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Google Professional Machine Learning Engineer exam dumps: 521 free Google Professional Machine Learning Engineer practice questions

Free Google Professional Machine Learning Engineer practice questions for the Professional Machine Learning Engineer exam, with the correct answer and a full explanation for every option. Read the first 10 below, browse all 521 by number, or take a timed practice exam.

Question bank last updated December 2024

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Free Google Professional Machine Learning Engineer practice questions

Questions 1 to 10 of 521

Pick an answer before you open the explanation. Each question also has its own page with a permalink.

Google Professional Machine Learning Engineer Question 1

Single answerGoogle Cloud Platform

You are a machine learning engineer tasked with creating a predictive model for customer churn using BigQuery ML. The dataset is stored in BigQuery, and you need to include categorical features like 'subscription type' and 'customer region' in your model training. What should you do to ensure these features are properly handled by BigQuery ML?

A
Manually encode the categorical features into numeric values before training the model.
B
Use BigQuery ML's automatic feature preprocessing to handle categorical features.
C
Exclude categorical features from the training dataset as BigQuery ML does not support them.
D
Train the model first and then preprocess categorical features at the prediction stage.

Show answer and explanation

Correct answer: B

Explanation

BigQuery ML is designed to handle categorical features automatically during model training, eliminating the need for manual encoding or exclusion of such features. By enabling automatic feature preprocessing, BigQuery ML simplifies the workflow for machine learning engineers, ensuring that categorical features are processed appropriately without additional effort.

A. Incorrect.
This is incorrect because BigQuery ML automatically handles categorical features through feature preprocessing, so manual encoding is unnecessary.
B. Correct.
This is correct because BigQuery ML provides automatic feature preprocessing, including handling categorical features through techniques like one-hot encoding or embeddings.
C. Incorrect.
This is incorrect because BigQuery ML supports categorical features and can process them automatically during training.
D. Incorrect.
This is incorrect because preprocessing categorical features after training would result in inconsistent inputs for the model, leading to incorrect predictions.

Google Professional Machine Learning Engineer Question 2

Select 3Google Cloud Platform

You are a machine learning engineer at a retail company. Your team wants to predict product sales using historical sales data stored in BigQuery. You decide to use BigQuery ML to build a regression model. Which of the following are important considerations when developing the model in BigQuery ML?

A
Ensure the dataset is split into training and evaluation sets using a SQL query.
B
Select 'KMeans' as the model type for regression tasks.
C
Include feature engineering steps such as creating derived features using SQL.
D
Use the 'ML.EVALUATE' function to assess model performance.
E
Manually implement gradient descent logic in the SQL query.

Show answer and explanation

Correct answers: A, C, D

Explanation

When developing ML models with BigQuery ML, it is important to properly split the dataset for training and evaluation, perform feature engineering to improve model performance, and use built-in functions like 'ML.EVALUATE' for model assessment. Selecting the correct model type and leveraging BigQuery ML's built-in capabilities are crucial for the success of the project.

A. Correct.
Correct: Splitting the dataset into training and evaluation sets is a critical step to ensure model performance is evaluated properly. This can be done using SQL queries in BigQuery.
B. Incorrect.
Incorrect: 'KMeans' is a clustering algorithm and is not suitable for regression tasks. For regression, you should use models like 'LINEAR_REG' or 'AUTO_ML'.
C. Correct.
Correct: Feature engineering, such as creating derived features, is an important part of improving model performance, and SQL provides a powerful way to perform this in BigQuery ML.
D. Correct.
Correct: The 'ML.EVALUATE' function is specifically designed to assess the performance of models built using BigQuery ML, providing metrics like RMSE for regression tasks.
E. Incorrect.
Incorrect: Implementing gradient descent manually is unnecessary in BigQuery ML as it abstracts the training process, including optimization algorithms, from the user.

Google Professional Machine Learning Engineer Question 3

Single answerGoogle Cloud Platform

You are tasked with building a machine learning model for predicting customer churn using BigQuery ML. The dataset is stored in BigQuery and contains customer behavior metrics such as 'last_purchase_date', 'average_monthly_spending', and 'total_logins'. You need a model that provides interpretable predictions and supports SQL-based feature engineering directly within BigQuery. Which model type should you choose for this task?

A
Logistic Regression
B
Deep Neural Network
C
k-means Clustering
D
AutoML Tables

Show answer and explanation

Correct answer: A

Explanation

BigQuery ML supports building interpretable models like Logistic Regression directly within BigQuery using SQL. For a binary classification task such as churn prediction, Logistic Regression is a natural fit. It allows you to leverage SQL for feature engineering and ensures the model is interpretable, which is critical for understanding the reasons behind customer churn.

