Google Professional Machine Learning Engineer exam dumps

Google Professional Machine Learning Engineer practice question 231 of 522

Professional Machine Learning Engineer. Professional level, Google Cloud. Free question with the correct answer and a full explanation.

Google Professional Machine Learning Engineer Question 231

Select 3Google Cloud Platform

You are working on a machine learning project to classify images of handwritten digits. The dataset is stored in a local directory on your workstation. You want to organize the data for training and validation on Google Cloud while ensuring scalability and efficient access for training your model using TensorFlow. What steps should you take to properly organize and store the data on Google Cloud?

  1. A

    Upload the dataset to a Cloud Storage bucket and structure it into separate directories for training, validation, and testing.

  2. B

    Store the dataset in a BigQuery table and use BigQuery ML to directly process the images.

  3. C

    Use the Google Cloud Storage URI to load the images into TensorFlow during training.

  4. D

    Convert the images into TFRecord format and store the TFRecords in a Cloud Storage bucket for efficient input pipeline processing.

  5. E

    Store the images in Firestore to allow for real-time data retrieval during training.

Show answer and explanation

Correct answers: A, C, D

Explanation

To properly organize and store training data on Google Cloud for an image classification task, you should upload the dataset to a Cloud Storage bucket, structure it into directories for training/validation/testing, and use efficient data formats like TFRecords. Cloud Storage URIs enable TensorFlow to access the data directly, ensuring scalability and efficiency. Firestore and BigQuery are not suitable for raw image data storage.

  • A. Correct.

    Uploading the dataset to a Cloud Storage bucket and organizing it into directories for training, validation, and testing is a best practice for managing datasets for machine learning projects on Google Cloud.

  • B. Incorrect.

    BigQuery is not optimized for storing image data. While BigQuery ML can be used for ML tasks, it is not suitable for directly processing raw image data.

  • C. Correct.

    Using the Google Cloud Storage URI to load images into TensorFlow ensures scalability and avoids the need to download the dataset locally during training.

  • D. Correct.

    Converting images into TFRecord format is a best practice for efficient data pipeline processing in TensorFlow, especially for large datasets.

  • E. Incorrect.

    Firestore is not designed for storing large binary files such as images. It is more suitable for structured, real-time data.

Timed practice exam

Take a Google Professional Machine Learning Engineer practice test under exam conditions

60 questions in 120 minutes, drawn from this bank, with a score report and a per-question review when you finish.

Start timed exam

More Google Professional Machine Learning Engineer practice questions

  1. 232.You are designing a machine learning pipeline to train a model on a large dataset of labeled images...
  2. 233.You are building a machine learning model that requires processing millions of images for training....
  3. 234.You are tasked with designing a real-time analytics pipeline for a machine learning model that...
  4. 235.Your team is building a machine learning pipeline to analyze customer transaction data stored in a...
  5. 236.You are building a machine learning pipeline to train and evaluate a model for predicting product...
Back to the Google Professional Machine Learning Engineer exam dumps