Exam Details:
The Google Professional Machine Learning Engineer exam is a certification exam designed to test an individual's proficiency in machine learning concepts and skills related to designing, building, and deploying machine learning models on the Google Cloud Platform. The exam is available in English, Japanese, Spanish, Portuguese, French, German, and Italian.
The exam consists of multiple-choice and multiple-select questions and is delivered through an online proctoring system. The exam lasts for 2 hours, and candidates are required to achieve a passing score of 70% or higher to earn the certification. The cost of the exam is $200 USD.
Exam Objectives:
The exam is designed to test an individual's proficiency in the following areas:
- Designing and planning a machine learning solution architecture
- Building and training machine learning models
- Optimizing and deploying machine learning models
- Ensuring quality and reliability of machine learning models
- Managing machine learning projects and infrastructure
- Implementing responsible AI practices
Candidates are expected to demonstrate their understanding of these topics through a series of scenario-based questions that require them to analyze and solve real-world problems using machine learning concepts and techniques.
Related Books:
Here are some books that can help candidates prepare for the Google Professional Machine Learning Engineer exam:
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron
- Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-Learn, and TensorFlow by Sebastian Raschka and Vahid Mirjalili
- Machine Learning Yearning by Andrew Ng
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Building Machine Learning Powered Applications: Going from Idea to Product by Emmanuel Ameisen
These books cover various machine learning concepts and techniques, as well as practical applications of machine learning in real-world scenarios. They can help candidates gain a deeper understanding of the topics covered in the exam and prepare them for the types of questions they can expect to encounter.
In addition to studying these books, candidates should also take advantage of the official Google Cloud Platform documentation and training resources, as well as hands-on experience working with machine learning models on the platform.
By combining these resources with a solid understanding of the exam objectives and format, candidates can increase their chances of passing the Google Professional Machine Learning Engineer exam and earning their certification.