PMLE practice questions 261–270

1. Question 261 · 364 in bank

   You work for a large retailer and have been asked to segment your customers by their purchasing habits. The purchase history of all customers has been uploaded to BigQuery. You suspect that there may be several distinct customer segments, however you are unsure of how many, and you don’t yet understand the commonalities in their behavior. You want to find the most efficient solution. What should you do?

   • ACreate a k-means clustering model using BigQuery ML. Allow BigQuery to automatically optimize the number of clusters.
   • BCreate a new dataset in Dataprep that references your BigQuery table. Use Dataprep to identify similarities within each column.
   • CUse the Data Labeling Service to label each customer record in BigQuery. Train a model on your labeled data using AutoML Tables. Review the evaluation metrics to understand whether there is an underlying pattern in the data.
   • DGet a list of the customer segments from your company’s Marketing team. Use the Data Labeling Service to label each customer record in BigQuery according to the list. Analyze the distribution of labels in your dataset using Data Studio.

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   Answer: A

   Explanation

   ans: A, pretty sure. C, D => discarded, very time consuming. B => yes, you can identify similarities within each column, but when i read "you don’t yet understand the commonalities in their behavior" i understand that this job would be difficult, because there could be many columns to analyze, and i don't think that this would be efficient. A => BigQuery ML is compatible with kmeans clustering, it's easy and efficient to create, and i would automatically detect the number of clusters. Also from the BigQuery ML docs: "K-means clustering for data segmentation; for example, identifying customer segments." (Source: https://cloud.google.com/bigquery-ml/docs/introduction#supported_models_in)

   Source: Adapted from community discussion — wish0035

   References

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

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2. Question 262 · 364 in bank

   You recently designed and built a custom neural network that uses critical dependencies specific to your organization’s framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by AI Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

   • AUse a built-in model available on AI Platform Training.
   • BBuild your custom container to run jobs on AI Platform Training.
   • CBuild your custom containers to run distributed training jobs on AI Platform Training.
   • DReconfigure your code to a ML framework with dependencies that are supported by AI Platform Training.

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   Answer: C

   Explanation

   Answer C. By running your machine learning (ML) training job in a custom container, you can use ML frameworks, non-ML dependencies, libraries, and binaries that are not otherwise supported on Vertex AI. Model and your data are too large to fit in memory on a single machine hence distributed training jobs. https://cloud.google.com/vertex-ai/docs/training/containers-overview

   Source: Adapted from community discussion — mil_spyro

   References

   • Google Cloud documentation

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3. Question 263 · 364 in bank

   While monitoring your model training’s GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?

   • AIncrease the CPU load
   • BAdd caching to the pipeline
   • CIncrease the network bandwidth
   • DAdd parallel interleave to the pipeline

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   Answer: D

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4. Question 264 · 364 in bank

   Your data science team is training a PyTorch model for image classification based on a pre-trained RestNet model. You need to perform hyperparameter tuning to optimize for several parameters. What should you do?

   • AConvert the model to a Keras model, and run a Keras Tuner job.
   • BRun a hyperparameter tuning job on AI Platform using custom containers.
   • CCreate a Kuberflow Pipelines instance, and run a hyperparameter tuning job on Katib.
   • DConvert the model to a TensorFlow model, and run a hyperparameter tuning job on AI Platform.

   Show answer

   Answer: B

   Explanation

   ans: B A, D => too much work. C => not sure why you would complicate so much when Vertex AI has this feature in custom containers.

   Source: Adapted from community discussion — wish0035

   References

   • Google Cloud documentation
   • Google Cloud documentation

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5. Question 265 · 364 in bank

   You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?

   • AUse the Cloud Natural Language API to obtain metadata to classify the incoming cases.
   • BUse AutoML Natural Language to build and test a classifier. Deploy the model as a REST API.
   • CUse BigQuery ML to build and test a logistic regression model to classify incoming requests. Use BigQuery ML to perform inference.
   • DCreate a TensorFlow model using Google’s BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex AI.

   Show answer

   Answer: B

   Explanation

   ans: B A => no, you need customization. C, B => more work and complexity B => AutoML is easier and faster and "you need to quickly build, test, and deploy". Also the REST API part fits our use case.

