Google Cloud Professional Machine Learning Engineer — Question 11

You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, Scikit-learn, and custom libraries. What should you do?

Answer options

• A. Use the AI Platform custom containers feature to receive training jobs using any framework.
• B. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TF Job.
• C. Create a library of VM images on Compute Engine, and publish these images on a centralized repository.
• D. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

Correct answer: A

Explanation

The correct answer is A because the AI Platform custom containers feature allows for flexibility in using various frameworks, making it suitable for the diverse needs of the data scientists. Option B is less ideal as it focuses specifically on TensorFlow jobs, limiting framework compatibility. Option C, while providing VM images, does not offer the managed service aspect required for easier administration. Option D introduces another layer of complexity with Slurm, which may not be necessary given the managed services available.