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Google Professional Machine Learning Engineer Sample Questions (Q103-Q108):

NEW QUESTION # 103
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?

A. Train local surrogate models to explain individual predictions.
B. Configure sampled Shapley explanations on Vertex Explainable AI.
C. Measure the effect of each feature as the weight of the feature multiplied by the feature value.
D. Configure integrated gradients explanations on Vertex Explainable AI.

Answer: B

Explanation:
Option A is incorrect because training local surrogate models to explain individual predictions is not a feature of Vertex Explainable AI, but rather a general technique for interpreting black-box models. Local surrogate models are simpler models that approximate the behavior of the original model around a specific input1.
Option B is correct because configuring sampled Shapley explanations on Vertex Explainable AI is a way to explain the difference between the actual prediction and the average prediction for a given input. Sampled Shapley explanations are based on the Shapley value, which is a game-theoretic concept that measures how much each feature contributes to the prediction2. Vertex Explainable AI supports sampled Shapley explanations for tabular data, such as customer churn3.
Option C is incorrect because configuring integrated gradients explanations on Vertex Explainable AI is not suitable for explaining the difference between the actual prediction and the average prediction for a given input. Integrated gradients explanations are based on the idea of computing the gradients of the prediction with respect to the input features along a path from a baseline input to the actual input4. Vertex Explainable AI supports integrated gradients explanations for image and text data, but not for tabular data3.
Option D is incorrect because measuring the effect of each feature as the weight of the feature multiplied by the feature value is not a valid way to explain the difference between the actual prediction and the average prediction for a given input. This method assumes that the model is linear and additive, which is not the case for an ensemble of trees and neural networks. Moreover, this method does not account for the interactions between features or the non-linearity of the model5.
Reference:
Local surrogate models
Shapley value
Vertex Explainable AI overview
Integrated gradients
Feature importance

NEW QUESTION # 104
You work for a retail company. You have a managed tabular dataset in Vertex Al that contains sales data from three different stores. The dataset includes several features such as store name and sale timestamp. You want to use the data to train a model that makes sales predictions for a new store that will open soon You need to split the data between the training, validation, and test sets What approach should you use to split the data?

A. Use Vertex Al default data split.
B. Use Vertex Al manual split, using the store name feature to assign one store for each set.
C. Use Vertex Al chronological split and specify the sales timestamp feature as the time vanable.
D. Use Vertex Al random split assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set.

Answer: B

NEW QUESTION # 105
You are developing a mode! to detect fraudulent credit card transactions. You need to prioritize detection because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to tram a model on users' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you adjust the training parameters in AutoML to improve model performance?
Choose 2 answers

A. Increase the score threshold.
B. Add more positive examples to the training set.
C. Decrease the score threshold.
D. Reduce the maximum number of node hours for training.
E. Add more negative examples to the training set.

