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Your company operates a large retail website and has developed multiple ML models using PyTorch, TensorFlow, and BigQueryML. After migrating to Google Cloud, you're collaborating on an international project with partners from a different organization. They need access to your Vertex AI dataset stored in Cloud Storage for analysis. The dataset includes sensitive customer information, and the partners are located in a region with strict data compliance laws. You need to ensure the solution is cost-effective, scalable, and complies with data protection regulations. What is the best approach to facilitate this? (Choose two correct options if E is available)
A
Share your GCP account credentials with them to allow direct access to the Cloud Storage bucket
B
Transfer the data to a removable storage device and send it to them via secure courier
C
Export the dataset's metadata and annotations into a CSV file for them to use, excluding any sensitive data
D
Provide access to the Cloud Storage file via a service account with minimal necessary permissions or a time-limited signed URL
E
Export the dataset's metadata and annotations into a JSONL file, including Cloud Storage URIs, and grant access to the files via a service account with minimal necessary permissions