Google Professional Machine Learning Engineer Question 405
Select 3Google Cloud PlatformYou are tasked with designing a machine learning pipeline to process large-scale streaming data and train a model in a repeatable and automated manner. You decide to use TFX components and Dataflow for scalable data processing. Which of the following components should be included in your pipeline to ensure the data is validated and preprocessed appropriately before being used for training?
- A
ExampleGen
- B
StatisticsGen
- C
Transform
- D
Trainer
- E
ModelValidator
Show answer and explanation
Correct answers: A, B, C
Explanation
To ensure the data is validated and preprocessed appropriately in a TFX pipeline, you need components that handle data ingestion (ExampleGen), compute statistics to validate data quality (StatisticsGen), and preprocess the data (Transform). While Trainer and ModelValidator are vital components for other stages of the pipeline, they are not directly involved in the data validation and preprocessing steps.
- A. Correct.
ExampleGen is the entry point for TFX pipelines and is responsible for ingesting and splitting the data. It is a necessary component for processing the data in a TFX pipeline.
- B. Correct.
StatisticsGen computes statistics for the ingested data, which is essential for understanding the data and identifying potential anomalies. This is a key step in validating the data.
- C. Correct.
Transform applies feature engineering and preprocessing steps to the data so that it can be used for model training. This is critical for ensuring the data is in the correct format and scale for the model.
- D. Incorrect.
Trainer is responsible for training the machine learning model. While important, it is not directly involved in data validation or preprocessing.
- E. Incorrect.
ModelValidator is used to validate the trained model for deployment but does not contribute to data validation or preprocessing.