PMLE practice questions 101–110

1. Question 101 · 364 in bank

   You recently deployed a model to a Vertex AI endpoint. Your data drifts frequently, so you have enabled request-response logging and created a Vertex AI Model Monitoring job. You have observed that your model is receiving higher traffic than expected. You need to reduce the model monitoring cost while continuing to quickly detect drift. What should you do?

   • AReplace the monitoring job with a DataFlow pipeline that uses TensorFlow Data Validation (TFDV)
   • BReplace the monitoring job with a custom SQL script to calculate statistics on the features and predictions in BigQuery
   • CDecrease the sample_rate parameter in the RandomSampleConfig of the monitoring job
   • DIncrease the monitor_interval parameter in the ScheduleConfig of the monitoring job

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   Answer: C

   Explanation

   Reduced Monitoring Overhead: By decreasing the sample_rate, you instruct Vertex AI Model Monitoring to analyze a smaller percentage of incoming requests. This directly reduces the billing cost associated with monitoring. Fast Drift Detection: A well-chosen sampling rate can still provide enough data to capture significant data drift. Monitoring a smaller sample shouldn't significantly impact your ability to detect drift if it's happening rapidly.

   Source: Adapted from community discussion — fitri001

   References

   • Google Cloud documentation

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2. Question 102 · 364 in bank

   You work for a retail company. You have created a Vertex AI forecast model that produces monthly item sales predictions. You want to quickly create a report that will help to explain how the model calculates the predictions. You have one month of recent actual sales data that was not included in the training dataset. How should you generate data for your report?

   • ACreate a batch prediction job by using the actual sales data. Compare the predictions to the actuals in the report.
   • BCreate a batch prediction job by using the actual sales data, and configure the job settings to generate feature attributions. Compare the results in the report.
   • CGenerate counterfactual examples by using the actual sales data. Create a batch prediction job using the actual sales data and the counterfactual examples. Compare the results in the report.
   • DTrain another model by using the same training dataset as the original, and exclude some columns. Using the actual sales data create one batch prediction job by using the new model and another one with the original model. Compare the two sets of predictions in the report.

   Show answer

   Answer: B

   Explanation

   Feature Attribution: By enabling feature attributions in the batch prediction job, you gain insights into how each feature in the actual sales data contributes to the model's predictions. This information is crucial for explaining the model's reasoning to non-technical audiences. Direct Model Insights: Analyzing the feature attributions allows you to demonstrate how the model uses historical trends, seasonality, and other factors (represented by features) to predict future sales.

   Source: Adapted from community discussion — fitri001

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3. Question 103 · 364 in bank

   Your team has a model deployed to a Vertex AI endpoint. You have created a Vertex AI pipeline that automates the model training process and is triggered by a Cloud Function. You need to prioritize keeping the model up-to-date, but also minimize retraining costs. How should you configure retraining?

   • AConfigure Pub/Sub to call the Cloud Function when a sufficient amount of new data becomes available
   • BConfigure a Cloud Scheduler job that calls the Cloud Function at a predetermined frequency that fits your team’s budget
   • CEnable model monitoring on the Vertex AI endpoint. Configure Pub/Sub to call the Cloud Function when anomalies are detected
   • DEnable model monitoring on the Vertex AI endpoint. Configure Pub/Sub to call the Cloud Function when feature drift is detected

   Show answer

   Answer: D

   Explanation

   Data-driven Retraining: Monitoring for feature drift identifies significant changes in the underlying data distribution used to train the model. Retraining based on drift detection ensures the model stays relevant to evolving data patterns, prioritizing model accuracy. Reduced Cost: Triggering retraining only when drift is detected avoids unnecessary training runs, minimizing costs associated with Vertex AI training jobs.

   Source: Adapted from community discussion — fitri001

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4. Question 104 · 364 in bank

   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?