A. Correct.
Logistic Regression is suitable for binary classification tasks like churn prediction. It is interpretable and natively supported by BigQuery ML, making it a good choice for this scenario.
B. Incorrect.
Deep Neural Network is a powerful model for complex tasks, but it is less interpretable and not the best choice for a straightforward binary classification task with structured data.
C. Incorrect.
k-means Clustering is an unsupervised learning algorithm used for grouping data points into clusters. It is not suitable for a supervised task like churn prediction.
D. Incorrect.
AutoML Tables is a managed solution for building machine learning models with minimal effort, but it operates outside BigQuery ML and does not allow direct SQL-based feature engineering within BigQuery.

Google Professional Machine Learning Engineer Question 4

Select 3Google Cloud Platform

You are a machine learning engineer tasked with building a predictive model for customer churn using BigQuery ML. The dataset is already in BigQuery and contains millions of rows. Which of the following steps should you take to effectively develop the model using BigQuery ML?

A
Preprocess the data using SQL-based transformations in BigQuery to handle missing values and normalize features.
B
Export the dataset from BigQuery to a local environment and preprocess it using a Python library like pandas.
C
Use the CREATE MODEL statement in BigQuery ML to train the model directly on the dataset.
D
Manually split the dataset into training and testing sets before training the model.
E
Enable feature engineering by defining feature transformations directly within the SQL query used for training.

Show answer and explanation

Correct answers: A, C, E

Explanation

BigQuery ML is designed to simplify the machine learning workflow by allowing you to preprocess data, engineer features, and train models directly within the BigQuery environment using SQL. There is no need to export data or manually split it for training. This approach is well-suited for handling large datasets efficiently.

A. Correct.
Correct: BigQuery ML supports SQL-based preprocessing. Handling missing values and normalizing features directly in BigQuery is efficient for large datasets.
B. Incorrect.
Incorrect: Exporting the dataset to a local environment is unnecessary, as BigQuery ML is designed to train models directly on data stored in BigQuery.
C. Correct.
Correct: The CREATE MODEL statement is the standard way to train models in BigQuery ML, leveraging its built-in machine learning capabilities.
D. Incorrect.
Incorrect: BigQuery ML automatically splits the dataset into training, evaluation, and testing sets, so manual splitting is not required.
E. Correct.
Correct: BigQuery ML allows feature transformations to be applied via SQL queries, making it easier to engineer features during the training process.

Google Professional Machine Learning Engineer Question 5

Select 4Google Cloud Platform

You are a data scientist at a retail company and want to build a machine learning model to predict customer churn using BigQuery ML. You have a dataset stored in BigQuery with millions of rows and several features. Which steps should you perform to ensure the model is developed effectively while leveraging BigQuery ML's capabilities?

A
Use the CREATE MODEL statement in BigQuery ML to define the model type and target column.
B
Preprocess the dataset by writing SQL queries to handle missing values and normalize features within BigQuery.
C
Export the dataset from BigQuery to a third-party tool for feature engineering and model training.
D
Leverage built-in feature importance tools in BigQuery ML to evaluate which features contribute the most to the model.
E
Use a separate validation dataset within BigQuery to evaluate model performance after training.

Show answer and explanation

Correct answers: A, B, D, E

Explanation

BigQuery ML enables end-to-end machine learning workflows within BigQuery, making it unnecessary to export data for external preprocessing or training. You can preprocess the data using SQL, train models using the CREATE MODEL statement, evaluate feature importance, and validate performance, all within the BigQuery environment.

A. Correct.
Correct: The CREATE MODEL statement is the core feature of BigQuery ML, allowing you to specify model type (e.g., logistic regression) and the target column for prediction.
B. Correct.
Correct: BigQuery ML supports SQL-based preprocessing, enabling you to handle missing values, normalize data, or create new features directly in BigQuery.
C. Incorrect.
Incorrect: Exporting the dataset to a third-party tool contradicts the goal of leveraging BigQuery ML's native capabilities for model development and training.
D. Correct.
Correct: BigQuery ML provides built-in feature importance tools for specific model types, helping you understand which features are most relevant to the model.
E. Correct.
Correct: Using a separate validation dataset within BigQuery ensures proper model evaluation and avoids overfitting to the training data.

