Google Professional-Machine-Learning-Engineer - PDF電子當

Professional-Machine-Learning-Engineer pdf
  • 考試編碼:Professional-Machine-Learning-Engineer
  • 考試名稱:Google Professional Machine Learning Engineer
  • 更新時間:2025-07-01
  • 問題數量:290 題
  • PDF價格: $59.98
  • 電子當(PDF)試用

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Professional-Machine-Learning-Engineer Online Test Engine

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  • 考試編碼:Professional-Machine-Learning-Engineer
  • 考試名稱:Google Professional Machine Learning Engineer
  • 更新時間:2025-07-01
  • 問題數量:290 題
  • PDF電子當 + 軟件版 + 在線測試引擎(免費送)
  • 套餐價格: $119.96  $79.98
  • 節省 50%

Google Professional-Machine-Learning-Engineer - 軟件版

Professional-Machine-Learning-Engineer Testing Engine
  • 考試編碼:Professional-Machine-Learning-Engineer
  • 考試名稱:Google Professional Machine Learning Engineer
  • 更新時間:2025-07-01
  • 問題數量:290 題
  • 軟件版價格: $59.98
  • 軟件版

Google Professional Machine Learning Engineer : Professional-Machine-Learning-Engineer 考試題庫簡介

Google Professional-Machine-Learning-Engineer 考試大綱:

主題簡介
主題 1
  • Collaborating within and across teams to manage data and models: It explores and processes organization-wide data including Apache Spark, Cloud Storage, Apache Hadoop, Cloud SQL, and Cloud Spanner. The topic also discusses using Jupyter Notebooks to model prototypes. Lastly, it discusses tracking and running ML experiments.
主題 2
  • Architecting low-code AI solutions: This section of the exam measures the skills of Google Machine Learning Engineers and covers developing machine learning models using BigQuery ML. It includes selecting appropriate models for business problems, such as linear and binary classification, regression, time series, matrix factorization, boosted trees, and autoencoders. Additionally, it involves feature engineering or selection and generating predictions using BigQuery ML.
主題 3
  • Automating and orchestrating ML pipelines: This topic focuses on developing end-to-end ML pipelines, automation of model retraining, and lastly tracking and auditing metadata.
主題 4
  • Serving and scaling models: This section deals with Batch and online inference, using frameworks such as XGBoost, and managing features using VertexAI.

參考:https://cloud.google.com/certification/guides/machine-learning-engineer

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