   • A1. Upload the audio files to Cloud Storage 2. Call the speech:longrunningrecognize API endpoint to generate transcriptions 3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions.
   • B1. Upload the audio files to Cloud Storage. 2. Call the speech:longrunningrecognize API endpoint to generate transcriptions 3. Create a Cloud Function that calls the Natural Language API by using the analyzeSentiment method
   • C1. Iterate over your local files 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.
   • D1. Iterate over your local files 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:longrunningrecognize API endpoint to generate transcriptions. 4. Call the Natural Language API by using the analyzeSentiment method

   Show answer

   Answer: B

   Explanation

   My answer: B According to https://cloud.google.com/vertex-ai/docs/text-data/sentiment-analysis/prepare-data, AutoML sentiment analysis requires a minimum of 10 labeled training documents per sentiment category, with a maximum of 100,000 total training documents. This means you need to ensure you have an adequate amount of labeled data to train a reliable model. Therefore, option B is more suitable since the API will return the sentiment and there is no mention of a customized problem that justifies the use of AutoML.

   Source: Adapted from community discussion — guilhermebutzke

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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5. Question 105 · 364 in bank

   You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories. You have a labeled dataset in Cloud Storage. You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency. What should you do?

   • ATrain the model by using AutoML, and register the model in Vertex AI Model Registry. Configure your mobile application to send batch requests during prediction.
   • BTrain the model by using AutoML Edge, and export it as a Core ML model. Configure your mobile application to use the .mlmodel file directly.
   • CTrain the model by using AutoML Edge, and export the model as a TFLite model. Configure your mobile application to use the .tflite file directly.
   • DTrain the model by using AutoML, and expose the model as a Vertex AI endpoint. Configure your mobile application to invoke the endpoint during prediction.

   Show answer

   Answer: B

   Explanation

   it's possible to use either Core ML or TF Lite, but since it's necessary to ensure the lowest possible latency, choose Core ML https://cloud.google.com/vertex-ai/docs/export/export-edge-model#classification

   Source: Adapted from community discussion — BlehMaks

   References

   • Google Cloud documentation

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6. Question 106 · 364 in bank

   You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company’s sales data, and created a table with the following rows: • Customer_id • Product_id • Date • Days_since_last_purchase (measured in days) • Average_purchase_frequency (measured in 1/days) • Purchase (binary class, if customer purchased product on the Date) You need to interpret your model’s results for each individual prediction. What should you do?

   • ACreate a BigQuery table. Use BigQuery ML to build a boosted tree classifier. Inspect the partition rules of the trees to understand how each prediction flows through the trees.
   • BCreate a Vertex AI tabular dataset. Train an AutoML model to predict customer purchases. Deploy the model to a Vertex AI endpoint and enable feature attributions. Use the “explain” method to get feature attribution values for each individual prediction.
   • CCreate a BigQuery table. Use BigQuery ML to build a logistic regression classification model. Use the values of the coefficients of the model to interpret the feature importance, with higher values corresponding to more importance
   • DCreate a Vertex AI tabular dataset. Train an AutoML model to predict customer purchases. Deploy the model to a Vertex AI endpoint. At each prediction, enable L1 regularization to detect non-informative features.

   Show answer

   Answer: B

   Explanation

   Individual Prediction Explanation: Vertex AI feature attributions provide insights into how each feature (e.g., days_since_last_purchase, average_purchase_frequency) contributes to a specific prediction for a customer-product combination. This allows you to understand the rationale behind the model's prediction for each instance. AutoML Convenience: AutoML simplifies model training without extensive configuration.

   Source: Adapted from community discussion — fitri001

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7. Question 107 · 364 in bank

   You work for a company that captures live video footage of checkout areas in their retail stores. You need to use the live video footage to build a model to detect the number of customers waiting for service in near real time. You want to implement a solution quickly and with minimal effort. How should you build the model?

   • AUse the Vertex AI Vision Occupancy Analytics model.
   • BUse the Vertex AI Vision Person/vehicle detector model.
   • CTrain an AutoML object detection model on an annotated dataset by using Vertex AutoML.
   • DTrain a Seq2Seq+ object detection model on an annotated dataset by using Vertex AutoML.

