You work as a machine learning specialist for a start-up software company that builds a mobile app that subscribers can use to identify various types of birds from pictures they take with their phone camera. You have a large set of unlabeled images of birds that you want to use as your training data for your image recognition application. Which option is the most efficient approach to creating a labeling job to build the training dataset for your mobile app?

Answer options:

A.Use the SageMaker k-means built-in algorithm to label your unlabeled images. Leverage a SageMaker Semantic Segmentation algorithm-based model to perform auto-annotation of your images.
B.Use SageMaker Data Wrangler to label your unlabeled images. Leverage a SageMaker Image Classification algorithm-based model to perform auto-annotation of your images.
C.Use SageMaker Ground Truth to label your unlabeled images, leveraging lambda functions to perform annotation consolidation and pre-labeling. Leverage a SageMaker Image Classification algorithm-based model to perform auto-annotation of your images.
D.Use SageMaker Ground Truth to label your unlabeled images, leveraging Glue ETL jobs to perform annotation consolidation and pre-labeling. Leverage a SageMaker Object Detection algorithm-based model to perform auto-annotation of your images.

Correct Answer: C
Option A is incorrect. Using the k-means algorithm to label your unlabeled images would be far less efficient than using SageMaker Ground Truth. Also, the SageMaker Semantic Segmentation algorithm is not an efficient algorithm to use to auto-annotate your images.
Option B is incorrect. Using the Data Wrangler to label your unlabeled images would be far less efficient than using SageMaker Ground Truth.
Option C is correct. SageMaker Ground Truth is the preferred method of labeling unlabeled image data. Also, using lambda functions in your labeling job allows you to automate the annotation consolidation and pre-labeling tasks. Finally, the SageMaker Image Classification built-in algorithm is the best choice for the auto-annotation task.
Option D is incorrect. Glue ETL jobs cannot perform your annotation consolidation and pre-labeling tasks as efficiently as using lambda functions for these tasks in your labeling job. 
References:
Please see the AWS SageMaker developer guide titled Image Classification Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html),
The AWS Examples GitHub repository titled From Unlabeled Data to a Deployed Machine Learning Model: A SageMaker Ground Truth Demonstration for Image Classification (https://github.com/aws/amazon-sagemaker-examples/blob/master/ground_truth_labeling_jobs/from_unlabeled_data_to_deployed_machine_learning_model_ground_truth_demo_image_classification/from_unlabeled_data_to_deployed_machine_learning_model_ground_truth_demo_image_classification.ipynb),
The AWS SageMaker developer guide titled Prepare ML Data with Amazon SageMaker Data Wrangler (https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler.html),
The AWS SageMaker developer guide titled Object Detection Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/object-detection.html)