11: Measuring, Monitoring, and Troubleshooting Machine Learning Models This chapter focuses on key concepts in machine learning, including machine learning terminology and core concepts and common sources of error in machine learning. Machine learning is a broad discipline with many areas of specialization. This chapter provides you with a high-level overview to help you pass the Professional Data Engineer exam, but it is not a substitute for learning machine learning from resources designed for that purpose.
Chapter 12: Leveraging Prebuilt ML Models as a Service This chapter describes Google Cloud Platform options for using pretrained machine learning models to help developers build and deploy intelligent services quickly. The services are broadly grouped into sight, conversation, language, and structured data. These services are available through APIs or through Cloud AutoML services.
Interactive Online Learning Environment and TestBank
Learning the material in the Official Google Cloud Certified Professional Engineer Study Guide is an important part of preparing for the Professional Data Engineer certification exam, but we also provide additional tools to help you prepare. The online TestBank will help you understand the types of questions that will appear on the certification exam.
The sample tests in the TestBank include all the questions in each chapter as well as the questions from the assessment test. In addition, there are two practice exams with 50 questions each. You can use these tests to evaluate your understanding and identify areas that may require additional study.
The flashcards in the TestBank will push the limits of what you should know for the certification exam. Over 100 questions are provided in digital format. Each flashcard has one question and one correct answer.
The online glossary is a searchable list of key terms introduced in this Study Guide that you should know for the Professional Data Engineer certification exam.
To start using these to study for the Google Cloud Certified Professional Data Engineer exam, go to www.wiley.com/go/sybextestprep and register your book to receive your unique PIN. Once you have the PIN, return to www.wiley.com/go/sybextestprep, find your book, and click Register, or log in and follow the link to register a new account or add this book to an existing account.
Additional Resources
People learn in different ways. For some, a book is an ideal way to study, whereas other learners may find video and audio resources a more efficient way to study. A combination of resources may be the best option for many of us. In addition to this Study Guide, here are some other resources that can help you prepare for the Google Cloud Professional Data Engineer exam:
The Professional Data Engineer Certification Exam Guide: https://cloud.google.com/certification/guides/data-engineer/
Exam FAQs: https://cloud.google.com/certification/faqs/
Google’s Assessment Exam: https://cloud.google.com/certification/practice-exam/data-engineer
Google Cloud Platform documentation: https://cloud.google.com/docs/
Cousera’s on-demand courses in “Architecting with Google Cloud Platform Specialization” and “Data Engineering with Google Cloud” are both relevant to data engineering: www.coursera.org/specializations/gcp-architecture https://www.coursera.org/professional-certificates/gcp-data-engineering
QwikLabs Hands-on Labs: https://google.qwiklabs.com/quests/25
Linux Academy Google Cloud Certified Professional Data Engineer video course: https://linuxacademy.com/course/google-cloud-data-engineer/
The best way to prepare for the exam is to perform the tasks of a data engineer and work with the Google Cloud Platform.
Objective Map
| Objective | Chapter |
| Section 1: Designing data processing system | |
| 1.1 Selecting the appropriate storage technologies | 1 |
| 1.2 Designing data pipelines | 2, 3 |
| 1.3 Designing a data processing solution | 4 |
| 1.4 Migrating data warehousing and data processing | 4 |
| Section 2: Building and operationalizing data processing systems | |
| 2.1 Building and operationalizing storage systems | 2 |
| 2.2 Building and operationalizing pipelines | 3 |
| 2.3 Building and operationalizing infrastructure | 5 |
| Section 3: Operationalizing machine learning models | |
| 3.1 Leveraging prebuilt ML models as a service | 12 |
| 3.2 Deploying an ML pipeline | 9 |
| 3.3 Choosing the appropriate training and serving infrastructure | 10 |
| 3.4 Measuring, monitoring, and troubleshooting machine learning models | 11 |
| Section 4: Ensuring solution quality | |
| 4.1 Designing for security and compliance | 6 |
| 4.2 Ensuring scalability and efficiency | 7 |
|
4.3 Ensuring
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