PMLE practice questions 61–70

1. Question 61 · 364 in bank

   You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?

   • AModify the target variable using the Box-Cox transformation.
   • BZ-normalize all the numeric features.
   • COversample the fraudulent transaction 10 times.
   • DLog transform all numeric features.

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   Answer: C

   Explanation

   The answer is C beacause it's the only way to improve model performance. Box-Cox transformation: transform feature values according to normal distribution Z-normalization: transform feature values according to x_new = (x – μ) / σ (so {x_new} have mean 0 and std dev 1) Log transform: just log transformation Also, the Random Forest algorithm is not a distance-based model but it is a tree-based model, there's no need of normalization process.

   Source: Adapted from community discussion — Scipione_

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2. Question 62 · 364 in bank

   You are developing a classification model to support predictions for your company’s various products. The dataset you were given for model development has class imbalance You need to minimize false positives and false negatives What evaluation metric should you use to properly train the model?

   • AF1 score
   • BRecall
   • CAccuracy
   • DPrecision

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   Answer: A

   Explanation

   if there wasn't a class imbalance that C. Accuracy would have been the right answer. There A. F1-score which is harmonic mean of precision and recall, that balances the trade-off between precision and recall. It is useful when both false positives and false negatives are important as per the question at hand, and you want to optimize for both.

   Source: Adapted from community discussion — Antmal

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3. Question 63 · 364 in bank

   You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

   • AIncrease the instance memory to 512 GB, and increase the batch size.
   • BReplace the NVIDIA P100 GPU with a K80 GPU in the training job.
   • CEnable early stopping in your Vertex AI Training job.
   • DUse the tf.distribute.Strategy API and run a distributed training job.

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   Answer: D

   Explanation

   D. Use the tf.distribute.Strategy API and run a distributed training job. Here's why: A. Increase instance memory and batch size: This might not be helpful. While increasing memory could help with loading more images at once, the main bottleneck here is likely processing these large images. Increasing the batch size can worsen the problem by further straining the GPU's memory. B. Replace P100 with K80 GPU: A weaker GPU would likely slow down training instead of speeding it up. C. Enable early stopping: This can save time but might stop training before reaching optimal performance. D. Use tf.distribute.Strategy: This allows you to distribute the training workload across multiple GPUs or cores within your instance, significantly accelerating training without changing the model itself. This effectively leverages the available hardware efficiently.

   Source: Adapted from community discussion — guilhermebutzke

   References

   • developers.google.com documentation
   • Google Cloud documentation

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4. Question 64 · 364 in bank

   You recently developed a deep learning model. To test your new model, you trained it for a few epochs on a large dataset. You observe that the training and validation losses barely changed during the training run. You want to quickly debug your model. What should you do first?

   • AVerify that your model can obtain a low loss on a small subset of the dataset
   • BAdd handcrafted features to inject your domain knowledge into the model
   • CUse the Vertex AI hyperparameter tuning service to identify a better learning rate
   • DUse hardware accelerators and train your model for more epochs

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   Answer: A

   Explanation

   Isolating the Issue: Training on a small subset helps isolate the problem to the model itself rather than the entire training pipeline or large dataset. Efficiency: Debugging with a small dataset is faster, allowing you to iterate through potential solutions quicker. Identifying Fundamental Issues: If the model struggles to learn even on a small dataset, it indicates a more fundamental problem in the model architecture, data preprocessing, or learning algorithm.

   Source: Adapted from community discussion — fitri001

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5. Question 65 · 364 in bank

   You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company’s manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

   • ADevelop a custom TensorFlow regression model, and optimize it using Vertex AI Training.
   • BDevelop a regression model using BigQuery ML.
   • CDevelop a custom scikit-learn regression model, and optimize it using Vertex AI Training.
   • DDevelop a custom PyTorch regression model, and optimize it using Vertex AI Training.

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   Answer: B

   Explanation

   Scalability: BigQuery is a serverless data warehouse designed to handle massive datasets. It can efficiently process tens of millions of records daily for model training. Minimal Development Work: BigQuery ML offers built-in regression models like linear regression that you can train directly on your data stored in BigQuery. This eliminates the need for extensive custom code development with TensorFlow, PyTorch, or scikit-learn (options A, C, and D). Daily Training Runs: BigQuery ML allows scheduling queries for automated model training. You can set up a daily scheduled query to train your model on the latest data.

