You have a large collection of unlabeled images that only employees may access. To achieve the highest labeling accuracy, which combination of steps should you take? (Choose two.)

To achieve the highest labeling accuracy for a private, unlabeled image dataset, the best approach is to leverage human annotation with controlled access. Amazon SageMaker Ground Truth is the service designed for creating high-quality training datasets. Creating an annotation job within Ground Truth allows you to define precise labeling instructions and requirements, which is crucial for accuracy. Since the images are sensitive and only accessible by employees, setting up private workforce teams is essential. This ensures that only authorized personnel can view and label the data, maintaining security and control while still benefiting from human intelligence for accurate labeling. Incorrect options: Amazon Rekognition is for pre-trained computer vision services, not for custom labeling of private, unlabeled datasets. Training a deep learning model directly on raw, unlabeled data for inference would result in unsupervised learning, which doesn't provide ground truth labels for supervised model training. Amazon Mechanical Turk is a public workforce and would expose the sensitive images to external, unauthorized individuals, violating the access restrictions.