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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Data Engineer Exam |
| Exam Number: | Professional-Data-Engineer |
| Passing Score: | 700 / 1000 |
| Exam Format: | Multiple select, Multiple choice |
| Real Exam Qty: | 40 - 50 |
| Available Languages: | Japanese, English |
| Exam Price: | USD 200 (plus tax where applicable) |
| Related Certifications: | Google Cloud Professional Data Analyst Google Cloud Professional Cloud Architect Google Cloud Associate Cloud Engineer |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Recommended Training: | Official Exam Guide Google Cloud Professional Data Engineer Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer Sample Questions |
| Exam Way: | Online-proctored or onsite-proctored |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
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The following will be discussed here:
Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.
NEW QUESTION # 234
You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm. To do this you need to add a synthetic feature. What should the value of that feature be?
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