Google Professional Machine Learning Engineer Question 6

Single answerGoogle Cloud Platform

You are working with a marketing dataset in BigQuery that contains customer details and their responses to a promotional email. The dataset includes columns such as 'age', 'gender', 'region', 'income', and 'responded_to_email' (1 for yes, 0 for no). Your task is to predict whether a customer will respond to a promotional email based on these features. Which BigQuery ML model is most appropriate for this use case?

A
Linear Regression
B
Binary Logistic Regression
C
K-means Clustering
D
Matrix Factorization

Show answer and explanation

Correct answer: B

Explanation

The goal in this scenario is to predict a binary outcome (whether a customer will respond to a promotional email or not) based on various features. Binary Logistic Regression is the most suitable model in BigQuery ML for solving this type of binary classification problem. Other options like Linear Regression, K-means Clustering, and Matrix Factorization are designed for different use cases and are not appropriate here.

A. Incorrect.
Linear Regression is used for predicting continuous numerical values, such as revenue or temperature. It is not suitable for binary classification tasks like predicting a yes/no response.
B. Correct.
Binary Logistic Regression is designed for binary classification problems, where the target variable has two possible outcomes (e.g., 1 for yes and 0 for no). This makes it the most appropriate choice for this scenario.
C. Incorrect.
K-means Clustering is an unsupervised learning model used for grouping data points into clusters. It does not perform predictive tasks like classification.
D. Incorrect.
Matrix Factorization is typically used for recommendation systems, where the goal is to predict missing values in a user-item interaction matrix. It is not suitable for binary classification tasks.

Google Professional Machine Learning Engineer Question 7

Single answerGoogle Cloud Platform

Your company operates an e-commerce platform, and you want to predict whether a user will complete a purchase based on features such as session duration, pages visited, and previous purchase history. Which BigQuery ML model type is the most appropriate for solving this problem?

A
Linear regression
B
Binary logistic regression
C
K-means clustering
D
Time series model

Show answer and explanation

Correct answer: B

Explanation

Binary logistic regression is the most appropriate BigQuery ML model for this scenario because the goal is to predict a binary outcome: whether or not a user will complete a purchase. This type of problem involves classifying a data point into one of two categories, which is the exact purpose of binary logistic regression. Other model types like linear regression, k-means clustering, and time series models are designed for different kinds of problems and are not suitable for this task.

A. Incorrect.
Linear regression is used for predicting continuous numerical values, such as sales revenue or temperature. It is not appropriate for binary classification tasks like predicting whether a user will complete a purchase.
B. Correct.
Binary logistic regression is used for binary classification tasks, where the outcome is one of two possible categories (e.g., purchase or no purchase). This makes it the most appropriate choice for the given scenario.
C. Incorrect.
K-means clustering is an unsupervised learning algorithm used to group data into clusters. It is not suitable for supervised classification tasks like predicting a binary outcome.
D. Incorrect.
Time series models are used for forecasting trends over time, such as predicting future stock prices or sales. This does not apply to the binary classification task in the scenario.

Google Professional Machine Learning Engineer Question 8

Single answerGoogle Cloud Platform

Your company wants to build a machine learning model using BigQuery ML to predict whether a customer will purchase a product (yes or no) based on their past behavior and demographic data. After analyzing the dataset, you find that the target variable ‘purchase’ is binary (0 or 1). Which BigQuery ML model type should you use to achieve this?

A
LINEAR_REG
B
LOGISTIC_REG
C
KMEANS
D
MATRIX_FACTORIZATION

Show answer and explanation

Correct answer: B

Explanation

The LOGISTIC_REG model in BigQuery ML is specifically designed for binary classification tasks where the target variable has two possible values (e.g., 0 or 1). Since the task described involves predicting a binary outcome ('yes' or 'no'), LOGISTIC_REG is the appropriate model type. Other options, such as LINEAR_REG, KMEANS, and MATRIX_FACTORIZATION, are designed for different use cases and would not be suitable for this scenario.

A. Incorrect.
LINEAR_REG is used for regression problems where the target variable is continuous, not binary or categorical, so it is not suitable for this use case.
B. Correct.
LOGISTIC_REG is the correct choice for binary classification problems like this one, where the target variable is binary (0 or 1).
C. Incorrect.
KMEANS is used for clustering tasks, where the goal is to group similar data points together, not for classification tasks like predicting a binary outcome.
D. Incorrect.
MATRIX_FACTORIZATION is typically used for recommendation systems, such as predicting user preferences, and is not suitable for binary classification.

Google Professional Machine Learning Engineer Question 9

Single answerGoogle Cloud Platform

You are a machine learning engineer tasked with building a model in BigQuery ML to predict whether a customer will purchase a product (yes or no) based on features such as age, income, and browsing history. Which type of BigQuery ML model should you create to solve this problem?

