PMLE practice questions 271–280

1. Question 271 · 364 in bank

   You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production. What is the most streamlined and reliable way to perform this validation?

   • AUse then TFX ModelValidator tools to specify performance metrics for production readiness.
   • BUse k-fold cross-validation as a validation strategy to ensure that your model is ready for production.
   • CUse the last relevant week of data as a validation set to ensure that your model is performing accurately on current data.
   • DUse the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.

   Show answer

   Answer: A

   Explanation

   A. Use the TFX ModelValidator tools to specify performance metrics for production readiness. TensorFlow Extended (TFX) ModelValidator is a useful tool for evaluating your model's performance on specific subsets of data before pushing it to production. It enables you to set specific performance metrics that the model should meet to be considered production-ready. ModelValidator can help ensure that your model is performing well on the different subsets of data that matter to your business, addressing the highly dynamic nature of customer behavior in the footwear retail context. Option C, using the last relevant week of data as a validation set, may not be sufficient to validate the model's performance on various subsets of data.

   Source: Adapted from community discussion — alejandroverger

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2. Question 272 · 364 in bank

   You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an “Out of Memory” error. What should you do?

   • AUse batch prediction mode instead of online mode.
   • BSend the request again with a smaller batch of instances.
   • CUse base64 to encode your data before using it for prediction.
   • DApply for a quota increase for the number of prediction requests.

   Show answer

   Answer: B

   Explanation

   @BenMS this feels like a trick question.... makes on to zone to the word batch. https://cloud.google.com/ai-platform/training/docs/troubleshooting .... states then when an error occurs with an online prediction request, you usually get an HTTP status code back from the service. These are some commonly encountered codes and their meaning in the context of online prediction: 429 - Out of Memory The processing node ran out of memory while running your model. There is no way to increase the memory allocated to prediction nodes at this time. You can try these things to get your model to run: Reduce your model size by: 1. Using less precise variables. 2. Quantizing your continuous data. 3. Reducing the size of other input features (using smaller vocab sizes, for example). 4. Send the request again with a smaller batch of instances.

   Source: Adapted from community discussion — Antmal

   References

   • Google Cloud documentation

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3. Question 273 · 364 in bank

   You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

   • ATrain local surrogate models to explain individual predictions.
   • BConfigure sampled Shapley explanations on Vertex Explainable AI.
   • CConfigure integrated gradients explanations on Vertex Explainable AI.
   • DMeasure the effect of each feature as the weight of the feature multiplied by the feature value.

   Show answer

   Answer: B

   Explanation

   Agree with B : individual instance prediction + ensemble of trees and neural networks (recommended model types for Sampled Shapley : "Non-differentiable models, such as ensembles of trees and neural networks " ). Check out the link below : https://cloud.google.com/vertex-ai/docs/explainable-ai/overview

   Source: Adapted from community discussion — JeanEl

   References

   • Google Cloud documentation
   • Google Cloud documentation

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4. Question 274 · 364 in bank

   You are working on a classification problem with time series data. After conducting just a few experiments using random cross-validation, you achieved an Area Under the Receiver Operating Characteristic Curve (AUC ROC) value of 99% on the training data. You haven’t explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

   • AAddress the model overfitting by using a less complex algorithm and use k-fold cross-validation.
   • BAddress data leakage by applying nested cross-validation during model training.
   • CAddress data leakage by removing features highly correlated with the target value.
   • DAddress the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

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   Answer: B

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5. Question 275 · 364 in bank

   You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline. What should you do?

   • AImport the TensorFlow model with BigQuery ML, and run the ml.predict function.
   • BUse the TensorFlow BigQuery reader to load the data, and use the BigQuery API to write the results to BigQuery.
   • CCreate a Dataflow pipeline to convert the data in BigQuery to TFRecords. Run a batch inference on Vertex AI Prediction, and write the results to BigQuery.
   • DLoad the TensorFlow SavedModel in a Dataflow pipeline. Use the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and write the results to BigQuery.

