Azure Data Scientists Associate Practice Exam

When creating a batch endpoint for predicting new values, what output action should be used to collate results from multiple nodes?

1. summary_only

2. append_row

3. concurrency

4. merge_output

The correct answer is: append_row

Using the append_row output action is an effective choice for collating results from multiple nodes in a batch endpoint for predictions. This action allows the results of each prediction to be added as separate rows in a single output file. As each node processes its segment of data, it can append its results to the existing data structure, creating a consolidated view of all predictions in a straightforward manner. This approach is particularly useful in batch processing scenarios where distributed computing resources are utilized, as it enables the seamless integration of outputs generated concurrently across various nodes. By utilizing the append_row action, users can ensure that they maintain the order and integrity of the predictions, making further analysis on the complete dataset easier and more efficient. The other options may not provide the same clarity or efficiency in collating results. For example, summary_only would limit the output to a high-level overview rather than providing detailed results, while concurrency refers to the ability to manage simultaneous operations, not directly tied to result collation. Merge_output typically implies an operation that combines outputs from multiple sources but may not offer the clear structure that append_row provides in this scenario. Thus, append_row stands out as the most suitable option for effectively collecting results from predictions made across multiple processing nodes.