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Google Professional Machine Learning Engineer Certification Exam is a highly prestigious certification offered by Google. It is designed for individuals who wish to showcase their expertise in the field of machine learning and demonstrate their ability to design, build, and deploy highly scalable and reliable machine learning models. Google Professional Machine Learning Engineer certification exam tests candidates on a variety of topics related to machine learning, including data preprocessing, feature engineering, model selection and evaluation, and deployment and monitoring of machine learning models.

Google Professional Machine Learning Engineer Sample Questions (Q101-Q106):

NEW QUESTION # 101
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?

A. Use AutoML Tables to train the model with RMSLE as the optimization objective
B. Use AutoML Tables to train the model without early stopping.
C. Create a custom TensorFlow DNN model.
D. Use BQML XGBoost regression to train the model

Answer: A

Explanation:
AutoML Tables is a service that allows you to automatically build, analyze, and deploy machine learning models on tabular data. It is suitable for large-scale regression and classification problems, and it supports various optimization objectives, data splitting methods, and hyperparameter tuning algorithms. AutoML Tables can handle both categorical and numerical features, and it can also handle missing values and outliers.
AutoML Tables is a good choice for this problem because it minimizes the effort and training time required to train a regression model, while maximizing the model performance.
RMSLE stands for Root Mean Squared Logarithmic Error, and it is a metric that measures the average difference between the logarithm of the predicted values and the logarithm of the actual values.RMSLE is useful for regression problems where the target variable can include negative values, and where large differences between small values are more important than large differences between large values. For example, RMSLE penalizes underestimating a value of 10 by 2 more than overestimating a value of 1000 by
20. RMSLE is a good optimization objective for this problem because it can handle negative values in the target variable, and it can reduce the impact of outliers and large errors.
For more information about AutoML Tables and RMSLE, see the following references:
* AutoML Tables: end-to-end workflows on AI Platform Pipelines
* Predict workload failures before they happen with AutoML Tables
* How to Calculate RMSE in R

NEW QUESTION # 102
You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company's sales data, and created a table with the following rows:
* Customer_id
* Product_id
* Date
* Days_since_last_purchase (measured in days)
* Average_purchase_frequency (measured in 1/days)
* Purchase (binary class, if customer purchased product on the Date)
You need to interpret your models results for each individual prediction. What should you do?

A. Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint. At each prediction enable L1 regularization to detect non-informative features.
B. Create a BigQuery table Use BigQuery ML to build a boosted tree classifier Inspect the partition rules of the trees to understand how each prediction flows through the trees.
C. Create a BigQuery table Use BigQuery ML to build a logistic regression classification model Use the values of the coefficients of the model to interpret the feature importance with higher values corresponding to more importance.
D. Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint and enable feature attributions Use the "explain" method to get feature attribution values for each individual prediction.

Answer: D

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "explain the predictions of a trained model". Vertex AI provides feature attributions using Shapley Values, a cooperative game theory algorithm that assigns credit to each feature in a model for a particular outcome2. Feature attributions can help you understand how the model calculates the predictions and debug or optimize the model accordingly. You can use AutoML for Tabular Data to generate and query local feature attributions3. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Feature attributions for classification and regression
AutoML for Tabular Data
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 103
You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

A. Use the Al Platform Training built-in algorithms to create a custom model
B. Use AutoML Natural Language to extract custom entities for classification
C. Use the Cloud Natural Language API to extract custom entities for classification
D. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm

Answer: A

NEW QUESTION # 104
You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?

A. Use the representation transformation (normalization) technique.
B. Use feature construction to combine the strongest features.
C. Improve the data cleaning step by removing features with missing values.
D. Change the partitioning step to reduce the dimension of the test set and have a larger training set.

Answer: C

NEW QUESTION # 105
You are an ML engineer at a travel company. You have been researching customers' travel behavior for many years, and you have deployed models that predict customers' vacation patterns. You have observed that customers' vacation destinations vary based on seasonality and holidays; however, these seasonal variations are similar across years. You want to quickly and easily store and compare the model versions and performance statistics across years. What should you do?

A. Create versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.
B. Store the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.
C. Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.
D. Store the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.

Answer: C

Explanation:
* Option A is incorrect because Cloud SQL is a relational database service that is not designed for storing and comparing model performance statistics. It would require writing complex SQL queries to perform the comparison, and it would not provide any visualization or analysis tools.
* Option B is incorrect because Vertex AI does not support creating versions of models for each season per year. Vertex AI models are versioned based on the training data and hyperparameters, not on
* external factors such as seasonality or holidays. Moreover, the Evaluate tab of the Vertex AI UI only shows the performance metrics of a single model version, not across multiple versions.
* Option C is incorrect because Kubeflow is a different platform than Vertex AI, and it does not integrate well with Vertex AI Pipelines. Kubeflow experiments are used to group pipeline runs that share a common goal or objective, not to compare performance statistics across different seasons or years.
Kubeflow UI does not provide any tools to compare the results across the experiments, and it would require switching between different platforms to access the data.
* Option D is correct because Vertex ML Metadata is a service that allows storing and tracking metadata associated with machine learning workflows, such as models, datasets, metrics, and events. Events are user-defined labels that can be used to group or slice the metadata for analysis. By using seasons and years as events, you can easily store and compare the performance statistics of each version of your models across different time periods. Vertex ML Metadata also provides tools to visualize and analyze the metadata, such as the ML Metadata Explorer and the What-If Tool.

NEW QUESTION # 106
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