   Source: Adapted from community discussion — wish0035

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6. Question 266 · 364 in bank

   You need to quickly build and train a model to predict the sentiment of customer reviews with custom categories without writing code. You do not have enough data to train a model from scratch. The resulting model should have high predictive performance. Which service should you use?

   • AAutoML Natural Language
   • BCloud Natural Language API
   • CAI Hub pre-made Jupyter Notebooks
   • DAI Platform Training built-in algorithms

   Show answer

   Answer: A

   Explanation

   Quickly ==> A and B and custom categories + you do not have enough data to train a model (it doesn't mean no data to train) it will probably have a few samples Let's say 10 samples) as this link https://cloud.google.com/natural-language/automl/docs/beginners-guide#include-enough-labeled-examples-in-each-category ==> A

   Source: Adapted from community discussion — John_Pongthorn

   References

   • Google Cloud documentation
   • Google Cloud documentation

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7. Question 267 · 364 in bank

   You need to build an ML model for a social media application to predict whether a user’s submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?

   • AUse AutoML to optimize the model’s recall in order to minimize false negatives.
   • BUse AutoML to optimize the model’s F1 score in order to balance the accuracy of false positives and false negatives.
   • CUse Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.
   • DUse Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.

   Show answer

   Answer: A

   Explanation

   The answer is A. The negative event is usually labeled as positive (e.g., fraud detection, customer default prediction, and here non-compliant picture identification). The question explicitly says, "ensure that the application does not falsely accept a non-compliant picture." So we should avoid falsely labeling a non-compliant image as compliant (negative). It is never mentioned in the question that false positives are also a concern. So, recall is better than F1-score for this problem.

   Source: Adapted from community discussion — [Removed]

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8. Question 268 · 364 in bank

   You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team’s spending. How should you reduce your Google Cloud compute costs without impacting the model’s performance?

   • AUse AI Platform to run distributed training jobs with checkpoints.
   • BUse AI Platform to run distributed training jobs without checkpoints.
   • CMigrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.
   • DMigrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.

   Show answer

   Answer: C

   Explanation

   C is the best approach because it allows you to reduce your compute costs without impacting the model's performance. Preemptible VMs are much cheaper than standard VMs, but they can be terminated at any time. By using checkpoints, you can ensure that your training job can be resumed if a preemptible VM is terminated. Also, even if training takes days, the checkpoints will prevent lossing the progress if preemtible VM are down.

   Source: Adapted from community discussion — joaquinmenendez

   References

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

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9. Question 269 · 364 in bank

   You need to train a regression model based on a dataset containing 50,000 records that is stored in BigQuery. The data includes a total of 20 categorical and numerical features with a target variable that can include negative values. You need to minimize effort and training time while maximizing model performance. What approach should you take to train this regression model?

   • ACreate a custom TensorFlow DNN model
   • BUse BQML XGBoost regression to train the model.
   • CUse AutoML Tables to train the model without early stopping.
   • DUse AutoML Tables to train the model with RMSLE as the optimization objective.

   Show answer

   Answer: B

   Explanation

   I recommend option D, Use AutoML Tables to train the model with RMSLE as the optimization objective. Using AutoML Tables to train the model can be a convenient and efficient way to minimize effort and training time while still maximizing model performance. In this case, using RMSLE as the optimization objective can be a good choice because it is a good fit for regression models with negative values in the target variable.

   Source: Adapted from community discussion — zeic

   References

   • Google Cloud documentation

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10. Question 270 · 364 in bank

    You are building a linear model with over 100 input features, all with values between –1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

    • AUse principal component analysis (PCA) to eliminate the least informative features.
    • BUse L1 regularization to reduce the coefficients of uninformative features to 0.
    • CAfter building your model, use Shapley values to determine which features are the most informative.
    • DUse an iterative dropout technique to identify which features do not degrade the model when removed.

    Show answer

    Answer: B

    Explanation

    A. PCA reconfigures the features, so no. C. After building your model, so no. D. Dropout should be in the model and it doesn't tell us which features are informative or not. Big No! For me, it's B.

    Source: Adapted from community discussion — ares81

    References

    • Google developer documentation
    • Google developer documentation

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PMLE practice, question 261. 364 questions available.