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Question 139/139
One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pipeline more robust to issues like this. What should you do?
Correct Answer: A
TensorFlow Data Validation (TFDV) is a library that helps you understand, validate, and monitor your data for machine learning. It can automatically detect and report schema anomalies, such as missing features, new features, or different data types, in your data. It can also generate descriptive statistics and data visualizations to help you explore and debug your data. TFDV can be integrated with your model training pipeline to ensure data quality and consistency throughout the machine learning lifecycle. References:
* TensorFlow Data Validation
* Data Validation | TensorFlow
* Data Validation | Machine Learning Crash Course | Google Developers
* TensorFlow Data Validation
* Data Validation | TensorFlow
* Data Validation | Machine Learning Crash Course | Google Developers
- Question List (139q)
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- Question 35: You have deployed a model on Vertex AI for real-time inferen...
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- Question 39: You developed a Vertex Al ML pipeline that consists of prepr...
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- Question 44: During batch training of a neural network, you notice that t...
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- Question 48: You are using Kubeflow Pipelines to develop an end-to-end Py...
- Question 49: You work at an ecommerce startup. You need to create a custo...
- Question 50: You work for a bank You have been asked to develop an ML mod...
- Question 51: You have developed an AutoML tabular classification model th...
- Question 52: You recently created a new Google Cloud Project After testin...
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- Question 65: You recently trained a XGBoost model that you plan to deploy...
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- Question 67: You work for a large technology company that wants to modern...
- Question 68: You have written unit tests for a Kubeflow Pipeline that req...
- Question 69: You recently joined a machine learning team that will soon r...
- Question 70: You recently trained an XGBoost model on tabular data You pl...
- Question 71: You have been asked to build a model using a dataset that is...
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- Question 78: You lead a data science team at a large international corpor...
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- Question 108: You work with a team of researchers to develop state-of-the-...
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- Question 110: You work on a growing team of more than 50 data scientists w...
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- Question 112: You work for a semiconductor manufacturing company. You need...
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- Question 128: You recently deployed a pipeline in Vertex Al Pipelines that...
- Question 129: You work at a leading healthcare firm developing state-of-th...
- Question 130: You developed a Python module by using Keras to train a regr...
- Question 131: You have developed a fraud detection model for a large finan...
- Question 132: You are developing a training pipeline for a new XGBoost cla...
- Question 133: As the lead ML Engineer for your company, you are responsibl...
- Question 134: You are going to train a DNN regression model with Keras API...
- Question 135: You are an ML engineer at an ecommerce company and have been...
- Question 136: You are responsible for building a unified analytics environ...
- Question 137: You deployed an ML model into production a year ago. Every m...
- Question 138: Your company stores a large number of audio files of phone c...
- Question 139: One of your models is trained using data provided by a third...
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