Professional-Machine-Learning-Engineer Question Includes: Single Choice Questions: 281, Multiple Choice Questions: 4,
You trained a model, packaged it with a custom Docker container for serving, and deployed it to Vertex Al Model Registry. When you submit a batch prediction job, it fails with this error "Error model server never became ready Please validate that your model file or container configuration are valid. There are no additional errors in the logs What should you do?
You are working on a prototype of a text classification model in a managed Vertex AI Workbench notebook. You want to quickly experiment with tokenizing text by using a Natural Language Toolkit (NLTK) library. How should you add the library to your Jupyter kernel?
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?
Your organization’s marketing team is building a customer recommendation chatbot that uses a generative AI large language model (LLM) to provide personalized product suggestions in real time. The chatbot needs to access data from millions of customers, including purchase history, browsing behavior, and preferences. The data is stored in a Cloud SQL for PostgreSQL database. You need the chatbot response time to be less than 100ms. How should you design the system?
Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data. How should you address the input differences in production?
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The Google Professional-Machine-Learning-Engineer Exam is a certification designed for professionals who build, evaluate, and optimize machine learning models using Google Cloud technologies. It assesses expertise in ML model architecture, data pipeline creation, MLOps, and responsible AI practices.
This exam is ideal for data scientists, ML engineers, and AI professionals who work with Google Cloud to develop scalable machine learning solutions. Candidates should have experience in Python, Cloud SQL, and distributed data processing tools.
The Google Professional-Machine-Learning-Engineer exam covers:
The Google Professional-Machine-Learning-Engineer exam consists of multiple-choice and multiple-select questions. It does not directly assess coding skills, but candidates should be able to interpret Python and Cloud SQL code snippets.
Yes, the Google Professional-Machine-Learning-Engineer certification is highly valuable for professionals in machine learning and AI. It enhances credibility, improves job prospects, and ensures familiarity with industry best practices.
To become a Professional Machine Learning Engineer, follow these steps:
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The certification is valid for two years, after which candidates must recertify to maintain their credentials.
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