   Show answer

   Answer: A

   Explanation

   for this: https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-inference-overview Predict the label, either a numerical value for regression tasks or a categorical value for classification tasks on DNN regresion

   Source: Adapted from community discussion — enghabeth

   References

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

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6. Question 276 · 364 in bank

   You are creating a deep neural network classification model using a dataset with categorical input values. Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?

   • AConvert each categorical value into an integer value.
   • BConvert the categorical string data to one-hot hash buckets.
   • CMap the categorical variables into a vector of boolean values.
   • DConvert each categorical value into a run-length encoded string.

   Show answer

   Answer: B

   Explanation

   https://cloud.google.com/ai-platform/training/docs/algorithms/wide-and-deep If the column is categorical with high cardinality, then the column is treated with hashing, where the number of hash buckets equals to the square root of the number of unique values in the column.

   Source: Adapted from community discussion — CloudKida

   References

   • Google Cloud documentation
   • Google Cloud documentation

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7. Question 277 · 364 in bank

   You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?

   • ACreate a hot-encoding of words, and feed the encodings into your model.
   • BIdentify word embeddings from a pre-trained model, and use the embeddings in your model.
   • CSort the words by frequency of occurrence, and use the frequencies as the encodings in your model.
   • DAssign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model.

   Show answer

   Answer: B

   References

   • Google developer documentation
   • Google developer documentation
   • Google developer documentation
   • Google developer documentation

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8. Question 278 · 364 in bank

   You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

   • AConfigure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
   • BUse a model trained and deployed on BigQuery ML, and trigger retraining with the scheduled query feature in BigQuery.
   • CWrite a Cloud Functions script that launches a training and deploying job on AI Platform that is triggered by Cloud Scheduler.
   • DUse Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model.

   Show answer

   Answer: A

   Explanation

   Answer: A A. Kubeflow Pipelines can form an end-to-end architecture (https://www.kubeflow.org/docs/components/pipelines/overview/pipelines-overview/) and deploy models. B. BigQuery ML can't offer an end-to-end architecture because it must use another tool, like AI Platform, for serving models at the end of the process (https://cloud.google.com/bigquery-ml/docs/export-model-tutorial#online_deployment_and_serving). C. Cloud Scheduler can trigger the first step in a pipeline, but then some orchestrator is needed to continue the remaining steps. Besides, having Cloud Scheduler alone can't ensure failure handling during pipeline execution. D. A Dataflow job can't deploy models, it must use AI Platform at the end instead.

   Source: Adapted from community discussion — Paul_Dirac

   References

   • Google Cloud documentation
   • Google Cloud documentation

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9. Question 279 · 364 in bank

   Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

   • AVertex AI Pipelines and App Engine
   • BVertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring
   • CCloud Composer, BigQuery ML, and Vertex AI Prediction
   • DCloud Composer, Vertex AI Training with custom containers, and App Engine

   Show answer

   Answer: B

   Explanation

   The Cloud Compose may be good consideration if you are involved in getting Google Data Engineer Cert App enging is relevant to Dev-Op Cert Pls. if you know a bit about ML Google Cloud, we are preparing to take Google ML Cert, if there is no specifically particular requirement in the question. We must emphasize on use of Vertext AI as much as possible.

   Source: Adapted from community discussion — John_Pongthorn

   References

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

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10. Question 280 · 364 in bank

    You are profiling the performance of your TensorFlow model training time and notice a performance issue caused by inefficiencies in the input data pipeline for a single 5 terabyte CSV file dataset on Cloud Storage. You need to optimize the input pipeline performance. Which action should you try first to increase the efficiency of your pipeline?

    • APreprocess the input CSV file into a TFRecord file.
    • BRandomly select a 10 gigabyte subset of the data to train your model.
    • CSplit into multiple CSV files and use a parallel interleave transformation.
    • DSet the reshuffle_each_iteration parameter to true in the tf.data.Dataset.shuffle method.

    Show answer

    Answer: C

    Explanation

    Converting a large 5 terabyte CSV file to a TFRecord can be a time-consuming process, and you would still be dealing with a single large file.

    Source: Adapted from community discussion — pinimichele01

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PMLE practice, question 271. 364 questions available.