6 EXCLUSIVE Practice Exams | GCP Machine Learning Engineer

[UPDATED JULY 2024]

Disclaimer:

This course is designed to provide 300 questions across 6 practice exams, each simulating real certification tests. Currently, the course does not yet contain all the questions, but it will be continuously updated to reach this goal. As new questions and updates are added, they will be made available to all enrolled students at no additional cost.

Are you ready to pass your GCP Professional Machine Learning Engineer certification exam? This is THE practice exams course to give you the winning edge.

Our course includes 300 EXCLUSIVE practice questions, meticulously categorized by section and subsections from the certification exam guide. Each question is designed to mimic the tone and tenor of the real exam, providing you with a realistic testing experience. With detailed explanations for every answer, you’ll not only learn the correct response but also understand why it’s correct, solidifying your knowledge of Google Cloud Platform (GCP) fundamentals.

This course covers the most important areas of the GCP Professional Machine Learning Engineer certification, including:

• Designing and implementing machine learning models

• Building scalable and reliable machine learning systems

• Automating and orchestrating machine learning pipelines

• Monitoring, optimizing, and maintaining machine learning models in production

• Ensuring solution quality and compliance

Each practice exam is crafted to help you master these areas and gain the confidence you need to excel in the actual exam.

Course Features:

• 300 exclusive, high-quality practice questions.

• Detailed explanations for each answer.

• Up-to-date content based on the latest GCP syllabus.

• Realistic exam simulations to help you get familiar with the exam format.

• Comprehensive coverage of GCP fundamentals, services, and application.

We want you to think of this course as your final pit-stop before the exam, equipping you with the knowledge, skills, and confidence to cross the finish line and get GCP Professional Machine Learning Engineer certified!

Trust our process, and you are in good hands!With our course, you’ll be prepared to ace the exam and advance your career in cloud technology!

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Check out this SAMPLE QUESTIONand the detailed explanations for each answer:

You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

A. Configure AutoML Tables to perform the classification task.

This option isCORRECT because configuring AutoML Tables is the correct choice as it allows you to perform the classification task without writing any code. AutoML Tables automates the process of exploratory data analysis, feature selection, model building, training, hyperparameter tuning, and serving, making it a suitable option for repetitive classification tasks over structured datasets in BigQuery

B. Run a BigQuery ML task to perform logistic regression for the classification.

This option isincorrect because running a BigQuery ML task to perform logistic regression is not the most efficient choice for completing the steps without writing code. While BigQuery ML is a powerful tool for machine learning tasks within BigQuery, it may require manual coding and configuration for each step of the classification workflow, which goes against the requirement of avoiding code.

C. Use AI Platform Notebooks to run the classification model with pandas library.

This option isincorrect because using AI Platform Notebooks with the pandas library is not the optimal choice for completing the steps without writing code. While AI Platform Notebooks provide a collaborative environment for running code, using the pandas library would still require manual coding for exploratory data analysis, feature selection, model building, training, hyperparameter tuning, and serving, which contradicts the requirement of avoiding code.

D. Use AI Platform to run the classification model job configured for hyperparameter tuning.

This option isincorrect because using AI Platform to run the classification model job configured for hyperparameter tuning is not the best option for completing the steps without writing code. While AI Platform supports hyperparameter tuning and job configuration, it may still involve manual coding and configuration, which does not align with the goal of avoiding code for the classification workflow over structured datasets in BigQuery.

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Do you know why get Google Cloud certified?

• 87% of Google Cloud certified individuals are more confident in their cloud skills.

• Google Cloud certifications are among the highest paying IT certifications of 2023.

• More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work.

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