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NEW QUESTION 51 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?

• A. Add caching to the pipeline
• B. Increase the CPU load
• C. Add parallel interleave to the pipeline
• D. Increase the network bandwidth

Answer: B   NEW QUESTION 52 You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

• A. This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.
• B. This is a good result because the accuracy across both groups is greater than 80%.
• C. This is not a good result because the model is performing worse than predicting that people will always renew their subscription.
• D. This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.

Answer: D   NEW QUESTION 53 Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this: A) B) C) D)

• A. Option C
• B. Option B
• C. Option A
• D. Option D

Answer: B Explanation: If we just put inside the Training set , Validation set and Test set , randomly Text, Paragraph or sentences the model will have the ability to learn specific qualities about The Author's use of language beyond just his own articles. Therefore the model will mixed up different opinions. Rather if we divided things up a the author level, so that given authors were only on the training data, or only in the test data or only in the validation data. The model will find more difficult to get a high accuracy on the test validation (What is correct and have more sense!). Because it will need to really focus in author by author articles rather than get a single political affiliation based on a bunch of mixed articles from different authors. https://developers.google.com/machine-learning/crash-course/18th-century-literature For example, suppose you are training a model with purchase data from a number of stores. You know, however, that the model will be used primarily to make predictions for stores that are not in the training data. To ensure that the model can generalize to unseen stores, you should segregate your data sets by stores. In other words, your test set should include only stores different from the evaluation set, and the evaluation set should include only stores different from the training set. https://cloud.google.com/automl-tables/docs/prepare#ml-use   NEW QUESTION 54 ...... BONUS!!! Download part of UpdateDumps Professional-Machine-Learning-Engineer dumps for free: https://drive.google.com/open?id=1OoiWnKAUWdMvsPeSwDcId3GWOHY0vCAB