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   Answer: A

   Explanation

   My Answer: A: Vertex AI Vision Occupancy Analytics is a pre-trained model specifically designed to count people in live video streams. This removes the need for expensive and time-consuming data labeling and training, making it ideal for quick implementation. ****Vertex AI Vision Person/Vehicle Detector model detects individual people and vehicles, not specifically focusing on occupancy counting. It would require further processing to estimate the number of waiting customers. Option C and D requires labeling data and training, which adds effort and time. https://cloud.google.com/vision-ai/docs/overview

   Source: Adapted from community discussion — guilhermebutzke

   References

   • Google Cloud documentation
   • developers.google.com documentation

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8. Question 108 · 364 in bank

   You work as an analyst at a large banking firm. You are developing a robust scalable ML pipeline to tram several regression and classification models. Your primary focus for the pipeline is model interpretability. You want to productionize the pipeline as quickly as possible. What should you do?

   • AUse Tabular Workflow for Wide & Deep through Vertex AI Pipelines to jointly train wide linear models and deep neural networks
   • BUse Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models
   • CUse Tabular Workflow for TabNet through Vertex AI Pipelines to train attention-based models
   • DUse Cloud Composer to build the training pipelines for custom deep learning-based models

   Show answer

   Answer: C

   Explanation

   https://www.sciencedirect.com/science/article/pii/S0957417423000441 • When compared to XGBoost & GLM, TabNet provides better or comparable performance. • Unlike other Deep Learning models, TabNet is highly interpretable.

   Source: Adapted from community discussion — ddogg

   References

   • Google Cloud documentation

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9. Question 109 · 364 in bank

   You developed a Transformer model in TensorFlow to translate text. Your training data includes millions of documents in a Cloud Storage bucket. You plan to use distributed training to reduce training time. You need to configure the training job while minimizing the effort required to modify code and to manage the cluster’s configuration. What should you do?

   • ACreate a Vertex AI custom training job with GPU accelerators for the second worker pool. Use tf.distribute.MultiWorkerMirroredStrategy for distribution.
   • BCreate a Vertex AI custom distributed training job with Reduction Server. Use N1 high-memory machine type instances for the first and second pools, and use N1 high-CPU machine type instances for the third worker pool.
   • CCreate a training job that uses Cloud TPU VMs. Use tf.distribute.TPUStrategy for distribution.
   • DCreate a Vertex AI custom training job with a single worker pool of A2 GPU machine type instances. Use tf.distribute.MirroredStrategv for distribution.

   Show answer

   Answer: A

   Explanation

   Vertex AI custom training job: This leverages a managed service within GCP, reducing cluster configuration and management overhead. GPU accelerators for the second worker pool: This allows for distributed training across multiple GPUs, significantly speeding up training compared to a single worker pool. tf.distribute.MultiWorkerMirroredStrategy: This is a TensorFlow strategy specifically designed for distributed training on multiple machines. It minimizes code changes as it handles data parallelization and model replication across devices.

   Source: Adapted from community discussion — fitri001

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10. Question 110 · 364 in bank

    You are developing a process for training and running your custom model in production. You need to be able to show lineage for your model and predictions. What should you do?

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

    Show answer

    Answer: B

    Explanation

    B) REF https://cloud.google.com/vertex-ai/docs/pipelines/lineage Track the lineage of pipeline artifacts When you run a pipeline using Vertex AI Pipelines, the artifacts and parameters of your pipeline run are stored using Vertex ML Metadata. Vertex ML Metadata makes it easier to analyze the lineage of your pipeline's artifacts, by saving you the difficulty of keeping track of your pipeline's metadata. An artifact's lineage includes all the factors that contributed to its creation, as well as artifacts and metadata that are derived from this artifact. For example, a model's lineage could include the following: The training, test, and evaluation data used to create the model. The hyperparameters used during model training. Metadata recorded from the training and evaluation process, such as the model's accuracy. Artifacts that descend from this model, such as the results of batch predictions.

    Source: Adapted from community discussion — ddogg

    References

    • Google Cloud documentation

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PMLE practice, question 101. 364 questions available.