Google Professional Machine Learning Engineer
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As a consultant to the CIO of a large financial firm, you're tasked with advising on the optimization of machine learning (ML) projects on Google Cloud Platform (GCP) to achieve cost efficiency without compromising on performance. The firm is particularly concerned about unnecessary expenditures and seeks to implement Google's recommended best practices for ML projects. The CIO asks, 'Given our need to balance cost and performance in our ML initiatives, what strategies should we adopt to ensure we're following Google's best practices for cost optimization?' Please select the two most effective strategies from the options provided. (Choose two)
Explanation:
Options A through D are all valid strategies recommended by Google for optimizing costs in ML projects on GCP. Implementing automatic shutdown policies for idle instances (Option A) and using Preemptible VMs for interruptible workloads (Option B) are direct cost-saving measures. Monitoring GPU usage (Option C) and treating notebooks as temporary resources (Option D) also contribute to cost efficiency by ensuring resources are used judiciously. Since the question asks for the two most effective strategies, Options A and B are highlighted for their direct impact on reducing costs. However, Option E is the most comprehensive answer as it includes all the mentioned practices, making it the best choice when selecting a single option.