Topic 1 Question 260

Professional Machine Learning Engineer

Topic 1 Question 260
You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed. You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex AI Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?
- 1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata.
  2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_merrics function to log loss values.
- 1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata.
  2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values.
- 1. Create a Vertex AI TensorBoard instance and use the Vertex AI SDK to create an experiment and associate the TensorBoard instance.
  2. Use the assign_input_artifact method to track the preprocessed data and use the log_time_series_metrics function to log loss values.
- 1. Create a Vertex AI TensorBoard instance, and use the Vertex AI SDK to create an experiment and associate the TensorBoard instance.
  2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values.
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- 正解だと思う選択肢: C
  log_time_series_metrics requires setting Tensorboard: https://cloud.google.com/vertex-ai/docs/experiments/log-data
  
  assign_input_artifacts can be used to track input data: https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/get_started_with_vertex_experiments.ipynb
  
  👍 5
  gscharly2024/04/18
- 正解だと思う選択肢: B
  Vertex AI Experiment and ML Metadata: This is the foundation for tracking experiments and artifacts within Vertex AI.expand_more Creating an experiment allows you to group related runs and log data associated with those runs. ML Metadata helps manage the lineage of data and models used in your experiments.expand_more
  
  Logging Data:
  
  log_time_series_metrics: This function is specifically designed for tracking time-series data, making it suitable for logging the preprocessed multivariate time series data in your experiment. log_metrics: This function is appropriate for logging loss values during model training. It can handle numerical values like loss efficiently. By combining these techniques, you can effectively track both the preprocessed data (time series) and the training performance metrics (loss values) within your Vertex AI Experiment.
  
  👍 4
  fitri0012024/04/27
- 正解だと思う選択肢: B
  A and B are the same
  
  👍 3
  tungdeptraiqua2024/07/22
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