Free Google Professional-Machine-Learning-Engineer Exam Questions
Absolute Free Professional-Machine-Learning-Engineer Exam Practice for Comprehensive Preparation
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Google Professional-Machine-Learning-Engineer Exam Questions
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Provided By: Google
- Exam: Professional Machine Learning Engineer
- Certification: Google Cloud Certified
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Total Questions: 289
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Updated On: Mar 03, 2025
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Rated: 4.9 |
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Online Users: 578
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Question 1
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You work for a hotel and have a dataset that contains customers' written comments scanned from paper-based customer feedback forms which are stored as PDF files Every form has the same layout. You need to quickly predict an overall satisfaction score from the customer comments on each form. How should you accomplish this task'?
Answer: D
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Question 2
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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?
Answer: D
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Question 3
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You have deployed multiple versions of an image classification model on AI Platform. You want to monitor the performance of the model versions over time. How should you perform this comparison?
Answer: B
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Question 4
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You recently deployed an ML model. Three months after deployment, you notice that your model is underperforming on certain subgroups, thus potentially leading to biased results. You suspect that the inequitable performance is due to class imbalances in the training data, but you cannot collect more data. What should you do? (Choose two.)
Answer: B,D
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Question 5
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While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?
Answer: C
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