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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 232 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 232
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images. Before training the model, you need to prepare the data. You want the data preprocessing and model training workflow to be as efficient, scalable, and low maintenance as possible. What should you do?

  • A. 1. Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory.
    2. Reference tf.data.TFRecordDataset in the training script.
    3. Train the model by using Vertex AI Training with a V100 GPU.
  • B. 1. Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label
    2. Reference tfds.folder_dataset:ImageFolder in the training script.
    3. Train the model by using Vertex AI Training with a V100 GPU.
  • C. 1. Create a Jupyter notebook that uses an nt-standard-64 V100 GPU Vertex AI Workbench instance.
    2. Write a Python script that creates sharded TFRecord files in a directory inside the instance.
    3. Reference tf.data.TFRecordDataset in the training script.
    4. Train the model by using the Workbench instance.
  • D. 1. Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex AI Workbench instance.
    2. Write a Python script that copies the images into multiple Cloud Storage directories, where each. directory is named according to the corresponding label.
    3. Reference tfds.foladr_dataset.ImageFolder in the training script.
    4. Train the model by using the Workbench instance.
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Suggested Answer: A 🗳️
by pikachu007 at Jan. 13, 2024, 8:01 a.m.

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pinimichele01
Highly Voted 7 months, 3 weeks ago
Selected Answer: A
millions of labeled images -> dataflow tfrecord faster than folder-based
upvoted 5 times
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AzureDP900
Most Recent 4 months, 3 weeks ago
A is correct Here's why You need to prepare the data before training an image classification model. Using TFRecord files allows you to store your data in a format that can be efficiently read and processed by TensorFlow. Sharding the data into multiple files allows for parallel processing and scalability. Dataflow is a Google Cloud service that provides a scalable and reliable way to process large datasets. By using Vertex AI Training with a V100 GPU, you can train your model in an efficient and cost-effective manner.
upvoted 1 times
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b1a8fae
10 months, 2 weeks ago
Selected Answer: A
Ideally you want to export your data in TFRecords (most efficient image format) in Cloud Storage, and not in the instance (to improve scalability)
upvoted 3 times
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pikachu007
10 months, 2 weeks ago
Selected Answer: A
B. Folder-Based Structure: While viable, it's less efficient for large datasets compared to TFRecord files, potentially leading to slower I/O during training. C. Workbench Processing: Local preprocessing on a single instance can be less scalable and efficient for millions of images, potentially introducing bottlenecks. D. Workbench Training: While Workbench offers a Jupyter environment, Vertex AI Training is specifically designed for scalable model training, providing optimized hardware and infrastructure.
upvoted 2 times
...

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