Answer: B,C

Explanation:
The best options for adjusting the training parameters in AutoML to improve model performance are to decrease the score threshold and add more positive examples to the training set. These options can help increase the detection rate of fraudulent transactions, which is the priority for this use case. The score threshold is a parameter that determines the minimum probability score that a prediction must have to be classified as positive. Decreasing the score threshold can increase the recall of the model, which is the proportion of actual positive cases that are correctly identified. Increasing the recall can help reduce the number of false negatives, which are fraudulent transactions that aremissed by the model. However, decreasing the score threshold can also decrease the precision of the model, which is the proportion of positive predictions that are actually correct. Decreasing the precision can increase the number of false positives, which are legitimate transactions that are flagged as fraudulent by the model. Therefore, there is a trade-off between recall and precision, and the optimal score threshold depends on the business objective and the cost of errors1.
Adding more positive examples to the training set can help balance the data distribution and improve the model performance. Positive examples are the instances that belong to the target class, which in this case are fraudulent transactions. Negative examples are the instances that belong to the other class, which in this case are legitimate transactions. Fraudulent transactions are usually rare and imbalanced compared to legitimate transactions, which can cause the model to be biased towards the majority class and fail to learn the characteristics of the minority class. Adding more positive examples can help the model learn more features and patterns of the fraudulent transactions, and increase the detection rate2.
The other options are not as good as options B and C, for the following reasons:
* Option A: Increasing the score threshold would decrease the detection rate of fraudulent transactions, which is the opposite of the desired outcome. Increasing the score threshold would decrease the recall of the model, which is the proportion of actual positive cases that are correctly identified. Decreasing the recall would increase the number of false negatives, which are fraudulent transactions that are missed by the model. Increasing the score threshold would increase the precision of the model, which is the proportion of positive predictions that are actually correct. Increasing the precision would decrease the number of false positives, which are legitimate transactions that are flagged as fraudulent by the
* model. However, in this use case, the cost of false negatives is much higher than the cost of false positives, so increasing the score threshold is not a good option1.
* Option D: Adding more negative examples to the training set would not improve the model performance, and could worsen the data imbalance. Negative examples are the instances that belong to the other class, which in this case are legitimate transactions. Legitimate transactions are usually abundant and dominant compared to fraudulent transactions, which can cause the model to be biased towards the majority class and fail to learn the characteristics of the minority class. Adding more negative examples would exacerbate this problem, and decrease the detection rate of the fraudulent transactions2.
* Option E: Reducing the maximum number of node hours for training would not improve the model performance, and could limit the model optimization. Node hours are the units of computation that are used to train an AutoML model. The maximum number of node hours is a parameter that determines the upper limit of node hours that can be used for training. Reducing the maximum number of node hours would reduce the training time and cost, but also the model quality and accuracy. Reducing the maximum number of node hours would limit the number of iterations, trials, and evaluations that the model can perform, and prevent the model from finding the optimal hyperparameters and architecture3.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 5: Responsible AI, Week
4: Evaluation
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 2: Developing high-quality ML models, 2.2 Handling imbalanced data
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4:
Low-code ML Solutions, Section 4.3: AutoML
* Understanding the score threshold slider
* Handling imbalanced data sets in machine learning
* AutoML Vision pricing

NEW QUESTION # 106
You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer's loan request has been rejected by your model, and the bank's risks department is asking you to provide the reasons that contributed to the model's decision. What should you do?

A. Use local feature importance from the predictions.
B. Use the feature importance percentages in the model evaluation page.
C. Vary features independently to identify the threshold per feature that changes the classification.
D. Use the correlation with target values in the data summary page.

Answer: A

Explanation:
This would allow you to identify which features contributed the most to the model's decision, which would in turn help you to better explain why the model rejected the customer's loan request.
Varying features independently could also be a useful approach, as this would help to identify specific threshold values that change the classification. However, this strategy would be more time-consuming and would not provide as much insight into the inner-workings of the model compared to using local feature importance.

NEW QUESTION # 107
You work for a large social network service provider whose users post articles and discuss news. Millions of comments are posted online each day, and more than 200 human moderators constantly review comments and flag those that are inappropriate. Your team is building an ML model to help human moderators check content on the platform. The model scores each comment and flags suspicious comments to be reviewed by a human.
Which metric(s) should you use to monitor the model's performance?

A. Number of messages flagged by the model per minute confirmed as being inappropriate by humans.
B. Precision and recall estimates based on a random sample of 0.1% of raw messages each minute sent to a human for review
C. Precision and recall estimates based on a sample of messages flagged by the model as potentially inappropriate each minute
D. Number of messages flagged by the model per minute

Answer: C

Explanation:
* Precision measures the fraction of messages flagged by the model that are actually inappropriate, while recall measures the fraction of inappropriate messages that are flagged by the model. These metrics are useful for evaluating how well the model can identify and filter out inappropriate comments.
* Option A is not a good metric because it does not account for the accuracy of the model. The model might flag many messages that are not inappropriate, or miss many messages that are inappropriate.
* Option B is better than option A, but it still does not account for the recall of the model. The model might flag only a few messages that are highly likely to be inappropriate, but miss many other messages that are less obvious but still inappropriate.
* Option C is not a good metric because it does not focus on the messages that are flagged by the model.
The random sample of 0.1% of raw messages might contain very few inappropriate messages, making the precision and recall estimates unreliable.

NEW QUESTION # 108
......

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