A
LINEAR_REG
B
LOGISTIC_REG
C
KMEANS
D
MATRIX_FACTORIZATION

Show answer and explanation

Correct answer: B

Explanation

LOGISTIC_REG is the appropriate BigQuery ML model for this task because it is specifically designed for binary classification problems, where the target variable has two possible outcomes. In this scenario, the goal is to predict whether a customer will purchase a product ('yes' or 'no'), which fits the binary classification framework. Other options like LINEAR_REG, KMEANS, and MATRIX_FACTORIZATION are designed for different types of machine learning tasks and are not suitable for this problem.

A. Incorrect.
LINEAR_REG is used for regression tasks, which predict continuous numerical values, not binary outcomes like 'yes' or 'no'.
B. Correct.
LOGISTIC_REG is the correct choice because it is designed for binary classification tasks, such as predicting two possible outcomes (e.g., yes/no or true/false).
C. Incorrect.
KMEANS is used for clustering tasks, which group data into clusters based on similarities, but it is not suitable for supervised binary classification.
D. Incorrect.
MATRIX_FACTORIZATION is used for recommendation systems and is not applicable for binary classification tasks.

Google Professional Machine Learning Engineer Question 10

Single answerGoogle Cloud Platform

You are working for a retail company that wants to predict whether a customer will make a purchase based on features such as age, income, browsing history, and past purchase behavior. You have decided to use BigQuery ML for this task. Which type of model should you build in BigQuery ML to address this problem?

A
Linear regression
B
Binary logistic regression
C
K-means clustering
D
Time-series forecasting

Show answer and explanation

Correct answer: B

Explanation

Since the task is to predict whether a customer will make a purchase (a binary outcome: yes or no), the most suitable model in BigQuery ML is binary logistic regression. This model is specifically designed for binary classification problems where the target variable has only two possible outcomes.

A. Incorrect.
Linear regression is used for predicting continuous numeric values, such as sales revenue or temperature, and is not appropriate for a binary classification problem.
B. Correct.
Binary logistic regression is the appropriate choice for a binary classification problem, where the goal is to predict one of two possible outcomes (e.g., purchase vs. no purchase).
C. Incorrect.
K-means clustering is an unsupervised learning technique used for grouping data points into clusters, not for predicting binary outcomes.
D. Incorrect.
Time-series forecasting is used for predicting future values based on historical time-based data, and it is not suitable for a binary classification problem.

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60 questions in 120 minutes, drawn from this bank, with a score report and a per-question review when you finish.

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What the Google Professional Machine Learning Engineer exam covers

Official Professional Machine Learning Engineer exam domains and weightings.

Framing ML Problems
15% of exam
Architecting ML Solutions
20% of exam
Designing Data Preparation and Processing Systems
20% of exam
Developing ML Models
25% of exam
Automating and Orchestrating ML Pipelines
10% of exam
Monitoring, Optimizing, and Maintaining ML Solutions
10% of exam

Google Professional Machine Learning Engineer practice questions 1 to 100 of 521

Every question has a page with the answer and explanation. Numbers are stable, so you can bookmark or share them. The bank is split into 6 pages of up to 100 questions.

Google Professional Machine Learning Engineer exam dumps FAQ

Are these Google Professional Machine Learning Engineer dumps real exam questions?

No. These are original practice questions written to the Professional Machine Learning Engineer exam objectives, not questions copied from a live exam. Memorising leaked questions violates Google Cloud's candidate agreement and stops working the moment the question pool rotates. Use this bank to check your understanding of each domain and to find the topics you still need to study.

How many Google Professional Machine Learning Engineer practice questions are there?

521 questions, each with the correct answer, an explanation of the answer, and a note on why every other option is wrong. The first 10 are on this page and every question has its own page linked below.

Are the Google Professional Machine Learning Engineer exam dumps free?

Yes. Every question, answer and explanation on this page and the linked question pages is free to read without an account. A free HydraNode account adds timed practice exams, scoring and progress tracking across attempts.

How do I take a timed Google Professional Machine Learning Engineer practice test?

Sign in and start the Professional Machine Learning Engineer exam on HydraNode. A session gives you 60 questions drawn from this bank in 120 minutes, then a score report with a per-question review.

What topics does the Google Professional Machine Learning Engineer exam cover?

The official exam domains are: Framing ML Problems; Architecting ML Solutions; Designing Data Preparation and Processing Systems; Developing ML Models; Automating and Orchestrating ML Pipelines; Monitoring, Optimizing, and Maintaining ML Solutions.

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