatbot and return the model’s responses to the users.
   • BDeploy an internal website to a Google Kubernetes Engine (GKE) cluster. Build a search index by ingesting all of the organization’s internal documentation. Use Vertex AI Vector Search to implement a semantic search that retrieves results from the search index based on the query entered into the search box.
   • CUse Vertex AI Agent Builder to create an agent. Securely index the organization’s internal documentation to the agent’s datastore. Send users’ queries to the agent and return the agent’s grounded responses to the users.
   • DDeploy an internal website to a Google Kubernetes Engine (GKE) cluster. Organize the relevant internal documentation into sections. Collect user feedback on website content and store it in BigQuery. Request that the onboarding team regularly update the links based on user feedback.

   Show answer

   Answer: C

   Next question

9. Question 219 · 364 in bank

   You work for an ecommerce company that wants to automatically classify products in images to improve user experience. You have a substantial dataset of labeled images depicting various unique products. You need to implement a solution for identifying custom products that is scalable, effective, and can be rapidly deployed. What should you do?

   • ADevelop a rule-based system to categorize the images.
   • BUse a TensorFlow deep learning model that is trained on the image dataset.
   • CUse a pre-trained object detection model from Model Garden.
   • DUse AutoML Vision to train a model using the image dataset.

   Show answer

   Answer: D

   Next question

10. Question 220 · 364 in bank

    Your team is developing a customer support chatbot for a healthcare company that processes sensitive patient information. You need to ensure that all personally identifiable information (PII) captured during customer conversations is protected prior to storing or analyzing the data. What should you do?

    • AUse the Cloud Natural Language API to identify and redact PII in chatbot conversations.
    • BUse the Cloud Natural Language API to classify and categorize all data, including PII, in chatbot conversations.
    • CUse the DLP API to encrypt PII in chatbot conversations before storing the data.
    • DUse the DLP API to scan and de-identify PII in chatbot conversations before storing the data.

    Show answer

    Answer: D

    Next question

Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page

PMLE practice, question 211. 364 questions available.