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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development

The following will be discussed in Google Professional-Machine-Learning-Engineer Exam Dumps:

Model generalization
Training a model as a job in different environments
Build a model
Tracking metrics during training
Hardware accelerators
Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
Modeling techniques given interpretability requirements
Choice of framework and model
Overfitting
Model performance against baselines, simpler models, and across the time dimension
Model explainability on Cloud AI Platform
Retraining/redeployment evaluation
Scale model training and serving
Productionizing

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Professional-Machine-Learning-Engineer - Accurate Google Professional Machine Learning Engineer Latest Test Practice

The Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) certification exam is one of the top-rated career advancement certification exams. The Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) certification exam can play a significant role in career success. With the Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) certification you can gain several benefits such as validation of skills, career advancement, competitive advantage, continuing education, and global recognition of your skills and knowledge. The Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) certification is a valuable credential that assists you to enhance your existing skills and experience.

Google Professional Machine Learning Engineer Certification Exam is designed to test the skills and knowledge of individuals who are experts in the field of machine learning. Google Professional Machine Learning Engineer certification exam is a comprehensive test that covers a wide range of topics related to machine learning, such as data preparation, model building, model deployment, and monitoring. It is intended for individuals who have experience in developing and deploying machine learning models at scale.

Google Professional Machine Learning Engineer Sample Questions (Q240-Q245):

NEW QUESTION # 240
You developed a custom model by using Vertex AI to predict your application's user churn rate.
You are using Vertex AI Model Monitoring for skew detection. The training data stored in BigQuery contains two sets of features - demographic and behavioral. You later discover that two separate models trained on each set perform better than the original model. You need to configure a new model monitoring pipeline that splits traffic among the two models. You want to use the same prediction-sampling-rate and monitoring-frequency for each model. You also want to minimize management effort. What should you do?

A. Separate the training dataset into two tables based on demographic and behavioral features.
Deploy both models to the same endpoint, and submit a Vertex AI Model Monitoring job with a monitoring-config-from-file parameter that accounts for the model IDs and training datasets.
B. Keep the training dataset as is. Deploy both models to the same endpoint and submit a Vertex AI Model Monitoring job with a monitoring-config-from-file parameter that accounts for the model IDs and feature selections.
C. Keep the training dataset as is. Deploy the models to two separate endpoints, and submit two Vertex AI Model Monitoring jobs with appropriately selected feature-thresholds parameters.
D. Separate the training dataset into two tables based on demographic and behavioral features.
Deploy the models to two separate endpoints, and submit two Vertex AI Model Monitoring jobs.

Answer: A

NEW QUESTION # 241
A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively.
How should the Specialist address this issue and what is the reason behind it?

A. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
B. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
C. The learning rate should be increased because the optimization process was trapped at a local minimum.
D. The dropout rate at the flatten layer should be increased because the model is not generalized enough.

Answer: A

Explanation:
Explanation/Reference: https://www.tensorflow.org/tutorials/keras/overfit_and_underfit

NEW QUESTION # 242
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 AI Platform. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

A. Use the AI Explanations feature on AI Platform. Submit each prediction request with the 'explain' keyword to retrieve feature attributions using the sampled Shapley method.
B. Stream prediction results to BigQuery. Use BigQuery's CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.
C. Use AI Platform notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.
D. Use 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.

Answer: A

Explanation:
* Option A is incorrect because using AI Platform notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal, is not a suitable way to determine which customer attribute has the most predictive power for each prediction served by the model. Lasso regression is a method of feature selection that applies a penalty to the coefficients of the linear model, and shrinks them to zero for irrelevant features1. However, this method assumes that the model is linear and additive, which may not be the case for a TensorFlow model. Moreover, this method does not provide feature attributions for each prediction, but rather for the entire dataset.
* Option B is incorrect because streaming prediction results to BigQuery, and using BigQuery's CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable, is not a valid way to determine which customer attribute has the most predictive power for each prediction served by the model. The Pearson correlation coefficient is a measure of the linear relationship between two variables, ranging from -1 to 12. However, this method does not account for the interactions between features or the non-linearity of the model. Moreover, this method does not provide feature attributions for each prediction, but rather for the entire dataset.
* Option C is correct because using the AI Explanations feature on AI Platform, and submitting each prediction request with the 'explain' keyword to retrieve feature attributions using the sampled Shapley method, is the best way to determine which customer attribute has the most predictive power for each prediction served by the model. AI Explanations is a service that allows you to get feature attributions for your deployed models on AI Platform3. Feature attributions are values that indicate how much each feature contributed to the prediction for a given instance4. The sampled Shapley method is a technique that uses the Shapley value, a game-theoretic concept, to measure the contribution of each feature to the prediction5. By using AI Explanations, you can get feature attributions for each prediction request, and identify the most important features for each customer.
* Option D is incorrect because using the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded, and ranking the feature importance in order of those that caused the most significant performance drop when removed from the model, is not a practical way to determine which customer attribute has the most predictive power for each prediction served by the model. The What-If tool is a tool that allows you to visualize and analyze your ML models and datasets.
However, this method requires manually editing or removing features for each instance, and observing
* the change in the prediction. This method is not scalable or efficient, and may not capture the interactions between features or the non-linearity of the model.
References:
* Lasso regression
* Pearson correlation coefficient
* AI Explanations overview
* Feature attributions
* Sampled Shapley method
* [What-If tool overview]

NEW QUESTION # 243
You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?

A. Keep the original test dataset unchanged even if newer products are incorporated into retraining
B. Replace your test dataset with images of the newer products when they are introduced to retraining.
C. Extend your test dataset with images of the newer products when they are introduced to retraining
D. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.

Answer: B

NEW QUESTION # 244
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?

A. Traceability, reproducibility, and explainability
B. Differential privacy federated learning, and explainability
C. Redaction, reproducibility, and explainability
D. Federated learning, reproducibility, and explainability

Answer: A

Explanation:
https://www.oecd.org/finance/Impact-Big-Data-AI-in-the-Insurance-Sector.pdf
https://medium.com/artefact-engineering-and-data-science/including-ethics-best-practices-in-your-data-science-project-from-day-one-c15b26c2bf99

NEW QUESTION # 245
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