   Source: Adapted from community discussion — fitri001

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6. Question 66 · 364 in bank

   Your organization manages an online message board. A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive. Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

   • AAdd synthetic training data where those phrases are used in non-toxic ways.
   • BRemove the model and replace it with human moderation.
   • CReplace your model with a different text classifier.
   • DRaise the threshold for comments to be considered toxic or harmful.

   Show answer

   Answer: A

   Explanation

   I went fo A because it directly tackels the issue of misclassification and improving the models unterstanding of religious references. B and C don't make sense. D would generally reduce the number of comments flagged as toxic, which could decrease the false positive rate. However, this approach risks allowing genuinely harmful comments to go unflagged. It addresses the symptom (high false positive rate) rather than the underlying cause

   Source: Adapted from community discussion — 7cb0ab3

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7. Question 67 · 364 in bank

   You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your model's features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

   • AClassification
   • BReinforcement Learning
   • CRecurrent Neural Networks (RNN)
   • DConvolutional Neural Networks (CNN)

   Show answer

   Answer: C

   Explanation

   As Y2Data pointed out, your reasoning for choosing B does not make much sense. Furthermore, Reinforcement Learning for this question does not make much sense to me. Reinforcement Learning is basically agent - task problems. You give the agent a task i.e. get out of a maze and then through trial and error and many many iterations the agent learns the correct way to perform the task. It is called Reinforcement because you ... well ... reinforce the agent, you reward the agent for correct choices and penalize for incorrect choices. In RL you dont use many / any previous data because the data is generated with each iteration I think.

   Source: Adapted from community discussion — george_ognyanov

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8. Question 68 · 364 in bank

   You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company’s historical data as your training set, you created a TensorFlow model and deployed it to Vertex AI. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

   • AStream prediction results to BigQuery. Use BigQuery’s CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.
   • BUse Vertex Explainable AI. Submit each prediction request with the explain' keyword to retrieve feature attributions using the sampled Shapley method.
   • CUse Vertex AI Workbench user-managed notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.
   • DUse the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.

   Show answer

   Answer: B

   Explanation

   Feature Importance per Prediction: Vertex Explainable AI with the Shapley method provides feature attributions for each individual prediction. This allows you to understand which attributes were most influential in the model's decision for that specific customer. No Code Required: This approach leverages a built-in Vertex AI service and doesn't require writing additional code for Lasso regression (option C) or using the What-If tool (option D).

   Source: Adapted from community discussion — fitri001

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9. Question 69 · 364 in bank

   You are an ML engineer at a manufacturing company. You are creating a classification model for a predictive maintenance use case. You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks. Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly. You have trained several binary classifiers to predict whether the machine will fail, where a prediction of 1 means that the ML model predicts a failure. You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure. Which model should you choose?

   • AThe model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0.5
   • BThe model with the lowest root mean squared error (RMSE) and recall greater than 0.5.
   • CThe model with the highest recall where precision is greater than 0.5.
   • DThe model with the highest precision where recall is greater than 0.5.

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   Answer: C

   Explanation

   Prioritizing Detection: Recall measures how well the model identifies true positives (correctly predicts failures). A high recall ensures most imminent failures are caught. Balancing with Precision: Precision measures how many of the predicted failures are true positives (avoiding unnecessary maintenance). The requirement of a precision greater than 0.5 ensures a reasonable number of triggered maintenances actually address failures.

   Source: Adapted from community discussion — fitri001

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10. Question 70 · 364 in bank

    You built a custom ML model using scikit-learn. Training time is taking longer than expected. You decide to migrate your model to Vertex AI Training, and you want to improve the model’s training time. What should you try out first?

    • ATrain your model in a distributed mode using multiple Compute Engine VMs.
    • BTrain your model using Vertex AI Training with CPUs.
    • CMigrate your model to TensorFlow, and train it using Vertex AI Training.
    • DTrain your model using Vertex AI Training with GPUs.

    Show answer

    Answer: B

    Explanation

    B is correct, because scikit only has CPU support for the following services: - prebuilt containers for custom training (this is the case here) - prebuilt containers for predictions and explanations - Vertex AI Pipelines - Vertex AI Workbench user-managed notebooks https://cloud.google.com/vertex-ai/docs/supported-frameworks-list#scikit-learn_2

    Source: Adapted from community discussion — AnnaR

    References

    • Google Cloud documentation

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PMLE practice, question 61. 364 questions available.